Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset (2024.lrec-main)
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Xinyu Ma, Xuebo Liu, Derek F. Wong, Jun Rao, Bei Li, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang
| Challenge: | Existing studies have shown that visual information in existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. |
| Approach: | They propose to use 3AM to create an ambiguity-aware multimodal machine translation dataset. |
| Outcome: | The proposed dataset includes more ambiguity and a greater variety of captions and images than other MMT datasets. |
A Benchmark Evaluation of Clinical Named Entity Recognition in French (2024.lrec-main)
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| Challenge: | Masked Language Models (MLMs) have shown strong performance on many NLP tasks. |
| Approach: | They evaluate masked language models for biomedical French on the task of clinical named entity recognition using gold-standard corpora. |
| Outcome: | The proposed model outperforms standard models on the task of clinical named entity recognition in biomedical French while remaining lighter than current models. |
A Benchmark for Recipe Understanding in Artificial Agents (2024.lrec-main)
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Jens Nevens, Robin de Haes, Rachel Ringe, Mihai Pomarlan, Robert Porzel, Katrien Beuls, Paul van Eecke
| Challenge: | a benchmark has been designed to evaluate whether artificial agents are able to understand how to perform everyday activities. |
| Approach: | They propose a benchmark task that maps a recipe to a set of cooking actions that are precise enough to be executed in the simulated kitchen. |
| Outcome: | The proposed benchmark consists of mapping a recipe to a set of cooking actions that is precise enough to be executed in the simulated kitchen and yields the desired dish. |
ABLE: Agency-BeLiefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness (2024.lrec-main)
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| Challenge: | Recent studies in Natural Language Processing (NLP) have unveiled a concerning issue: stereotypical biases associated with demographic groups are prevalent. |
| Approach: | They propose an approach that actively encodes stereotypical biases into the embedding space by integrating stereotypes into a model that acquires agency and belief scores rather than directly representing stereotypes. |
| Outcome: | The proposed model can learn agency and belief stereotypes while preserving the language model’s proficiency. |
Abstractive Multi-Video Captioning: Benchmark Dataset Construction and Extensive Evaluation (2024.lrec-main)
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| Challenge: | Abstractive multi-video captioning focuses on abstracting multiple videos with natural language. |
| Approach: | They propose a task that generates an abstract caption of shared video content . they propose end-to-end and cascade approaches to abstractive multi-video captioning . |
| Outcome: | The proposed task generates an abstract caption of shared content in a video group containing multiple videos. |
Abstract-level Deductive Reasoning for Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Existing methods fine-tune PLMs using the validity label and instance-level reasoning proofs as supervision signals. |
| Approach: | They propose to train PLMs to learn general reasoning patterns rather than instance-level knowledge by predicting the abstract reasoning proof of each sample. |
| Outcome: | The proposed model significantly reduces the impact of learning instance-level knowledge (over 70%) |
A Call for Clarity in Beam Search: How It Works and When It Stops (2024.lrec-main)
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| Challenge: | Empirical results show that a modified beam decoding implementation improves decoding performance of strong, neural language generation models. |
| Approach: | They propose a modification to a beam decoding implementation that generalizes the stopping criterion and provides flexibility to the depth of search. |
| Outcome: | The proposed method improves decoding performance of strong models on news text summarization and machine translation over diverse language pairs with negligible inference slowdown. |
A Canonical Form for Flexible Multiword Expressions (2024.lrec-main)
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| Challenge: | Until now, no well-defined canonical form exists for MWEs in Dutch . traditional dictionaries do not use a canonically form but an example to illustrate MWE . |
| Approach: | They propose a canonical form for Multiword Expressions in the Dutch language . it introduces a lexical resource with more than 11k Dutch multiword expressions in canonically form . |
| Outcome: | The proposed canonical form can be enriched with annotations to describe properties of the MWE and its components. |
A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations, but they are plagued by the Knowledge Hallucination problem. |
| Approach: | They propose a method that exploits the dialogue-knowledge interaction to reduce hallucination by using external knowledge resources to generate more informative responses. |
| Outcome: | The proposed method reduces hallucination without disrupting other dialogue performance while keeping adaptive to different generation models. |
Access Control Framework for Language Collections (2024.lrec-main)
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| Challenge: | Language Data Commons of Australia is developing a licence-based access control framework for language collections. |
| Approach: | This paper introduces the licence-based access control framework for language collections . the framework is based upon descriptions of access conditions in easy-to-read licences . it is implemented using identity-based authentication and authorisation systems where required . |
| Outcome: | The framework is developed by the Language Data Commons of Australia . it accommodates accessibility needs from unrestricted to extremely limited access . the framework is available in a range of languages including Indigenous languages and Australian English . |
A Challenge Dataset and Effective Models for Conversational Stance Detection (2024.lrec-main)
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| Challenge: | stance detection studies focus on evaluating stances within individual instances, hindering progress of conversational stance analysis. |
| Approach: | They propose a multi-turn conversation stance detection dataset that encompasses multiple targets for conversational stance detector. |
| Outcome: | The proposed dataset encompasses multiple targets for conversational stance detection. |
A Closer Look at Clustering Bilingual Comparable Corpora (2024.lrec-main)
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| Challenge: | Existing methods for clustering comparable corpora are not suitable for bilingual corpors. |
| Approach: | They propose new clustering models fully adapted to comparable corpora based on a deep variant of Kmeans . they illustrate their behavior on bilingual collections created from Wikipedia . |
| Outcome: | The proposed models show that they can cluster comparable corpora on bilingual collections . the proposed models are based on a state-of-the-art deep variant of Kmeans . |
AcnEmpathize: A Dataset for Understanding Empathy in Dermatology Conversations (2024.lrec-main)
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| Challenge: | Existing studies on empathy and mental health-related corpora focus on broader contexts and lack domain specificity. |
| Approach: | They propose a dataset that captures empathy expressed in acne-related discussions from forum posts focused on its emotional and psychological effects. |
| Outcome: | The AcnEmpathize dataset shows that it performs well at empathy classification. |
A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances (2024.lrec-main)
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| Challenge: | a new study uses pragmatic similarity measures to train speech synthesizers . the average inter-judge correlation between utterance pairs was 0.45. |
| Approach: | they use a re-enactment of a recorded dialog to create pragmatic similarity . they use 9 judges to listen to 220 utterance pairs and rate them on a continuous scale . |
| Outcome: | The results show that human judges listened to 220 utterance pairs and rated them on a continuous scale. |
A Community-Driven Data-to-Text Platform for Football Match Summaries (2024.lrec-main)
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| Challenge: | Prosebot is a community-driven data-to-text platform designed to generate textual summaries of football matches derived from match statistics. |
| Approach: | They describe the architecture and deployment of a community-driven data-to-text platform for generating textual summaries of football matches derived from match statistics. |
| Outcome: | The proposed system enhances visibility of lower-tier matches, traditionally accessible only through data tables. |
A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking the Privacy-Utility Trade-off (2024.lrec-main)
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| Challenge: | Differential Privacy (DP) has been used in NLP for years to address privacy concerns . privacy-enhancing technologies (PETs) are concrete technical solutions that can be incorporated into existing systems. |
| Approach: | They compare different Differential Privacy algorithms for word-level NLP tasks . they propose concrete steps forward to combat privacy risks in NLP settings . |
| Outcome: | The proposed methods perform better than the proposed methods on two NLP tasks. |
A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation (2024.lrec-main)
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| Challenge: | Existing studies on the explicit and implicit biases in large language models (LLMs) focus on either explicit or implicit bias. |
| Approach: | They propose a self-evaluation-based two-stage measurement of explicit and implicit biases within large language models grounded in social psychology. |
| Outcome: | The proposed model is based on two stages of self-evaluation on state-of-the-art LLMs to measure explicit bias toward social targets, where bias is less likely to be self-recognized by the LLM. |
A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media (2024.lrec-main)
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| Challenge: | Dehumanisation involves the perception and/or treatment of a social group’s members as less than human. |
| Approach: | They propose to use a new sentiment resource to make it easier to transfer to other languages and to evaluate and use . they then apply the method to study attitudes to migration expressed in Slovene newspapers, and examine how this discourse changed between the 2015-16 migration crisis and the 2022-23 period following the war in Ukraine. |
| Outcome: | The proposed method is easier to transfer to other languages and evaluates . it combines zero-shot cross-lingual valence and arousal detection with statistical significance testing to examine attitudes to migration expressed in Slovene newspapers . |
A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions (2024.lrec-main)
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| Challenge: | Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. |
| Approach: | They propose a computational approach to measure the degree of grammaticization of deverbal prepositions based on corpus data. |
| Outcome: | The proposed method correlates well with human judgements and supports previous findings in linguistics. |
A Computational Model of Latvian Morphology (2024.lrec-main)
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| Challenge: | a computational model of Latvian morphology provides a formal structure for Latvian word form inflection . the model explicitly enumerates and handles the many exceptions to the general Latvian inflation principles . |
| Approach: | They propose a computational model of Latvian morphology that provides a formal structure for Latvian word form inflection. |
| Outcome: | The proposed model provides a good coverage for modern Latvian literary language and potential to extend to Latgalian language. |
A Concept Based Approach for Translation of Medical Dialogues into Pictographs (2024.lrec-main)
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| Challenge: | Pictographs have been found to improve patient comprehension of medical information or instructions. |
| Approach: | They propose a system that automatically translates French speech into pictographs . the system is based on a semantic gloss that serves as pivot between spontaneous language and pictograms based in the ontology of the UMLS . |
| Outcome: | The proposed system achieves an F0.5 score on unseen data, with 71% of glosses transmitting intended meaning. |
A Construction Grammar Corpus of Varying Schematicity: A Dataset for the Evaluation of Abstractions in Language Models (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have been developed without a theoretical framework . evaluating and improving LLMs will benefit from theoretical frameworks that enable comparison of structures of human language and model of language built up by LLM. |
| Approach: | They propose to use a construction grammar schema corpus to compare human grammar to LLMs' model of language. |
| Outcome: | The proposed corpus shows that even the largest LLMs are limited to more substantive constructions and do not recognize similarity of purely schematic constructions. |
A Controlled Reevaluation of Coreference Resolution Models (2024.lrec-main)
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| Challenge: | a pretrained language model is used in state-of-the-art coreference resolution models. |
| Approach: | They evaluate five coreference resolution models and control for language model used . they find that encoder-based CR models outperform decoder--based models in accuracy . |
| Outcome: | The encoder-based model outperforms the decoder--based models in accuracy and speed . older model generalizes the best to out-of-domain textual genres . |
A Corpus and Method for Chinese Named Entity Recognition in Manufacturing (2024.lrec-main)
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| Challenge: | Existing resources and techniques for named entity recognition (NER) for manufacturing-specific named entities are limited. |
| Approach: | They propose a corpus of Chinese manufacturing specifications, named MS-NERC, with 4,424 sentences and 16,383 entities. |
| Outcome: | The proposed model outperforms neural methods in few-shot and rich-resource domains. |
A Corpus for Sentence-Level Subjectivity Detection on English News Articles (2024.lrec-main)
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Francesco Antici, Federico Ruggeri, Andrea Galassi, Katerina Korre, Arianna Muti, Alessandra Bardi, Alice Fedotova, Alberto Barrón-Cedeño
| Challenge: | Existing approaches to spotting subjectivity require language-specific tools. |
| Approach: | They develop annotation guidelines for sentence-level subjectivity detection that are not limited to language-specific cues. |
| Outcome: | The proposed framework enables subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. |
A Corpus of German Abstract Meaning Representation (DeAMR) (2024.lrec-main)
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| Challenge: | Abstract Meaning Representations (AMRs) are semantic graphs that abstract away from surface syntax and capture the meaning of who does what to whom in a sentence. |
| Approach: | They propose to use German Abstract Meaning Representation (Deutsche AMR) to represent the structure and semantics of German. |
| Outcome: | The proposed framework is based on an annotated corpus of 400 DeAMR in German and is validated through inter-annotator agreement. |
A Corpus of Spontaneous L2 English Speech for Real-situation Speaking Assessment (2024.lrec-main)
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| Challenge: | Existing automated scoring systems rely on highly controlled elicitation protocols, such as reading aloud isolated words or short sentences, limiting their ability to evaluate spontaneous speech. |
| Approach: | They propose to collect a corpus of spontaneous L2 English speech from university students as part of a French national certificate in English. |
| Outcome: | The results show that only 35.4% of the 6,350 targeted words had stress detected on the expected syllable, revealing a common stress shift to the final s. |
Action and Reaction Go Hand in Hand! a Multi-modal Dialogue Act Aided Sarcasm Identification (2024.lrec-main)
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| Challenge: | Existing studies have shown that sarcasm is reflected by the intended meaning of the speaker's utterance. |
| Approach: | They propose to extend the MUStARD dataset to enclose dialogue acts for each dialogue . they propose a dialogue act-aided multi-modal transformer network for sarcasm identification model . |
| Outcome: | The proposed model improves performance in dialogue act-aided sarcasm identification compared to sardasmatic identification alone. |
Action-Concentrated Embedding Framework: This Is Your Captain Sign-tokening (2024.lrec-main)
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| Challenge: | ACE is a new sign token embedding framework that tracks a signer’s actions based on human posture estimation and captures the token embeds using a short-time Fourier transform. |
| Approach: | They propose a novel sign token embedding framework that tracks a signer’s actions based on human posture estimation and a dedicated notation system tailored for sign language. |
| Outcome: | The proposed framework outperforms previous studies in translation performance against a disaster sign language dataset and improves by up to 5.79% for BLEU-4 and 5.46% for ROUGE-L metric. |
Active Learning Design Choices for NER with Transformers (2024.lrec-main)
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Robert Vacareanu, Enrique Noriega-Atala, Gus Hahn-Powell, Marco A. Valenzuela-Escarcega, Mihai Surdeanu
| Challenge: | In the field of natural language processing, active learning is a technique that is used to decide which examples are worth annotating . a number of studies have focused on sequence classification, text classification, question answering, and question answering. |
| Approach: | They propose two different approaches to deal with partially-annotated sentences . they propose an annotation scheme that can be used to train with tokens . |
| Outcome: | The proposed approaches achieve comparable or better performance than sentence-level annotations with a smaller number of annotated tokens. |
A CURATEd CATalog: Rethinking the Extraction of Pretraining Corpora for Mid-Resourced Languages (2024.lrec-main)
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Jorge Palomar-Giner, Jose Javier Saiz, Ferran Espuña, Mario Mina, Severino Da Dalt, Joan Llop, Malte Ostendorff, Pedro Ortiz Suarez, Georg Rehm, Aitor Gonzalez-Agirre, Marta Villegas
| Challenge: | CATalog 1.0 is the largest text corpus in Catalan to date . CURATE is a pipeline that can be parallelizable to run in high performance clusters . |
| Approach: | They propose a data pipeline that uses binary filters to filter documents based on text quality . they optimised the pipeline to run in high performance clusters . |
| Outcome: | The proposed pipeline is optimized for high performance cluster environments and runs in high performance. |
AdaKron: An Adapter-based Parameter Efficient Model Tuning with Kronecker Product (2024.lrec-main)
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| Challenge: | Large Pretrained Language Models (PLMs) have billions of parameters, causing computational challenges to fine-tuning models. |
| Approach: | They propose an Adapter-based fine-tuning with the Kronecker product that combine the outputs of two small networks to form a final vector whose dimension is the product of the dimensions of the individual outputs. |
| Outcome: | The proposed method achieves the same performance levels as state-of-the-art methods on the GLUE benchmark . |
Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation (2024.lrec-main)
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| Challenge: | Existing methods use contrastive learning (CL) to learn effective sentence representations, but require extensive human annotation. |
| Approach: | They propose a reinforcement learning approach for fine-tuning small-parameter LLMs to generate high-quality hard contrastive data without human feedback. |
| Outcome: | The proposed method achieves state-of-the-art on seven semantic text similarity tasks. |
Adaptive Simultaneous Sign Language Translation with Confident Translation Length Estimation (2024.lrec-main)
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| Challenge: | Existing non-simultaneous sign language translation methods suffer from inherent inference delays in real-time scenarios. |
| Approach: | They propose an adaptive policy for simultaneous sign language translation that progressively converts incrementally received sign video into its corresponding natural sentence. |
| Outcome: | The proposed policy excels in situations requiring extremely low latency. |
A Dataset for Named Entity Recognition and Entity Linking in Chinese Historical Newspapers (2024.lrec-main)
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| Challenge: | a novel historical Chinese dataset is used for named entity recognition, entity linking and entity relations. |
| Approach: | They propose a historical Chinese dataset for named entity recognition, entity linking, coreference and entity relations . they use Chinese newspapers from 1872 to 1949 and multilingual bibliographic resources from the same period . |
| Outcome: | The proposed dataset covers different styles and language uses, and is the largest historical Chinese NER dataset with manual annotations from this transitional period. |
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages (2024.lrec-main)
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Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin, Thomas Lavergne, Aurélie Névéol, Patrick Paroubek, Philippe Thomas, Tomohiro Nishiyama, Sebastian Möller, Eiji Aramaki, Yuji Matsumoto, Roland Roller, Pierre Zweigenbaum
| Challenge: | Existing clinical corpora mostly revolves around scientific articles in English . existing literature is limited to only a few scientific articles . |
| Approach: | They propose to use user-generated data sources to uncover adverse drug reactions . existing clinical corpora mostly revolves around scientific articles in english . authors provide statistics to highlight certain challenges associated with the corpus . |
| Outcome: | The proposed corpus includes 12 entity types, four attribute types, and 13 relation types . it provides strong baselines for extracting entities and relations between entities . |
Adding SPICE to Life: Speaker Profiling in Multiparty Conversations (2024.lrec-main)
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| Challenge: | Prior studies assumed the speaker’s persona’s immediate availability, a premise not universally applicable. |
| Approach: | They propose to synthesize persona attributes for each dialogue participant by combining three core tasks: persona discovery, persona-type identification, and persona value extraction. |
| Outcome: | The proposed task synthesizes persona attributes for each dialogue participant . the resulting model is compared against a baseline model and the proposed model is robust. |
ADEA: An Argumentative Dialogue Dataset on Ethical Issues Concerning Future A.I. Applications (2024.lrec-main)
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| Challenge: | Introducing ADEA: a dataset that captures online dialogues and focuses on ethical issues related to future AI applications. |
| Approach: | They propose a German dataset that captures online dialogues on ethical issues . the dataset includes over 2800 labeled user utterances on four different topics . they use an argument graph as the system's knowledge base and an annotation scheme . |
| Outcome: | The proposed dataset includes over 2800 user utterances on four ethical topics . the aim is to improve knowledge about AI ethics topics through argumentative dialogues . |
A Decade of Scholarly Research on Open Knowledge Graphs (2024.lrec-main)
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Houcemeddine Turki, Abraham Toluwase Owodunni, Mohamed Ali Hadj Taieb, René Fabrice Bile, Mohamed Ben Aouicha
| Challenge: | Several literature surveys have been done to understand how open knowledge graphs are constructed, evaluated, and integrated. |
| Approach: | They analyze 4445 scholarly articles retrieved from Scopus and analyze their results to identify trends, patterns, and impact of research in this field. |
| Outcome: | The results reveal an ever-increasing number of publications on open knowledge graphs published every year, especially in developed countries (+50 per year). |
A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference (2024.lrec-main)
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| Challenge: | Existing ILP frameworks are non-differentiable and cannot be integrated as part of a broader deep learning architecture. |
| Approach: | They propose a neuro-symbolic architecture for explanation-based NLI based on DBCS. |
| Outcome: | The proposed approach achieves superior performance when compared to existing solvers and black-box solver. |
A Document-Level Text Simplification Dataset for Japanese (2024.lrec-main)
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| Challenge: | Document-level text simplification tasks combine summarization and intra-sentence simplification. |
| Approach: | They devised a Japanese document-level text simplification dataset based on newspaper articles and Wikipedia. |
| Outcome: | The proposed dataset compared Japanese document-level text simplification models with English models and newspaper articles. |
A Dual-View Approach to Classifying Radiology Reports by Co-Training (2024.lrec-main)
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| Challenge: | Using the structure of a radiology report, we propose a co-training approach to train two machine learning models using the dual views of MRI and CT data. |
| Approach: | They propose a co-training approach where two machine learning models are built upon the Findings and Impression sections and use each other's information to boost performance with massive unlabeled data in a semi-supervised manner. |
| Outcome: | The proposed model outperforms supervised and semi-supervised methods in a public health surveillance study and outperformed existing methods. |
Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms (2024.lrec-main)
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| Challenge: | Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing models due to the lack of training data. |
| Approach: | They propose a method that uses masked tokens to generate a noisy text from a clean text by infilling mangled tokens with erroneous tokens. |
| Outcome: | The proposed method mimics translation errors found in real data and generates a noisy text from a clean text by infilling masked tokens with erroneous tokens. |
Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark (2024.lrec-main)
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| Challenge: | Compared with sentence-level topic structure, paragraph-level topics can grasp and understand the context of a document from a higher level. |
| Approach: | They propose a hierarchical paragraph-level topic structure representation with three layers to guide corpus construction. |
| Outcome: | The proposed method achieves the largest Chinese paragraph-level topic structure corpus, achieving high quality. |
A Family of Pretrained Transformer Language Models for Russian (2024.lrec-main)
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Dmitry Zmitrovich, Aleksandr Abramov, Andrey Kalmykov, Vitaly Kadulin, Maria Tikhonova, Ekaterina Taktasheva, Danil Astafurov, Mark Baushenko, Artem Snegirev, Tatiana Shavrina, Sergei S. Markov, Vladislav Mikhailov, Alena Fenogenova
| Challenge: | Developing Transformer language models for the Russian language has received little attention . most of these LMs are developed for English, which imposes substantial constraints on the potential of the language technologies. |
| Approach: | They propose to release 13 Russian Transformer language models that span three languages . they aim to broaden the scope of NLP research directions and develop industrial solutions for the Russian language. |
| Outcome: | The proposed models are based on Russian language datasets and benchmarks. |
A Fast and High-quality Text-to-Speech Method with Compressed Auxiliary Corpus and Limited Target Speaker Corpus (2024.lrec-main)
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| Challenge: | Existing methods to generate high-quality speech with limited target speaker corpus require extensive training data. |
| Approach: | They propose an auxiliary corpus compression algorithm that reduces the training cost while the naturalness of synthesized speech is not significantly degraded. |
| Outcome: | The proposed method significantly reduces training costs while maintaining the naturalness of synthesized speech. |
A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)
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| Challenge: | Neural text generation is notorious for repetitive loops and tedious outputs. |
| Approach: | They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text . |
| Outcome: | The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality. |
A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions (2024.lrec-main)
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| Challenge: | Visual question answering (VQA) tasks are often based on directives, which can cause ambiguities in human utterances. |
| Approach: | They propose a method that clarifies ambiguous questions using gaze information . they propose combining gaze information with gaze information to improve accuracy . |
| Outcome: | The proposed method improves performance in some cases of a GazeVQA system on Gaze. |
Agenda-Driven Question Generation: A Case Study in the Courtroom Domain (2024.lrec-main)
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| Challenge: | Existing automated question generation methods focus on unstructured text and lack agenda and background documents as context. |
| Approach: | They propose to leverage large language models for CourtQG by fine-tuning them on two auxiliary tasks, agenda explanation and question type prediction. |
| Outcome: | The proposed method generates better questions according to standard metrics when compared to several baselines. |
A Generative Model for Lambek Categorial Sequents (2024.lrec-main)
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| Challenge: | generative models such as PLC+ generate grammatical sentences with a high probability of being grammatized. |
| Approach: | They propose a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents. |
| Outcome: | The proposed model generates Lambek Categorial Grammar(LCG) sequents and is more robust to probabilistic context-free grammars. |
Agent-based Modeling of Language Change in a Small-world Network (2024.lrec-main)
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| Challenge: | linguists have been studying how external aspects of the linguistic system impact the grammatical system of a language. |
| Approach: | They propose to use agent-based modeling and simulation to study language change in a speech community using Zachary’s karate club network. |
| Outcome: | The proposed model shows that the centrality of each agent in the network, interpreted as social prestige, appears to be a factor influencing change. |
Agettivu, Aggitivu o Aghjettivu? POS Tagging Corsican Dialects (2024.lrec-main)
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| Challenge: | a series of experiments towards POS tagging Corsican are presented . POS tags are used to tag a less-resourced language spoken in corsica . |
| Approach: | They present a series of experiments towards POS tagging Corsican . they first contribute to the first gold standard POS-tagged corpus for Corsica . |
| Outcome: | The proposed model is the first POS tagger for Corsican, reaching an accuracy of 93.38%. |
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models (2024.lrec-main)
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Zhangyue Yin, Qiushi Sun, Qipeng Guo, Zhiyuan Zeng, Xiaonan Li, Tianxiang Sun, Cheng Chang, Qinyuan Cheng, Ding Wang, Xiaofeng Mou, Xipeng Qiu, Xuanjing Huang
| Challenge: | Recent advances in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. |
| Approach: | They propose a hierarchical reasoning aggregation framework to address this problem . they propose dynamic sampling to adjust the number of reasoning chains . |
| Outcome: | The proposed framework outperforms existing ensemble methods on complex reasoning tasks. |
A Hierarchical Sequence-to-Set Model with Coverage Mechanism for Aspect Category Sentiment Analysis (2024.lrec-main)
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| Challenge: | Aspect category sentiment analysis (ACSA) aims to detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs) generative models face three challenges, including addressing the missing predictions and focusing on relevant sentiment words. |
| Approach: | They propose to use sequence-to-set learning to tackle all three challenges simultaneously. |
| Outcome: | The proposed model is able to detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs) but it is unable to predict all aspect categories within a sentence due to the disordered set. |
A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News (2024.lrec-main)
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| Challenge: | a new dataset is being developed to enrich resources for sign language research . the dataset is 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses and 2,850 Chinese characters or 18K Chinese words. |
| Approach: | They introduce a new Hong Kong sign language dataset called TVB-HKSL-News . the dataset is collected from a TV news program and contains sign videos . they aim to support research in sign language recognition and translation . |
| Outcome: | The proposed dataset supports sign language recognition and translation research in Hong Kong . it consists of 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses and 2,850 Chinese characters or 18K Chinese words . |
A Hybrid Approach to Aspect Based Sentiment Analysis Using Transfer Learning (2024.lrec-main)
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| Challenge: | Aspect-Based Sentiment Analysis (ABSA) aims to identify terms or multiword expressions (MWEs) on which sentiments are expressed and the sentiment polarities associated with them. |
| Approach: | They propose a hybrid approach to Aspect-Based Sentiment Analysis using transfer learning . they exploit the strengths of large language models and traditional syntactic dependencies . |
| Outcome: | The proposed method exploits the strengths of large language models and traditional syntactic dependencies. |
A Japanese News Simplification Corpus with Faithfulness (2024.lrec-main)
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| Challenge: | Existing simplified corpora lack faithfulness to original text, resulting in errors in translation. |
| Approach: | They propose to simplify Japanese newspaper articles to prioritize faithfulness over automated models. |
| Outcome: | The proposed corpus preserves the original text, surpassing existing corpora. |
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation (2024.lrec-main)
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| Challenge: | Knowledge-based open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. |
| Approach: | They propose a benchmark for evaluating multi-source dialogue knowledge selection and response generation using Wikipedia's wizard of Wikipedia. |
| Outcome: | The proposed benchmark is called multi-source Wizard of Wikipedia (Ms.WoW) it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. |
A Large Annotated Reference Corpus of New High German Poetry (2024.lrec-main)
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| Challenge: | a corpus of public domain German poetry covering the time period 1600 to the 1920s contains 65k unique poems and over 1.6M lines, each tokenized, syllabified, pos-tagged, and meter-tagged. |
| Approach: | They present a large annotated corpus of public domain German poetry covering the time period 1600 to the 1920s with 65k poems. |
| Outcome: | The corpus contains 65k unique poems and over 1.6M lines, each tokenized, syllabified, pos-tagged, and meter-tagged. |
A Lifelong Multilingual Multi-granularity Semantic Alignment Approach via Maximum Co-occurrence Probability (2024.lrec-main)
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| Challenge: | Existing methods to mask and predict tokens in multilingual text limit multilingual interaction . |
| Approach: | They propose a lifelong multilingual multi-granularity semantic alignment approach which continuously extracts massive aligned linguistic units from noisy data via a maximum co-occurrence probability algorithm. |
| Outcome: | The proposed approach improves translation performance on WMT14 18 benchmarks in twelve directions. |
A Lightweight Approach to a Giga-Corpus of Historical Periodicals: The Story of a Slovenian Historical Newspaper Collection (2024.lrec-main)
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| Challenge: | a curated corpus of Slovenian historical newspapers is a complex undertaking requiring multiple steps to prepare . a shoestring budget is required to produce a corpus that is billion-words in size . |
| Approach: | They propose a lightweight approach to producing high-quality corpora using OCR . they use noisy OCR-ed data from the National and University Library of Slovenia . |
| Outcome: | The proposed method produces a billion-word giga-corpus of Slovenian historical newspapers from the 18th, 19th and 20th centuries on a shoestring budget. |
Aligning the Norwegian UD Treebank with Entity and Coreference Information (2024.lrec-main)
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| Challenge: | merged corpora of entity and coreference data are presented for the two written forms of Norwegian: Bokml and Nynorsk. |
| Approach: | They propose to combine entity and coreference data from two UD treebanks for Norwegian written forms: Bokml and Nynorsk. |
| Outcome: | The merged corpora comprise the first Norwegian UD treebank enriched with named entities and coreference information, supporting the standardized format for the CorefUD initiative. |
Alignment before Awareness: Towards Visual Question Localized-Answering in Robotic Surgery via Optimal Transport and Answer Semantics (2024.lrec-main)
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| Challenge: | Recent models for visual question localized-answering (VQLA) lack the ability to relate these answers to their localization at an instance level. |
| Approach: | They propose a model which introduces optimal transport to achieve bidirectional and fine-grained alignment between images and questions, enabling more precise localization. |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely-used datasets on surgical scenes and surgical instruments. |
Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine Translation (2024.lrec-main)
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| Challenge: | Existing knowledge distillation approaches to NMT often rely on heuristics when deciding which teacher layers to distill from. |
| Approach: | They propose an approach to align student attention heads with their teacher counterparts by heuristics to solve a feature mapping problem. |
| Outcome: | The proposed strategy shows gains of +3.61 and +0.63 BLEU points for WMT-2022 DeDsb and WMT-2014 EnDe compared to baselines. |
A Linguistically-Informed Annotation Strategy for Korean Semantic Role Labeling (2024.lrec-main)
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| Challenge: | Semantic role labeling is an essential component of semantic and syntactic processing of natural languages. |
| Approach: | They propose an annotation strategy for Korean semantic role labeling that is in line with the previously proposed linguistic theories as well as the distinct properties of the Korean language. |
| Outcome: | The proposed annotation strategy is consistent with the proposed linguistic theories and the distinct properties of the Korean language. |
Alleviating Exposure Bias in Abstractive Summarization via Sequentially Generating and Revising (2024.lrec-main)
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| Challenge: | Existing approaches to abstractive summarization suffer from exposure bias . Existing solutions bridge this gap through un- or semi-supervised holistic learning . |
| Approach: | They propose to reformat abstractive summarization to sequential generation and revision (SeGRe) this allows the model to assess the flawed summary from a global perspective and modify inappropriate expressions. |
| Outcome: | The proposed model can assess the flawed summary from a global view and modify inappropriate expressions. |
ALLIES: A Speech Corpus for Segmentation, Speaker Diarization, Speech Recognition and Speaker Change Detection (2024.lrec-main)
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| Challenge: | a meta corpus of audio files is used to gather, annotate and transcribe speech . a large number of speech databases are needed to perform multi-speaker tasks such as speaker diarization and speaker change detection. |
| Approach: | They propose to use human feedback to homogenize and correct speaker labels among the audio files by integrating human feedback within a speaker verification system. |
| Outcome: | The proposed protocol evaluates speech segmentation, speaker diarization, speech transcription and speaker change detection using human feedback. |
A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation (2024.lrec-main)
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| Challenge: | Existing models overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. |
| Approach: | They propose a model that utilizes logical entailment patterns to generate coherent explanations by leveraging logical patterns. |
| Outcome: | The proposed model produces more coherent and reasonable conclusions that closely align with the underlying premises. |
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework (2024.lrec-main)
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| Challenge: | Currently, ML&DL methods fail to provide reasons for stock trend predictions, lacking interpretability and reasoning processes. large language models (LLMs) suffer from hallucinations and are unable to keep up with the latest information. |
| Approach: | They develop a method to train large language models to handle financial analysis tasks . they use AlphaFin datasets to compare performance with traditional methods . |
| Outcome: | The proposed method improves stock trend prediction and financial question answering tasks. |
A Luxembourgish Corpus as a Gender Bias Evaluation Testset (2024.lrec-main)
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| Challenge: | According to the United Nations Development Programme, gender inequality is a metric composed of three dimensions: reproductive health, empowerment, and the labour market. |
| Approach: | They propose to design and play a physical game with digital elements that incorporates conversation analysis of transcribed speech and documenting bias. |
| Outcome: | The proposed game is based on a transcribed speech and audio transcripts. |
A Matter of Perspective: Building a Multi-Perspective Annotated Dataset for the Study of Literary Quality (2024.lrec-main)
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Yuri Bizzoni, Pascale Feldkamp Moreira, Ida Marie S. Lassen, Mads Rosendahl Thomsen, Kristoffer Nielbo
| Challenge: | a dataset collecting quality judgments on 9,000 English-language novels is presented . authors include experts opinions and crowd-sourced annotations . |
| Approach: | They propose a dataset collecting quality judgments on 9,000 English-language novels by 3,150 predominantly Anglophone authors. |
| Outcome: | The proposed dataset examines the perceived quality of 9,000 English-language novels by 3,150 predominantly anglophone authors. |
AMenDeD: Modelling Concepts by Aligning Mentions, Definitions and Decontextualised Embeddings (2024.lrec-main)
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| Challenge: | Contextualised Language Models (LMs) improve on word embeddings by encoding meaning of words in context. |
| Approach: | They propose to learn a unified embedding space in which all three types of representations can be integrated. |
| Outcome: | The proposed model outperforms existing approaches in ontology completion tasks. |
A Multi-Label Dataset of French Fake News: Human and Machine Insights (2024.lrec-main)
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Benjamin Icard, François Maine, Morgane Casanova, Géraud Faye, Julien Chanson, Guillaume Gadek, Ghislain Atemezing, François Bancilhon, Paul Égré
| Challenge: | a corpus of documents selected from 17 sources of french press considered unreliable by experts is annotated using 11 labels by 8 annotators. |
| Approach: | They present a corpus of documents annotated using 11 labels by 8 annotators . they use a subjectivity analyzer VAGO to clarify the link between subjective and fake news labels . |
| Outcome: | The proposed dataset identifies features that humans consider as characteristic of fake news . it also clarifies the link between ascriptions of the label Subjective and ascribeds of Fake News . |
A Multi-layered Approach to Physical Commonsense Understanding: Creation and Evaluation of an Italian Dataset (2024.lrec-main)
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| Challenge: | Using a multilingual model, we examine the ability of large language models to perform reasoning tasks. |
| Approach: | They propose to use a multilingual model to analyze commonsense reasoning in large language models for Italian and to provide a semi-automated system to complete the annotation. |
| Outcome: | The proposed model performs at high-level classification tasks but its easoning is inconsistent and unverifiable, since it does not capture intermediate evidence. |
A Multilingual Parallel Corpus for Aromanian (2024.lrec-main)
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| Challenge: | Aromanian is an endangered 1 language that currently lacks corpora and electronic resources that can potentially contribute to the preservation of its cultural heritage. |
| Approach: | They propose to create a corpus of Aromanian and equivalent sentence-aligned translations into Romanian, English, and French using orthographic standards. |
| Outcome: | The authors report that the first high-quality corpus of Aromanian is available in the Balkans and is available for download in Romanian, English, and French. |
A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation (2024.lrec-main)
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Cécile Macaire, Chloé Dion, Jordan Arrigo, Claire Lemaire, Emmanuelle Esperança-Rodier, Benjamin Lecouteux, Didier Schwab
| Challenge: | Existing algorithms for the automatic translation of spoken language into pictogram units are lacking for language impairments. |
| Approach: | They propose to use a French dataset that contains 230 hours of speech resources to create a rule-based pictogram grammar with a restricted vocabulary and a discussion of strategic decisions. |
| Outcome: | The proposed model is validated through multiple post-editing phases by expert annotators and is freely available under a non-commercial licence. |
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation (2024.lrec-main)
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| Challenge: | Existing methods for generating product descriptions from images are inaccurate and generic . e-commerce product descriptions are important for content marketing and increasing engagement . |
| Approach: | They propose a new setting for generating product descriptions from images, augmented by marketing keywords. |
| Outcome: | The proposed approach improves the accuracy and diversity of product descriptions by up to 3.3% on Rouge-L and 9.4% on D-5. |
A Multi-Task Transformer Model for Fine-grained Labelling of Chest X-Ray Reports (2024.lrec-main)
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| Challenge: | Chest radiography is one of the most commonly performed imaging examinations, with over 50 million chest X-rays annually in the United States . manual review of chest x-ray images can be onerous process prone to errors due to large volumes of images interpreted daily by radiologists . |
| Approach: | They propose a transformer-based model that segments radiology reports into semantically coherent segments and classifies each segment using a set of 37 radiological abnormalities. |
| Outcome: | The proposed model achieves state-of-the-art on report segmentation (0.0442 WinDiff) and multi-label classification (0.8 report-level macro F1) over 37 radiological labels and 8 NLP-specific labels. |
Analysis of Sensation-transfer Dialogues in Motorsports (2024.lrec-main)
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| Challenge: | a recent study has examined the effects of subjective ideas on group performance in motorsports. |
| Approach: | They collected dialogues between drivers and engineers in motorsports to test this hypothesis . they defined "sensation" as a unique event unfolding in the mind of a speaker . |
| Outcome: | The results show that the more subjective information interlocutors exchange, the better the group performance in collaborative work. |
Analysis on Unsupervised Acquisition Process of Bilingual Vocabulary through Iterative Back-Translation (2024.lrec-main)
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| Challenge: | Existing studies have demonstrated the effectiveness of iterative back-translation, but its reason has not been sufficiently elucidated. |
| Approach: | They propose a method for machine translation known as iterative back-translation . they use two monolingual data to create a pseudo-bilingual data and update translation models . |
| Outcome: | The proposed method improves translation quality and improves BLEU. |
Analyzing Chain-of-thought Prompting in Black-Box Large Language Models via Estimated V-information (2024.lrec-main)
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| Challenge: | Chain-of-Thought (CoT) prompting and large language models (LLMs) have shown great potential in improving performance on challenging reasoning tasks. |
| Approach: | They propose a new metric which extends the concept of pointwise V-information to black-box models and quantifies label-relevant new information introduced by CoT prompting. |
| Outcome: | The proposed metric extends the concept of pointwise V-information to black-box models, quantifying label-relevant new information introduced by CoT prompting beyond pre-existing label information. |
Analyzing Effects of Learning Downstream Tasks on Moral Bias in Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for fine-tuning large language models replicate and perpetuate social biases . pre-existing moral bias may be mitigated or amplified even when presented with opposing views . |
| Approach: | They develop methods to assess the agreement of LMs to explicit codified norms . they find that introducing downstream tasks may lead to unexpected inconsistencies . |
| Outcome: | The proposed model can be used to improve morality in data-scarce tasks. |
Analyzing Homonymy Disambiguation Capabilities of Pretrained Language Models (2024.lrec-main)
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Lorenzo Proietti, Stefano Perrella, Simone Tedeschi, Giulia Vulpis, Leonardo Lavalle, Andrea Sanchietti, Andrea Ferrari, Roberto Navigli
| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP) but current pretrained language models lack the granularity to perform disambiguation . |
| Approach: | They propose a large-scale resource that leverages homonymy relations to cluster WordNet senses and train Homonymy Disambiguation systems. |
| Outcome: | The proposed model can distinguish homonyms with up to 95% accuracy even without fine-tuning the underlying PLM. |
Analyzing Interpretability of Summarization Model with Eye-gaze Information (2024.lrec-main)
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| Challenge: | Existing studies have provided saliency scores for neural summarization models . eye-gaze information is often used as a proxy for human attention in reading tasks . |
| Approach: | They propose to compare model saliency to human eye-gaze data to determine whether it conforms to human gaze during summarization. |
| Outcome: | The proposed framework compares the model behavior to human summarization performance. |
Analyzing Large Language Models’ Capability in Location Prediction (2024.lrec-main)
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| Challenge: | Large language models (LLMs) are underutilized in the field of location prediction due to the sparsity of geotagged tweets. |
| Approach: | They present experimental results with four large language models in various instruction finetuning and exemplar settings and analyze whether taking into account the context is beneficial. |
| Outcome: | The proposed model is able to predict location in a variety of settings, including fine tuning and exemplar settings, and it is compared with the best model in the literature. |
Analyzing Occupational Distribution Representation in Japanese Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models have enabled users to generate fluent and seemingly convincing text, but they have uneven performance in different languages, which is associated with undesirable societal biases toward marginalized populations. |
| Approach: | They develop three Japanese language prompts to probe LLMs’ understanding of Japanese names and their association between gender and occupations. |
| Outcome: | The proposed models can associate Japanese names with correct gendered occupations when using constrained decoding, but with sampling or greedy decoding they prefer a small set of stereotypically genderes. |
Analyzing Symptom-based Depression Level Estimation through the Prism of Psychiatric Expertise (2024.lrec-main)
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| Challenge: | Existing approaches to automate depression estimation ignore medical professionals' knowledge of the problem. |
| Approach: | They propose to integrate domain experts' knowledge into a DAIC-WOZ dataset and propose a transformer-based model that incorporates their annotations. |
| Outcome: | The proposed model shows a strong correlation between the psychological tendencies of medical professionals and the behavior of the proposed model. |
Analyzing the Dynamics of Climate Change Discourse on Twitter: A New Annotated Corpus and Multi-Aspect Classification (2024.lrec-main)
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Shuvam Shiwakoti, Surendrabikram Thapa, Kritesh Rauniyar, Akshyat Shah, Aashish Bhandari, Usman Naseem
| Challenge: | a lack of data on climate change discourse has highlighted the need for further advancement . a new study examines the discourse on social media platforms that ignores climate change . |
| Approach: | They analyze climate change discourse on Twitter using a meticulously annotated dataset . they find relevance, stance, hate speech, direction of hate, humor and humor are key aspects . |
| Outcome: | The proposed method combines annotated tweets with a dataset of 15,309 tweets . it reveals tweet distribution patterns, stance prevalence, and hate speech trends . |
Analyzing the Performance of Large Language Models on Code Summarization (2024.lrec-main)
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| Challenge: | Large language models perform very well on tasks that involve both natural language and source code. |
| Approach: | They show that large language models perform very well on tasks that involve both natural language and source code. |
| Outcome: | The proposed models perform very well on tasks that involve both natural language and source code. |
Analyzing the Understanding of Morphologically Complex Words in Large Language Models (2024.lrec-main)
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| Challenge: | Morphologically complex languages are challenging for NLP as a large amount of information is condensed into a single word, unlike in analytical languages where separate words make it easier to derive meaning. |
| Approach: | They use a Large Language Model to analyse compositional word formation and derivation to find ill-formed word forms. |
| Outcome: | The proposed model is capable of solving most tasks except identifying ill-formed word forms. |
An Argument for Symmetric Coordination from Dependency Length Minimization: A Replication Study (2024.lrec-main)
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| Challenge: | a growing number of researchers are focusing on replication studies, but this is not the only reason. |
| Approach: | They replicate a result reported in a recent ACL paper that suggests left conjuncts are shorter in English coordinate structures. |
| Outcome: | The proposed results provide an argument for the symmetric structure of coordination in English coordinate structures. |
A Natural Approach for Synthetic Short-Form Text Analysis (2024.lrec-main)
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| Challenge: | Social media and news sites can be flooded with synthetically generated misinformation via tweets and posts while authentic users can inadvertently spread this text via shares and retweets. |
| Approach: | They propose a method of detecting synthetically generated tweets via a Transformer architecture and incorporate unique style-based features. |
| Outcome: | The proposed method detects synthetically generated tweets using a Transformer architecture and incorporating unique style-based features. |
An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation (2024.lrec-main)
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Ahmet Gunduz, Kamer Ali Yuksel, Kareem Darwish, Golara Javadi, Fabio Minazzi, Nicola Sobieski, Sébastien Bratières
| Challenge: | Text-to-speech (TTS) models require data availability and quality of training data. |
| Approach: | They propose an end-to-end tool to generate high-quality datasets for text-to speech models . language-specific phoneme distribution is integrated into sample selection, they argue . |
| Outcome: | The proposed tool aims to streamline the dataset creation process for voice-based technologies by integrating language-specific phonemes into sample selection and quality assurance of recordings. |
Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic Graphs (2024.lrec-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a sentencelevel formalism designed for English. |
| Approach: | They present an intuitive tool for evaluating graph-based meaning representations . they use an anchor broadcast alignment algorithm that is not subject to local maxima . |
| Outcome: | The proposed tool is highly correlated with the widely used Smatch score, but computation takes only about 40% the time. |
An Effective Span-based Multimodal Named Entity Recognition with Consistent Cross-Modal Alignment (2024.lrec-main)
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| Challenge: | Existing approaches to name entity recognition rely on word-based sequence labeling and align image and text at inconsistent semantic levels. |
| Approach: | They propose a span-based method which achieves a more consistent multimodal alignment from the perspectives of information-theoretic and cross-modal interaction. |
| Outcome: | Experiments on two datasets show that SMNER outperforms the state-of-the-art methods. |
An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition (2024.lrec-main)
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| Challenge: | Existing methods to recognize implicit discourse relations are limited by the lack of training data. |
| Approach: | They propose a method to generate synthetic data for IDRR using a large language model . they extract confused discourse relation pairs based on false negative rate and use two-stage prompting to generate effective synthetic data. |
| Outcome: | The proposed method achieves state-of-the-art macro-F1 performance without sacrificing micro-F1. |
An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation (2024.lrec-main)
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| Challenge: | Recent years have witnessed that massively multilingual neural machine translation (MMNMT) achieves a remarkable progress in both high- and low-resource language translation. |
| Approach: | They propose to use a robustness evaluation benchmark dataset to assess the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise. |
| Outcome: | The proposed dataset is publicly available at https://github.com/ID-ZH-MTRobustEval. |
An Evaluation of Croatian ASR Models for Čakavian Transcription (2024.lrec-main)
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| Challenge: | akavian is an endangered language closely related to Croatian . |
| Approach: | They evaluate four currently available automatic speech recognition systems that are trained on standard Croatian data and assess their performance in the transcription of akavian audio data. |
| Outcome: | The proposed models perform better than the standard conformer model and the best-performing variant of the CTC-based model. |
An Event-based Abductive Learning for Hard Time-sensitive Question Answering (2024.lrec-main)
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| Challenge: | Existing time-sensitive question answering models are limited for hard time-sensitive questions whose time qualifiers are implicit in the document. |
| Approach: | They propose a time-sensitive question answering framework that matches temporal events in documents with time qualifiers. |
| Outcome: | The proposed model outperforms baseline models for hard time-sensitive questions with 12.7% improvement in EM scores. |
A New Massive Multilingual Dataset for High-Performance Language Technologies (2024.lrec-main)
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Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón, Jelmer van der Linde, Shaoxiong Ji, Jaume Zaragoza-Bernabeu, Mikko Aulamo, Gema Ramírez-Sánchez, Andrey Kutuzov, Sampo Pyysalo, Stephan Oepen, Jörg Tiedemann
| Challenge: | a new massive multilingual dataset is available for language modeling and machine translation training. |
| Approach: | They present a massive multilingual dataset using web crawls from the Internet Archive and CommonCrawl . they use open-source software tools and high-performance computing to acquire, manage and process large corpora . |
| Outcome: | The HPLT language resources is a massive multilingual dataset . it includes monolingual and bilingual corpora extracted from CommonCrawl and the Internet Archive . the results are published online at the journal journal cense4 . |
An LCF-IDF Document Representation Model Applied to Long Document Classification (2024.lrec-main)
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| Challenge: | Document representation models have been used for years in NLP and Text Mining tasks but are limited when it comes to capturing the deeper semantics and context of textual data. |
| Approach: | They propose to use a Latent Concept Frequency-Inverse Document Frequence model to exploit the advantages of TF-IDF while incorporating semantic context into the model. |
| Outcome: | The proposed model outperforms existing models on the Long Document Classification task and shows that it performs better than TF-IDF and BERT-like representation models. |
An LLM-Enhanced Adversarial Editing System for Lexical Simplification (2024.lrec-main)
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| Challenge: | Existing methods to simplify text rely heavily on annotated data, making it challenging to apply in low-resource scenarios. |
| Approach: | They propose a Lexical Simplification method without parallel corpora that uses an Adversarial Editing System and an LLM-enhanced loss to distill knowledge into a small-size LS system. |
| Outcome: | The proposed method uses an LLM-enhanced loss to distill knowledge from Large Language Models (LLMs) into a small-size LS system. |
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports (2024.lrec-main)
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Lukas Lange, Marc Müller, Ghazaleh Haratinezhad Torbati, Dragan Milchevski, Patrick Grau, Subhash Chandra Pujari, Annemarie Friedrich
| Challenge: | Abstract: Natural language processing can help with managing large amounts of unstructured information. |
| Approach: | They propose to annotate a CC-BY-SA-licensed dataset of cyber threat reports . they use named entities, temporal expressions, and cybersecurity-specific concepts . |
| Outcome: | The proposed dataset annotates reports with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. |
Annotate Chinese Aspect with UMR——a Case Study on the Liitle Prince (2024.lrec-main)
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| Challenge: | Uniform Meaning Representation (UMR) is a graphbased cross-linguistically applicable semantic representation that allows for deep semantic analysis. |
| Approach: | They propose to use an aspectual lattice to adapt to different languages and design values that encompass both viewpoint aspect and situation aspect. |
| Outcome: | The proposed representations are based on the Chinese version of The Little Prince and are compared with other representations. |
Annotate the Way You Think: An Incremental Note Generation Framework for the Summarization of Medical Conversations (2024.lrec-main)
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| Challenge: | Existing datasets for summarization of medical conversations are limited to conversation-summary pairs . a novel annotation framework is proposed to capture the summarizing process via an annotation task . |
| Approach: | They propose an incremental note generation framework that captures the human summarization process via an annotation task by instructing annotators to first incrementally create a draft note and polish it into a reference note. |
| Outcome: | The proposed framework shows that the human summarization process is much more efficient and accurate than the current method. |
Annotating Chinese Word Senses with English WordNet: A Practice on OntoNotes Chinese Sense Inventories (2024.lrec-main)
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| Challenge: | a recent study has shown that large language models can be useful for cross-lingual applications. |
| Approach: | They propose to annotate Chinese word senses using English WordNet synsets . they examine the relationship between two annotators and find patterns among synset . |
| Outcome: | The proposed method shows that the annotators agree on 38% of the synsets compared with the original synset . the results highlight similarities between the synnotated synset and the WordNet structure . |
Annotating Customer-Oriented Behaviour in Call Centre Sales Dialogues (2024.lrec-main)
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| Challenge: | Customer-oriented behaviour (COB) is often hindered by a lack of clarity in its definition and lack of robust analytical, categorization, and computational approaches. |
| Approach: | They propose a conceptual and empirical framework for customer-oriented behaviour in call centre interactions . they aim to identify facets of COB that positively impact on Customer Satisfaction . |
| Outcome: | The proposed framework improves our understanding of the dynamics shaping sales strategies in call centres and holds promise for practical applications in optimising customer-agent interactions. |
Annotation and Classification of Relevant Clauses in Terms-and-Conditions Contracts (2024.lrec-main)
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| Challenge: | Using Large Language Models (LLMs) as foundational models, we propose a new annotation scheme to classify different types of clauses in Terms-and-Conditions contracts. |
| Approach: | They propose to use a new annotation scheme to classify clauses in Terms-and-Conditions contracts to support legal experts in identifying and assessing problematic issues. |
| Outcome: | The proposed annotation scheme achieves accuracies ranging from .79 to .95 on validation tasks. |
Annotation of Japanese Discourse Relations Focusing on Concessive Inferences (2024.lrec-main)
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| Challenge: | concessive discourse relations are crucial for understanding language, and annotation is performed on polysemous conjunctions. |
| Approach: | They present an annotation method for the Japanese conjunctions nagara and tsutsuke, and an annotation for the ambiguous Japanese conjunction tokorode. |
| Outcome: | The annotations for nagara and tsutsuke were performed on Japanese conjunctions and revealed the characteristics of concession that became apparent during annotation. |
Annotation of Transition-Relevance Places and Interruptions for the Description of Turn-Taking in Conversations in French Media Content (2024.lrec-main)
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| Challenge: | Few speech resources describe interruption phenomena, especially for TV and media content. |
| Approach: | They propose to annotation Transition-Relevance Places (TRPs) and Floor-Taking event types on an existing French TV and Radio broadcast corpus to facilitate studies of interruptions and turn-taking. |
| Outcome: | The proposed annotations on an existing French TV and Radio broadcast corpus show they are reliable and reliable . |
Annotations for Exploring Food Tweets from Multiple Aspects (2024.lrec-main)
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| Challenge: | The Latvian Twitter Eater Corpus (LTEC) is a collection of tweets gathered by following the appearance of 363 keywords related to food, drinks, eating and drinking in various valid word forms in the Latvian language. |
| Approach: | They build upon the Latvian Twitter Eater Corpus which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. |
| Outcome: | The Latvian Twitter Eater Corpus (LTEC) is a collection of tweets gathered by following the appearance of 363 keywords related to food and eating inflected in various valid word forms in the Latvian language. |
Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost (2024.lrec-main)
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| Challenge: | Current foundation models have shown impressive performance across various tasks, but they are not effective for everyone due to the imbalanced geographical and economic representation of the data used in the training process. |
| Approach: | They propose to identify the data to be annotated to balance model performance and annotation costs by finding countries with visual similarity for the topics. |
| Outcome: | The proposed methods improve model performance and reduce annotation costs by using data from countries with higher visual similarity for these topics. |
AnnoTheia: A Semi-Automatic Annotation Toolkit for Audio-Visual Speech Technologies (2024.lrec-main)
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| Challenge: | a small fraction of the languages currently covered by speech technologies are mainly spoken in English. |
| Approach: | They present an annotation toolkit that detects when a person speaks on the scene and the corresponding transcription. |
| Outcome: | The proposed toolkit can speed up the annotation process by up to four times . it can be used in Spanish, and is available on github. |
Announcing the Prague Discourse Treebank 3.0 (2024.lrec-main)
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| Challenge: | PDiT 3.0 contains 21,662 discourse relations (plus 445 list relations) in 49 thousand sentences. |
| Approach: | They present the Prague Discourse Treebank 3.0, a new version of the annotation of discourse relations marked by primary and secondary discourse connectives in the Prague Dependency Treebank. |
| Outcome: | The new version of the PDiT 3.0 brings a largely revised annotation of discourse relations and achieves consistency with a Lexicon of Czech Discourse Connectives (CzeDLex) and sense taxonomy. |
A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models (2024.lrec-main)
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Namu Park, Kevin Lybarger, Giridhar Kaushik Ramachandran, Spencer Lewis, Aashka Damani, Özlem Uzuner, Martin Gunn, Meliha Yetisgen
| Challenge: | Medical imaging reports document radiologists' interpretation of medical images through detailed narrative text. |
| Approach: | They propose a corpus of annotated medical imaging reports (CAMIR) that includes 609 annotation radiology reports from three imaging modality types. |
| Outcome: | The proposed schema captures clinical indications, lesions, and medical problems and can be used in secondary applications. |
A Novel Three-stage Framework for Few-shot Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing methods for Named Entity Recognition (NER) rely on labeled data, but data scarcity is a major challenge. |
| Approach: | They propose a framework for Few-shot Named Entity Recognition that can learn from limited labeled data and generalize to new domains. |
| Outcome: | The proposed framework surpasses existing methods on several benchmarks. |
AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments (2024.lrec-main)
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| Challenge: | Argument mining is a thriving task in natural language processing, but its generalization is limited by existing datasets. |
| Approach: | They propose to use a dataset to help model argument mining . the dataset AntCritic supports both argument component detection and argument relation prediction tasks. |
| Outcome: | The proposed model can detect arguments and identify their relationships automatically. |
An Unsupervised Framework for Adaptive Context-aware Simplified-Traditional Chinese Character Conversion (2024.lrec-main)
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| Challenge: | Traditional Chinese characters are still widely used in many areas of China . traditional methods to convert between simplified characters are ineffective . |
| Approach: | They propose an unsupervised adaptive context-aware conversion model that learns to convert between simplified and traditional Chinese characters under a denoising auto-encoder framework. |
| Outcome: | The proposed model outperforms strong unsupervised baselines and yields better conversion result for one-to-many cases. |
An Untold Story of Preprocessing Task Evaluation: An Alignment-based Joint Evaluation Approach (2024.lrec-main)
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Eunkyul Leah Jo, Angela Yoonseo Park, Grace Tianjiao Zhang, Izia Xiaoxiao Wang, Junrui Wang, MingJia Mao, Jungyeul Park
| Challenge: | a preprocessing task such as tokenization and sentence boundary detection (SBD) has been considered as a solution to many NLP challenges . however, the low error rates of current methods are mainly specific to certain tasks and rule-based tokenization can be difficult to use across different systems. |
| Approach: | They propose an evaluation algorithm that combines both tokenization and SBD results to improve evaluation reliability. |
| Outcome: | The proposed evaluation algorithm improves the reliability of evaluations by reevaluating the counts of true positive cases for F1 measures in both preprocessing tasks jointly. |
A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models (2024.lrec-main)
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Chenyang Lyu, Zefeng Du, Jitao Xu, Yitao Duan, Minghao Wu, Teresa Lynn, Alham Fikri Aji, Derek F. Wong, Longyue Wang
| Challenge: | Large Language Models (LLMs) are introducing a new phase in machine translation . despite advances in MT, there are still many challenges to overcome . |
| Approach: | They propose to highlight several new directions for MT that are influenced by Large Language Models like GPT-4 and ChatGPT. |
| Outcome: | The proposed models offer vast linguistic understandings and bring innovative methodologies, such as prompt-based techniques, that have the potential to further elevate MT. |
A Persona-Based Corpus in the Diabetes Self-Care Domain - Applying a Human-Centered Approach to a Low-Resource Context (2024.lrec-main)
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| Challenge: | Human-centered design (HCD) is a new approach to natural language processing that uses personas, user profiles and other tools to build corpus. |
| Approach: | They propose to use personas to model interpersonal interaction in a healthcare domain to follow an HCD approach. |
| Outcome: | The proposed model improves the quality of human-centered design in a healthcare domain and overcomes the lack of in-depth human-centricity in the field. |
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning (2024.lrec-main)
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| Challenge: | Existing methods to generate reasoning programs that ignore the differences between facts treated all facts equally, leading to wrong punishment of programs that differed from the ground truth. |
| Approach: | They propose an optimized training framework for long-form numerical reasoning that incorporates a number-aware negative sampling strategy and consistency-based reinforcement learning to increase execution accuracy. |
| Outcome: | The proposed method improves the performance of long-form numerical reasoning on the FinQA and ConvFinQA leaderboards. |
Applying Transfer Learning to German Metaphor Prediction (2024.lrec-main)
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| Challenge: | Existing methods for metaphor recognition are based on noun-verb pattern extraction or dictionary-informed. |
| Approach: | They propose to use an English corpus annotated for metaphor as a Gold standard for two different metaphor prediction setups. |
| Outcome: | The proposed method performs well on target-language data and achieves 90% F1 on target language data. |
Appraisal Framework for Clinical Empathy: A Novel Application to Breaking Bad News Conversations (2024.lrec-main)
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| Challenge: | Empathy is essential in healthcare communication. |
| Approach: | They propose an annotation approach that draws on well-established frameworks for clinical empathy and breaking bad news conversations for considering the dynamic dynamics of discourse relations. |
| Outcome: | The proposed model can be used to train models to detect causal relations involving empathy, a feature of systems that can provide feedback to medical professionals in training. |
Approaches and Challenges for Resolving Different Representations of Fictional Characters for Chinese Novels (2024.lrec-main)
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| Challenge: | Existing automatic text analysis tools and models are often developed for generic, open-domain tasks, restricting in-depth literary studies. |
| Approach: | They adapt a state-of-the-art anaphora resolution model to resolve character representations in Chinese novels by making some modifications and train a widely used BERT fine-tuned model for speaker extraction as assistance. |
| Outcome: | The proposed model is modified to resolve character representations in Chinese novels and train a BERT fine-tuned model for speaker extraction as assistance. |
A Preliminary Study of ChatGPT for Spanish E2R Text Adaptation (2024.lrec-main)
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| Challenge: | 16% of the world's population lacks literacy skills, according to the United Nations . |
| Approach: | They propose to use ChatGPT-4 to generate E2R text prompts and a checklist-based manual evaluation to evaluate 10 texts adapted by ChatGPt-4. |
| Outcome: | The proposed model is able to generate written language and adapt to LF in 10 texts. |
A Quantum-Inspired Matching Network with Linguistic Theories for Metaphor Detection (2024.lrec-main)
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| Challenge: | Metaphor identification procedures and selectional preference violations are challenging for machines to recognize and comprehend metaphors. |
| Approach: | They propose a quantum-inspired matching network for metaphor detection based on QLM . metaphors are widely present in the language, thought and behavior of humans . |
| Outcome: | The proposed method can be used to detect metaphors even in the face of conventional metaphors. |
Arabic Diacritization Using Morphologically Informed Character-Level Model (2024.lrec-main)
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Muhammad Morsy Elmallah, Mahmoud Reda, Kareem Darwish, Abdelrahman El-Sheikh, Ashraf Hatim Elneima, Murtadha Aljubran, Nouf Alsaeed, Reem Mohammed, Mohamed Al-Badrashiny
| Challenge: | Diacritics are typically omitted in Arabic writings and the reader needs to guess the proper diacritics as they are reading. |
| Approach: | They propose a morphologically informed character-level model that can recover both types of diacritics simultaneously. |
| Outcome: | The proposed model achieves lowest word-level diacritization error rate for Classical Arabic, MSA, and two dialectal Arabic texts. |
Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding (2024.lrec-main)
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| Challenge: | Existing knowledge graphs lack rich inference patterns and the limited ability to model arbitrary timestamps continuously. |
| Approach: | They propose a temporal knowledge graph-based temporal representation method that decomposes time information by polynomials and then enhances the model's capability to represent arbitrary timestamps flexibly. |
| Outcome: | The proposed method can encode arbitrary time information or even unseen timestamps while capturing rich inference patterns and higher-arity relations of the knowledge base. |
ARBRES Kenstur: A Breton-French Parallel Corpus Rooted in Field Linguistics (2024.lrec-main)
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| Challenge: | ARBRES is a project documenting the Breton language and state of research and engineering in linguistics and NLP. |
| Approach: | ARBRES is an ongoing project of open science documenting the Breton language and state of research and engineering in linguistics and NLP. |
| Outcome: | ARBRES Kenstur is a project documenting the Breton language and state of research and engineering in linguistics and NLP. |
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder (2024.lrec-main)
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| Challenge: | Existing methods for information extraction (IE) focus on training task-specific models, while common knowledge among different IE tasks is not explicitly modeled. |
| Approach: | They propose a regularization-based transfer learning method for IE via an instructed graph decoder which decodes various complex structures into a graph uniformly based on corresponding instructions. |
| Outcome: | The proposed method can learn common knowledge from existing datasets and transfer it to a new dataset with new labels. |
A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data (2024.lrec-main)
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| Challenge: | Existing methods for fine-tuning domain adaptation have overfitting problem in low-resource domains . lack of parallel data makes it difficult for model to learn domain-specific knowledge . |
| Approach: | They propose a Reinforcement Learning Domain Adaptation method for Neural Machine Translation that uses in-domain source monolingual data to make up for the lack of parallel data. |
| Outcome: | The proposed method can alleviate overfitting and reinforce the model to learn domain-specific knowledge. |
Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning (2024.lrec-main)
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| Challenge: | Recent studies on LLMs do not pay enough attention to linguistic and lexical semantic tasks, such as taxonomy learning. |
| Approach: | They propose a method for stochastic graph traversal and a new algorithm for data collection . they propose LLaMA-2 and Mistral for a lexical semantic task . |
| Outcome: | The proposed models can perform linguistic and lexical tasks, but they lack basic skills in taxonomy learning. |
Are Text Classifiers Xenophobic? A Country-Oriented Bias Detection Method with Least Confounding Variables (2024.lrec-main)
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| Challenge: | Existing methods for detecting biases are biased because of confounding variables . authors propose a method to detect the biased classifier on any type of unlabeled data . |
| Approach: | They propose a method to detect biases of a specific fine-tuned classifier on unlabeled data. |
| Outcome: | The proposed method detects biases on unlabeled data on named entity perturbations . it uses name-entity recognition on target-domain data and morphosynctactically different languages spoken in relation to countries of the target groups . |
Argument Quality Assessment in the Age of Instruction-Following Large Language Models (2024.lrec-main)
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Henning Wachsmuth, Gabriella Lapesa, Elena Cabrio, Anne Lauscher, Joonsuk Park, Eva Maria Vecchi, Serena Villata, Timon Ziegenbein
| Challenge: | Argument quality assessment is critical for opinion formation, decision making, writing education, and the like. |
| Approach: | They propose to use large language models to leverage knowledge across contexts to enable a much more reliable assessment. |
| Outcome: | The proposed approach improves the quality of argumentation and the ability to leverage knowledge across contexts. |
Article Classification with Graph Neural Networks and Multigraphs (2024.lrec-main)
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| Challenge: | Existing and newly published articles require complex and complex pipelines to classify them into context-specific label taxonomies. |
| Approach: | They propose to enrich Graph Neural Network pipelines with multi-graph representations that encode multiple signals of article relatedness as distinct edge types. |
| Outcome: | The proposed methods improve the performance of a variety of GNN models compared to default graphs. |
ART: The Alternating Reading Task Corpus for Speech Entrainment and Imitation (2024.lrec-main)
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Zheng Byron Yuan, Dorina de Jong, Ruitao Feng, Štefan Beňuš, Noël Nguyen, Róbert Sabo, Luciano Fadiga, Alessandro D’Ausilio
| Challenge: | The Alternating Reading Task (ART) Corpus is a collection of dyadic sentence readings for studying the entrainment and imitation behaviour in speech communication. |
| Approach: | They propose to use dyadic sentence reading to study entrainment and imitation in speech communication. |
| Outcome: | The proposed study includes three conditions and three subcorpora encompassing French-, Italian-, and Slovak-accented English. |
A Self-verified Method for Exploring Simile Knowledge from Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) have succeeded in natural language processing because they learn generic knowledge from a large corpus. |
| Approach: | They propose a method that allows pre-trained language models to explore simile knowledge from PLMs . they enhance PLM models with a multi-level simile recognition task that evaluates similes aplenty . |
| Outcome: | The proposed method can explore more accurate simile knowledge for PLMs. |
A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)
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| Challenge: | Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document. |
| Approach: | They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts. |
| Outcome: | The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets. |
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling (2024.lrec-main)
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| Challenge: | Existing models lack feature representations that capture the deep semantics of language and sensitivity to minor input variations, resulting in significant changes in the generated text. |
| Approach: | They propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots. |
| Outcome: | The proposed model outperforms existing models for generating empathetic embeddings, providing e-mpathetic and diverse responses. |
A Single Linear Layer Yields Task-Adapted Low-Rank Matrices (2024.lrec-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method that updates initial weight matrix W0 with a delta matrix W . |
| Approach: | They propose a method that updates initial weight matrix W0 with a delta matrix W consisting of two low-rank matrices A and B. |
| Outcome: | The proposed method maintains a performance on par with LoRA despite the fact that the trainable parameters of CondLoRA are fewer than those of LoRA. |
Asking and Answering Questions to Extract Event-Argument Structures (2024.lrec-main)
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| Challenge: | Traditionally, corpora are limited to arguments within the same sentence, and inter-sentential arguments are more challenging and have received less attention. |
| Approach: | They propose a question-answering approach to extract document-level event-argument structures by automating questions for each argument type an event may have. |
| Outcome: | The proposed model outperforms previous models and is especially beneficial to extract arguments that appear in different sentences than the event trigger. |
AssameseBackTranslit: Back Transliteration of Romanized Assamese Social Media Text (2024.lrec-main)
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| Challenge: | a novel dataset capturing native text composed in the Roman/Latin script is presented . the dataset comprises 60,312 Roman-native parallel transliterated sentences . |
| Approach: | They propose a back transliteration dataset capturing native text composed in the Roman/Latin script and its corresponding representation in the native Assamese script. |
| Outcome: | The proposed dataset outperforms baseline models in terms of word-level transliteration evaluation benchmarks and performance assessments. |
Assessing Online Writing Feedback Resources: Generative AI vs. Good Samaritans (2024.lrec-main)
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| Challenge: | Providing constructive feedback on student essays presents significant challenges . large language models (LLMs) such as ChatGPT can facilitate this process . |
| Approach: | They compare essayforum.com and large language models such as ChatGPT for students . they argue that both can mutually reinforce each other and provide constructive feedback . |
| Outcome: | The findings highlight the potential of AI in advancing the field of automated essay evaluation. |
Assessing the Capabilities of Large Language Models in Coreference: An Evaluation (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) are a new approach to coreference resolution, but their performance is not yet fully understood. |
| Approach: | They propose that future efforts should improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. |
| Outcome: | The proposed methods improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. |
Assessing the Efficacy of Grammar Error Correction: A Human Evaluation Approach in the Japanese Context (2024.lrec-main)
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| Challenge: | Using an automatic annotation toolkit, we evaluated the performance of the sequence tagging grammar error detection and correction model (SeqTagger) using Japanese university students’ writing samples. |
| Approach: | They evaluated the performance of the state-of-the-art sequence tagging grammar error detection and correction model using Japanese university students’ writing samples. |
| Outcome: | The proposed model shows a high precision but conservativeness in error detection and correction. |
A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing approaches to few-shot named entity recognition require large amounts of labeled data. |
| Approach: | They propose a streamlined span-based factorization method that solves few-shot NER problem . they propose to decompose the span-level alignment problem into several refined procedures . |
| Outcome: | The proposed method achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on SNIPS dataset. |
A Study on How Attention Scores in the BERT Model Are Aware of Lexical Categories in Syntactic and Semantic Tasks on the GLUE Benchmark (2024.lrec-main)
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| Challenge: | In the realm of sentence comprehension, human attention is not evenly distributed across all words, indicating systematic variations in language processing. |
| Approach: | They propose to categorize tokens according to their lexical categories and focus on changes in attention scores among these categories during the fine-tuning process for downstream tasks. |
| Outcome: | The proposed model is based on a GLUE benchmark dataset and demonstrates that it assigns more bias to specific lexical categories irrespective of the task. |
A Survey on Natural Language Processing for Programming (2024.lrec-main)
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| Challenge: | Natural language processing for programming is a field of NLP and software engineering . it is used to assist programming, and is increasingly prevalent for its effectiveness in improving productivity. |
| Approach: | They propose to use NLP techniques to assist programming by obtaining a structure-based representation and a functionality-oriented algorithm. |
| Outcome: | The proposed approach could relieve developers from laborious work while improving efficiency for non-professional users. |
A Tool for Determining Distances and Overlaps between Multimodal Annotations (2024.lrec-main)
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| Challenge: | Using the presented tool, it is possible to see how tiers overlap (even if they are of symbolic type) and the displacement regarding a reference unit. |
| Approach: | They propose a tool for extracting and comparing annotations from ELAN by comparing tiers to a reference unit. |
| Outcome: | The proposed tool can analyze ELAN annotations by comparing tiers to a reference unit. |
A Treebank of Asia Minor Greek (2024.lrec-main)
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| Challenge: | Asia Minor Greek (AMG) dialects are endangered because of declining speaker base and scarce linguistic resources. |
| Approach: | They propose to annotate a treebank of Pharasiot Greek, one of the Asia Minor Greek (AMG) dialects. |
| Outcome: | The proposed treebank consists of 350 sentences from six fairy tales in Pharasiot Greek. |
A Trusted Multi-View Evidential Fusion Framework for Commonsense Reasoning (2024.lrec-main)
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| Challenge: | Existing models that provide evidence for commonsense reasoning tasks have limitations . evidence is often interpreted in ways that are not directly available in the input. |
| Approach: | They propose a trusted multi-view evidential fusion framework that assesses the confidence of evidence and combines different views in a trustworthy manner. |
| Outcome: | The proposed framework can reason with multi-view evidence and compete with state-of-the-art models. |
Attack Named Entity Recognition by Entity Boundary Interference (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) is a cornerstone natural language processing task . despite its robustness, studies on its robustity are lacking. |
| Approach: | They propose a one-word modification NER attack that strategically inserts a new boundary into the sentence and triggers the model to make a wrong recognition. |
| Outcome: | The proposed method is effective on English and Chinese models with 70%-90% success rate. |
At the Crossroad of Cuneiform and NLP: Challenges for Fine-grained Part-of-speech Tagging (2024.lrec-main)
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| Challenge: | cuneiform texts are dominated by multiple languages and language families . the most dominant language written in cuniform is the Semitic Akkadian . existing cnl models are not suitable for digital editions of Akkadi . |
| Approach: | They focus on letters written in the Semitic Akkadian, a cuneiform language dominated by cuniform texts . they propose to use pre-trained embeddings, sentence segmentation and cnl to fine-tune language models . |
| Outcome: | The dominant language written in cuneiform is the Semitic Akkadian . the paper examines the input material and tries to initiate a discussion about best-practices . |
A Tulu Resource for Machine Translation (2024.lrec-main)
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| Challenge: | Using parallel datasets, we train a machine translation system in English–Tulu . |
| Approach: | They present a parallel dataset for English–Tulu translation using human translations into the multilingual machine translation resource FLORES-200. |
| Outcome: | The proposed model outperforms Google Translate by 19 BLEU points (in September 2023). |
A Two-Stage Framework with Self-Supervised Distillation for Cross-Domain Text Classification (2024.lrec-main)
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| Challenge: | Existing work on cross-domain text classification relies on domain-invariant features or task-agnostic features. |
| Approach: | They propose a two-stage framework for cross-domain text classification that leverages or reuses rich labeled data from the source domain and unlabeled data in the target domain. |
| Outcome: | The proposed framework achieves state-of-the-art on a public cross-domain text classification benchmark. |
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU (2024.lrec-main)
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| Challenge: | Multi-intent natural language understanding (NLU) models lack the rich information between the shared intents, especially in low-data scenarios. |
| Approach: | They propose a two-stage framework for multi-intent natural language understanding to harness shared intent information by word-level pre-training and prediction-aware contrastive fine-tuning. |
| Outcome: | The proposed framework surpasses baselines on low-data and full-data scenarios. |
A Typology of Errors for User Utterances in Chatbots (2024.lrec-main)
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| Challenge: | Non-prescriptive language uses in chatbot communication create challenges for Semantic Parsing (SP). |
| Approach: | They propose a typology based on a sample of non-prescriptive language uses mined from a domain-specific chatbot log and a framework for automatically mapping these errors to the typology. |
| Outcome: | The proposed typology can be extended to include new classes of errors across different domains and user demographics. |
Audiocite.net : A Large Spoken Read Dataset in French (2024.lrec-main)
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| Challenge: | Existing self-supervised learning methods for speech processing have proved difficult to apply to French due to the scarcity of large speech datasets. |
| Approach: | They present a corpus of 6,682 hours of audiobooks from 130 readers . they describe the creation process and final statistics of the corpus . |
| Outcome: | The proposed model based on the audiocite.net corpus, which contains 6,682 hours of audiobooks, was able to perform in 14k version. |
AuRoRA: A One-for-all Platform for Augmented Reasoning and Refining with Task-Adaptive Chain-of-Thought Prompting (2024.lrec-main)
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| Challenge: | Existing methods for large language models (LLMs) lean on handcrafted or task-specific demonstrations and lack reliable knowledge base. |
| Approach: | They propose a one-for-all platform for augmented reasoning and refining based on chain-of-thought prompting that excels in adaptability, reliability, integrity, and interpretability. |
| Outcome: | The proposed system exhibits superior performances across six reasoning tasks and offers real-time visual analysis. |
Automated Extraction of Prosodic Structure from Unannotated Sign Language Video (2024.lrec-main)
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| Challenge: | a new method for analyzing prosody in sign languages uses the velocity profile of the hands . the velocity profiles of hand movements can be used to analyse prosodic structure . |
| Approach: | They propose a method for extracting velocity information from unlabeled video of sign language using CoTracker. |
| Outcome: | The proposed method can extract prosodic information from unlabeled video clips. |
Automatically Estimating Textual and Phonemic Complexity for Cued Speech: How to See the Sounds from French Texts (2024.lrec-main)
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| Challenge: | Cued Speech (CS) is a visual communication system developed for people with hearing loss to complement speech reading at the phonetic level. |
| Approach: | They propose a method to phonemize written corpora so that each word is aligned with the corresponding CS key(s) this method is part of a wider project aimed at creating an augmented reality system displaying a virtual coding hand where the user will be able to choose a text upon its complexity for cueing. |
| Outcome: | The proposed method is part of a wider project aimed at creating an augmented reality system displaying a virtual coding hand where the user can choose a text upon its complexity for cueing. |
Automatic Animacy Classification for Romanian Nouns (2024.lrec-main)
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| Challenge: | Animacy is a semantic property of nouns that describes the quality of the noun's referent of being alive, sentient or volitional. |
| Approach: | They propose a type-based binary classifier of Romanian nouns into the classes human/non-human using pre-trained word embeddings and animacy information derived from Romanian WordNet. |
| Outcome: | The proposed classifiers perform well on the Romanian language and in a naturalistic setting. |
Automatic Annotation of Grammaticality in Child-Caregiver Conversations (2024.lrec-main)
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| Challenge: | Existing methods for analyzing child language acquisition have been tedious and inconsistent. |
| Approach: | They propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate 4,000 utterances from a large corpus of transcribed conversations. |
| Outcome: | The proposed method achieves human inter-annotation agreement levels and is faster and reproducible than manual methods. |
Automatic Authorship Analysis in Human-AI Collaborative Writing (2024.lrec-main)
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| Challenge: | Existing methods for authorship analysis and text detection are limited . authors: human-AI collaborative writing poses a potential challenge for existing methods . |
| Approach: | They investigate the extent to which existing AI detection and authorship analysis models can perform classification on data generated in human-AI collaborative writing sessions. |
| Outcome: | The proposed models outperform existing models on human-AI collaborative writing data . authors say human- AI co-written text will require adapting models in the near future . |
Automatic Coding of Contingency in Child-Caregiver Conversations (2024.lrec-main)
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| Challenge: | Current research on children's language development relies on manual annotation of a small sample of children, which limits our ability to draw general conclusions about development. |
| Approach: | They propose to use automatic tools to assess contingency in children's natural interactions with caregivers by annotating a small set of data with a Transformer-based model. |
| Outcome: | The proposed model replicates existing results and generates new data-driven hypotheses. |
Automatic Construction of a Chinese Review Dataset for Aspect Sentiment Triplet Extraction via Iterative Weak Supervision (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a task that involves the extraction of three key elements: target aspects, descriptive opinion spans, and their corresponding sentiment polarity. |
| Approach: | They propose a framework that facilitates automatic construction of Aspect Sentiment Triplet Extraction (ASTE) by iterative weak supervision and a discriminator to weed out subpar samples. |
| Outcome: | The proposed framework automates the construction of Aspect Sentiment Triplet Extraction tasks in Chinese by using iterative weak supervision. |
Automatic Construction of a Large-Scale Corpus for Geoparsing Using Wikipedia Hyperlinks (2024.lrec-main)
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| Challenge: | Existing methods to evaluate geoparsing systems are small-scale and lack coverage of location expressions on general domains. |
| Approach: | They propose a method to construct a large-scale corpus for geoparsing from Wikipedia articles. |
| Outcome: | The proposed method can annotate multiple location expressions with coordinates even with ambiguous expressions. |
Automatic Data Visualization Generation from Chinese Natural Language Questions (2024.lrec-main)
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| Challenge: | Existing studies on data visualization generation from natural languages have not been conducted on Chinese Text-to-Vis. |
| Approach: | They propose to generate a Chinese text-to-vis dataset using a multilingual encoder and a cross-lingual ability. |
| Outcome: | The proposed dataset is challenging and deserves further research. |
Automatic Decomposition of Text Editing Examples into Primitive Edit Operations: Toward Analytic Evaluation of Editing Systems (2024.lrec-main)
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| Challenge: | Existing methods to automate text editing tasks are blackboxed and do not understand the behavior of the systems. |
| Approach: | They propose a task of automatic decomposition of text editing examples into primitive edit operations by using a phrase aligner and a large language model. |
| Outcome: | The proposed method perfectly decomposes 44% and 64% of editing examples . Detailed analyses also provide insights into the difficulties of this task . |
Automatic Extraction of Language-Specific Biomarkers of Healthy Aging in Icelandic (2024.lrec-main)
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| Challenge: | Multiple studies have shown that individuals suffering from AD exhibit difficulties with word retrieval, produce fewer information units and content words, and use more pronouns than healthy age-matched controls. |
| Approach: | They administered three language tasks to participants aged 60–80 to examine the effects of task type and healthy aging on various automatically extracted part-of-speech features in Icelandic. |
| Outcome: | The results show that task type and healthy aging influence language production in Icelandic. |
Automatic Extraction of Nominal Phrases from German Learner Texts of Different Proficiency Levels (2024.lrec-main)
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| Challenge: | a pilot study has found that inflecting determiners and adjectives correctly is a challenge for learners of German. |
| Approach: | They propose to use dependency parsing to extract nouns, grammatical heads and dependents that have to agree with the noun in German. |
| Outcome: | The proposed method performs well on CEFR levels A1-B1 but not level B2 texts. |
Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats (2024.lrec-main)
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Philipp Heinrich, Andreas Blombach, Bao Minh Doan Dang, Leonardo Zilio, Linda Havenstein, Nathan Dykes, Stephanie Evert, Fabian Schäfer
| Challenge: | Existing methods to identify and track conspiracy narratives are difficult to track and use because of their short-lived nature. |
| Approach: | They analysed 1,000 German Telegram posts tagged with 14 fine-grained conspiracy narrative labels by three independent annotators. |
| Outcome: | The proposed methods compare well with off-the-shelf methods and human performance. |
Automatic Partitioning of a Code-Switched Speech Corpus Using Mixed-Integer Programming (2024.lrec-main)
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| Challenge: | Currently, partitioning speech corpora is done by hand, but this is not feasible for the dataset under investigation. |
| Approach: | They propose to partition a 41.6-hour corpus of code-switched speech into training, development and testing partitions using mixed-integer linear programming. |
| Outcome: | The proposed method allows to partition a 41.6-hour corpus of code-switched speech into training, development and testing partitions while maintaining a fixed number of speakers and a specific amount of codeswitching speech in the development and test partitions. |
Automatic Punctuation Model for Spanish Live Transcriptions (2024.lrec-main)
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Mario Perez-Enriquez, Jose Manuel Masiello-Ruiz, Jose Luis Lopez-Cuadrado, Israel Gonzalez-Carrasco, Paloma Martinez-Fernandez, Belen Ruiz-Mezcua
| Challenge: | Punctuation marks, including periods, commas, question marks, and exclamation points, are crucial cues for sentence boundaries, intonation, and context, significantly aiding in the comprehension of spoken Spanish. |
| Approach: | They propose to integrate punctuation into automatic transcription tools by using a model pre-trained with data from the Spanish National Library. |
| Outcome: | The proposed model improves readability and accuracy of Spanish transcriptions by incorporating punctuation into the outputs generated by automatic transcription tools. |
Automatic Speech Interruption Detection: Analysis, Corpus, and System (2024.lrec-main)
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| Challenge: | Interruption detection is a new but challenging task in the field of speech processing. |
| Approach: | They propose to define automatic speech interruption detection and build a specialized corpus to analyze interrupted conversations. |
| Outcome: | The proposed system can detect interruptions in speech with promising results . it can be used to ensure speaking turns are respected during official political debates . |
Automatic Speech Recognition for Gascon and Languedocian Variants of Occitan (2024.lrec-main)
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Iñigo Morcillo, Igor Leturia, Ander Corral, Xabier Sarasola, Michaël Barret, Aure Séguier, Benaset Dazéas
| Challenge: | a new system for automatic speech recognition is being developed for two main Occitan dialects . the difficulty lies in the fact that Occitian is a less-resourced language . |
| Approach: | They propose to develop an automatic speech recognition system for two Occitan dialects . they use Kaldi, acoustic models, and Whisper to create a model from corpora . |
| Outcome: | The proposed system is based on Kaldi and Whisper for two main Occitan dialects . the system is more robust than previous systems, and the results are promising . |
Automatic Speech Recognition System-Independent Word Error Rate Estimation (2024.lrec-main)
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| Challenge: | Word error rate (WER) is a metric used to evaluate the quality of transcriptions produced by Automatic Speech Recognition systems. |
| Approach: | They propose a hypothesis generation method for ASR system-dependent WER estimation . they use phonetically similar or linguistically more likely alternative words to generate hypotheses . |
| Outcome: | The proposed method outperforms baseline estimators on in-domain data and out-of-domain on Switchboard and CALLHOME. |
Automating Dataset Production Using Generative Text and Image Models (2024.lrec-main)
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| Challenge: | a lack of benchmarks or data for natural language processing is hindering empirical methods . a new pipeline is proposed to reduce the burden of producing image and text datasets . |
| Approach: | They propose a pipeline to reduce the research burden of producing image and text datasets when datasets may not exist. |
| Outcome: | The proposed pipeline reduces the research burden of producing image and text datasets when datasets may not exist. |
Autonomous Aspect-Image Instruction a2II: Q-Former Guided Multimodal Sentiment Classification (2024.lrec-main)
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| Challenge: | Existing methods to identify sentiment polarities of aspects are limited by the limited multimodal data available. |
| Approach: | They propose to use instruction tuning paradigm to combine language and vision data to combine text and image modalities. |
| Outcome: | The proposed model achieves state-of-the-art on benchmark datasets and in few-shot settings. |
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification (2024.lrec-main)
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| Challenge: | Existing methods for ICD indexing use machine learning to assign subset of codes to medical records . experimental results show proposed method achieves state-of-the-art performance on a number of measures. |
| Approach: | They propose a method that uses a deep dilated residual convolution encoder to learn document representations across different lengths of the texts. |
| Outcome: | The proposed method achieves state-of-the-art performance on a number of measures. |
A Virtual Patient Dialogue System Based on Question-Answering on Clinical Records (2024.lrec-main)
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| Challenge: | a new approach to annotating medical dialogues with intents is proposed for virtual patients . a VP is a system that allows medical students to simulate a real clinical consultation . |
| Approach: | They propose to annotate medical dialogue questions in Spanish and a second dataset of dialogues using a novel annotation approach. |
| Outcome: | The proposed approach eliminates the need for manually structured patient records . the two datasets and the code will be freely available for the research community. |
A Web Portal about the State of the Art of NLP Tasks in Spanish (2024.lrec-main)
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Enrique Amigó, Jorge Carrillo-de-Albornoz, Andrés Fernández, Julio Gonzalo, Guillermo Marco, Roser Morante, Laura Plaza, Jacobo Pedrosa
| Challenge: | a web portal has been created with information about the state of the art of natural language processing tasks in Spanish. |
| Approach: | They propose a web portal that provides information about the state of the art of natural language processing tasks in Spanish. |
| Outcome: | The portal provides information about forums, competitions, tasks and datasets in Spanish that would otherwise be spread in multiple articles and web sites. |
A Workflow for HTR-Postprocessing, Labeling and Classifying Diachronic and Regional Variation in Pre-Modern Slavic Texts (2024.lrec-main)
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Piroska Lendvai, Maarten van Gompel, Anna Jouravel, Elena Renje, Uwe Reichel, Achim Rabus, Eckhart Arnold
| Challenge: | a workflow for classifying diachronic and regional language variation in medieval texts is currently being developed . the workflow is generic or language-agnostic, but can be applied to other historical languages as well. |
| Approach: | They propose a workflow for classifying diachronic and regional language variation in medieval texts . they use handwritten text recognition and manual transcription to obtain the data . |
| Outcome: | The proposed workflow covers HTR-postprocessing, annotating and classifying medieval texts . it is accessible to humanists with limited experience in research data infrastructures, analysis or NLP . |
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have enabled advances in the field of natural language processing . however, their application and potential are still underexplored . |
| Approach: | They evaluate four state-of-the-art instruction-tuned Large Language Models on 13 NLP tasks in English. |
| Outcome: | The evaluated models outperform state-of-the-art models on 13 real-world clinical and biomedical NLP tasks in English. |
Backdoor NLP Models via AI-Generated Text (2024.lrec-main)
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| Challenge: | Existing attacks disregard fluency and semantic fidelity of poisoned text, rendering it easily detectable. |
| Approach: | They propose to use AI-generated poisoned text to attack NLP models by establishing covert associations between trigger patterns and target labels without affecting normal accuracy. |
| Outcome: | The proposed method achieves effective attacks while maintaining fluency and semantic similarity across all scenarios. |
BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages (2024.lrec-main)
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Roberts Dargis, Arturs Znotins, Ilze Auzina, Baiba Saulite, Sanita Reinsone, Raivis Dejus, Antra Klavinska, Normunds Gruzitis
| Challenge: | Latvian is a low-resource language for many NLP tasks, but most speech corpora are closed data . a crowdsourcing campaign to create a relatively large, diverse and open speech corpus for Latvian has been launched . |
| Approach: | a crowdsourcing campaign is helping to create an open speech corpus for Latvian . the goal is to enlarge the datasets and make them more diverse . authors use the opensource Mozilla Common Voice platform to validate speech samples . |
| Outcome: | a crowdsourcing initiative has increased the size and speaker diversity of the Latvian Common Voice 17.0 dataset by more than tenfold in less than a year. |
BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models (2024.lrec-main)
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| Challenge: | Existing long context models suffer from performance decline when the input text exceeds their length limit. |
| Approach: | They propose a multi-task long context benchmark to evaluate LLMs' long context ability using 10 datasets from 5 different NLP tasks. |
| Outcome: | The proposed model covers 5 domains and core capacities of large language models. |
BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering (2024.lrec-main)
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| Challenge: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. |
| Approach: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. authors propose a framework that can automatically construct Bengali KG from any Bangla text. |
| Outcome: | The proposed framework can automatically construct Bengali KGs from any Bangla text. |
BAN-PL: A Polish Dataset of Banned Harmful and Offensive Content from Wykop.pl Web Service (2024.lrec-main)
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Anna Kolos, Inez Okulska, Kinga Głąbińska, Agnieszka Karlinska, Emilia Wisnios, Paweł Ellerik, Andrzej Prałat
| Challenge: | a new dataset of offensive social media content for the Polish language is presented to address this gap . access to accurate and non-synthetic datasets of social media is limited for low-resource languages . |
| Approach: | They present a new open dataset of offensive social media content for the Polish language . authors propose to make the dataset publicly available to improve access . |
| Outcome: | The proposed dataset includes 691,662 posts and comments from the Polish Reddit . the authors describe the dataset and apply it to real-life content moderation processes . |
“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning (2024.lrec-main)
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| Challenge: | Existing methods for generating humorous puns are limited and require a broad spectrum of commonsense and worldly skills. |
| Approach: | They propose a GAN-based approach that employs semantic pruning and contrastive learning to generate humorous puns using a model that captures the semantic nuances of puns. |
| Outcome: | The proposed model produces semantically coherent and humorous puns while ensuring both correctness and humor. |
Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation (2024.lrec-main)
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| Challenge: | Davidson et al.: hate speech is a growing media presence, but research on generating CNs has been limited . he says a new dataset for CN generation is available for basque and spanish . this dataset is based on a multilingual encoder-decoder model . |
| Approach: | They propose a new Basque and Spanish dataset for automatic CN generation . they use machine translation and professional post-edition to generate CNs in both languages . |
| Outcome: | The proposed datasets show that training on post-edited data improves generation over monolingual settings . similar results in zero-shot crosslingual evaluations show multilingual data augmentation outperforms training in English and Spanish . |
Becoming a High-Resource Language in Speech: The Catalan Case in the Common Voice Corpus (2024.lrec-main)
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| Challenge: | a project to create a publicly available voice dataset for speech recognition systems in Catalan is a multifaceted challenge. |
| Approach: | They propose to create a publicly available voice dataset for future speech technologies in Catalan using the Mozilla Common Voice crowd-sourcing platform. |
| Outcome: | The proposed dataset shows that Catalan ranks as the most prominent language in the corpus. |
BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language (2024.lrec-main)
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| Challenge: | Existing multilingual evaluation benchmarks focus on IR in the Polish language, but the Polish is a relatively new field due to the limited availability of Polish datasets. |
| Approach: | They propose to establish large-scale resources for IR in the Polish language and translate them into a new benchmark which includes 13 datasets. |
| Outcome: | The proposed benchmarks are based on 13 open IR datasets in Polish and are a pioneering development in this area. |
Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have revolutionized the field of natural language processing . however, it has been shown that they lack systematic generalization, which allows to extrapolate the learned statistical regularities outside the training distribution. |
| Approach: | They propose to benchmark a LLM with two parameters to find out its performance . they compare it to a variant of the Transformer-Encoder architecture to find the same problem . |
| Outcome: | The proposed model outperforms the previous model on three algorithmic tasks with two parameters. |
Benchmarking Hallucination in Large Language Models Based on Unanswerable Math Word Problem (2024.lrec-main)
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| Challenge: | Large language models (LLMs) are highly effective in various natural language processing tasks, but can produce unreliable conjectures in ambiguous contexts, which is known as hallucination. |
| Approach: | They propose a method to evaluate LLM hallucination in Question Answering based on the unanswerable math word problem (UMWP) . they combine text similarity and mathematical expression detection to determine whether LLM considers the question unanswered. |
| Outcome: | The proposed method combines text similarity and mathematical expression detection to determine whether the LLM considers the question unanswerable. |
Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT (2024.lrec-main)
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Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi, Nesa Abbasi, Mohammad Hadi Babalou, Ali Edalat, Sepehr Kamahi, Samin Mahdizadeh Sani, Nikoo Naghavian, Danial Namazifard, Pouya Sadeghi, Yadollah Yaghoobzadeh
| Challenge: | a new study examines the efficacy of large language models (LLMs) for Persian . ChatGPT and LLMs have shown remarkable performance in English, but their efficiency for low-resource languages remains an open question. |
| Approach: | They present a benchmarking study of large language models (LLMs) for Persian . they focus on GPT-3.5-turbo, but also GPT-4 and OpenChat-3.5 . |
| Outcome: | The proposed model performs better in Persian than other low-resource languages . the study is the first comprehensive benchmarking of large language models . |
Benchmarking the Performance of Machine Translation Evaluation Metrics with Chinese Multiword Expressions (2024.lrec-main)
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| Challenge: | Multiword Expressions (MWEs) are hard nuts for many natural language processing tasks. |
| Approach: | They annotate 28 types of Chinese MWEs and then examine 31 MTE metrics on groups of sentences containing different MWE. |
| Outcome: | The results show that MT systems and MTE metrics still suffer from MWEs . |
Benchmarking the Simplification of Dutch Municipal Text (2024.lrec-main)
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| Challenge: | Text simplification (TS) is a technique that makes written information more accessible to all people, especially those with cognitive or language impairments. |
| Approach: | They propose to use English as a pivot language for simplification of Dutch medical and municipal texts. |
| Outcome: | The proposed approach improves on Dutch medical text, while the existing pipeline performs better on all metrics. |
BengaliLCP: A Dataset for Lexical Complexity Prediction in the Bengali Texts (2024.lrec-main)
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| Challenge: | Lexical Complexity Prediction (LCP) is a task for predicting the complexity score of a word or phrase based on its context. |
| Approach: | They propose a deep neural approach to predict lexical complexity of Bengali tokens using an annotated dataset. |
| Outcome: | The proposed neural approach outperforms state-of-the-art models for Bengali language. |
BenLLM-Eval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP (2024.lrec-main)
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Mohsinul Kabir, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, M Saiful Bari, Enamul Hoque
| Challenge: | Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing. |
| Approach: | They propose to evaluate LLMs in Bengali to benchmark their performance . they select Bangla NLP tasks such as text summarization, question answering, paraphrasing . |
| Outcome: | The proposed model performs better in some tasks than current models, but in most tasks, it is poor . |
BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)
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| Challenge: | Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios. |
| Approach: | They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation. |
| Outcome: | The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection. |
Beyond Binary: Towards Embracing Complexities in Cyberbullying Detection and Intervention - a Position Paper (2024.lrec-main)
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Kanishk Verma, Kolawole John Adebayo, Joachim Wagner, Megan Reynolds, Rebecca Umbach, Tijana Milosevic, Brian Davis
| Challenge: | Existing methods for CB detection oversimplify the problem of CB as a binary classification task. |
| Approach: | They propose to use large language models to generate CB-related datasets . they propose to combine cognitive and linguistic models to help identify CB incidents . |
| Outcome: | The proposed approach aims to help researchers and policymakers make informed decisions . it uses large language models such as Claude-2 and Llama2-Chat to generate CB-related datasets . |
Beyond Canonical Fine-tuning: Leveraging Hybrid Multi-Layer Pooled Representations of BERT for Automated Essay Scoring (2024.lrec-main)
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| Challenge: | Existing work on automated essay scoring focuses on capturing deep semantic features but are limited to lower-level textual features. |
| Approach: | They propose to use BERT's multi-layer architecture to leverage hierarchical linguistic information from its intermediate layers to improve overall essay scoring performance. |
| Outcome: | The proposed model outperforms the standard model with the default output on the ASAP AES dataset. |
Beyond Code: Evaluate Thought Steps for Complex Code Generation (2024.lrec-main)
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| Challenge: | Existing efforts to generate code in C++ rely on relatively simple programming problems . large language models (LLMs) pre-trained on numerous code data have opened up new opportunities for code generation. |
| Approach: | They propose a task that evaluates the quality of thought steps and code implementation . they construct a dataset of complex programming problems in C++ . |
| Outcome: | The proposed task evaluates the quality of thought steps and code implementation in a C++ programming language. |
Beyond Full Fine-tuning: Harnessing the Power of LoRA for Multi-Task Instruction Tuning (2024.lrec-main)
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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning algorithm for large-scale language models. |
| Approach: | They conduct a systematic study of Low-Rank Adaptation (LoRA) on diverse tasks and rich resources with different learning capacities. |
| Outcome: | The proposed algorithm can achieve remarkable performance in high-resource and multi-task scenarios, even comparable to full fine-tuning. |
Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling (2024.lrec-main)
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| Challenge: | Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers. |
| Approach: | They propose a method that incorporates both belief and desire to accurately identify emotions by extracting emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations. |
| Outcome: | The proposed model outperforms existing models on four popular ERC datasets and validates its performance with multiple state-of-the-art models. |
Beyond Model Performance: Can Link Prediction Enrich French Lexical Graphs? (2024.lrec-main)
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| Challenge: | lexical resources are essential for the development of NLP systems, but with advances in language models and deep learning, they are increasingly being replaced by web-derived text. |
| Approach: | They propose a resource-centric study of link prediction approaches over French lexical-semantic graphs. |
| Outcome: | The proposed method is more accurate and reliable than previous methods. |
Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants’ API Invocation Capabilities (2024.lrec-main)
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| Challenge: | Existing evaluation methods for human-machine interactions are static and can be misleading. |
| Approach: | They propose to use a LLM-based user agent to assess an assistant's API call capability without human involvement. |
| Outcome: | The proposed method mirrors real human conversation patterns in human-machine interactions, and shows that it aligns more closely with human assessment. |
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection (2024.lrec-main)
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Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
| Challenge: | Out-of-domain (OOD) intent detection is crucial for task-oriented dialogue systems. |
| Approach: | They conduct a comprehensive evaluation of large language models (LLMs) under various experimental settings and outline their strengths and weaknesses. |
| Outcome: | The proposed models exhibit strong zero-shot and few-shot capabilities, but is still at a disadvantage compared to models fine-tuned with full resource. |
Beyond Words: Decoding Facial Expression Dynamics in Motivational Interviewing (2024.lrec-main)
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| Challenge: | Motivational Interviewing (MI) is a directive, clientcentered therapeutic approach designed to facilitate behaviour change by enhancing individuals' intrinsic motivation. |
| Approach: | They propose to decode facial expressions of both client and therapist in the context of Motivational Interviewing using an annotation system. |
| Outcome: | The proposed method identifies facial expressions of both interlocutors and client over a counseling session and reveals the correlation between the facial expression and the different types of talk, as well as the interplay between interlocutetors’ expressions. |
BigNLI: Native Language Identification with Big Bird Embeddings (2024.lrec-main)
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| Challenge: | Native Language Identification (NLI) is a task that relies on time-consuming linguistic feature engineering and current transformer models are limited by input size. |
| Approach: | They propose to train a logistic regression classifier which only uses Big Bird embeddings to overcome this limitation. |
| Outcome: | The proposed method outperforms linguistic feature engineering models on the Reddit-L2 dataset and shows consistent out-of-sample and out-off-domain performance. |
Biomedical Concept Normalization over Nested Entities with Partial UMLS Terminology in Russian (2024.lrec-main)
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| Challenge: | Existing annotations in Russian do not include all entities, but only a small fraction of them are labeled in English. |
| Approach: | They present a manually annotated PubMed abstract dataset for concept normalization in Russian. |
| Outcome: | The proposed model improves on nested named entities in a zero-shot setting on bilingual terminology. |
Biomedical Entity Linking as Multiple Choice Question Answering (2024.lrec-main)
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| Challenge: | Existing methods for biomedical entity linking are discriminative and disambiguative . Existing models for bioMEDical entity linking use a BERT-based encoder to encode mentions and entities into the same embedding space and dissociate mentions by nearest neighbors. |
| Approach: | They propose a model that treats biomedical entity linking as Multiple Choice Question Answering. |
| Outcome: | The proposed model outperforms state-of-the-art models on several datasets. |
Bits and Pieces: Investigating the Effects of Subwords in Multi-task Parsing across Languages and Domains (2024.lrec-main)
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| Challenge: | Neural parsing is dependent on the underlying language model, but little is known about how choices affect parser performance. |
| Approach: | They examine how subword sharing is responsible for gains or negative transfer in multi-task learning . they find a preference for averaged or last subwords across languages and domains . |
| Outcome: | The proposed model favors averaged or last subwords across languages and domains . specific POS tags may require different subword, and distribution overlap is more important than discrepancies in the data sizes. |
BiVert: Bidirectional Vocabulary Evaluation Using Relations for Machine Translation (2024.lrec-main)
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| Challenge: | Neural machine translation (NMT) evaluation is crucial to determine the quality of translations. |
| Approach: | They propose a bidirectional semantic-based evaluation method to assess the sense distance of the translation from the source text. |
| Outcome: | The proposed method uses the multilingual encyclopedic dictionary BabelNet . it shows a strong correlation between the evaluation scores and human assessments . |
BKEE: Pioneering Event Extraction in the Vietnamese Language (2024.lrec-main)
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| Challenge: | Event Extraction (EE) is a fundamental task in information extraction. |
| Approach: | They propose a Vietnamese event extraction dataset that includes 33 different event types and 28 different event argument roles. |
| Outcome: | The proposed dataset provides a labeled dataset for entity mentions, event mentions and event arguments on 1066 documents. |
BlendX: Complex Multi-Intent Detection with Blended Patterns (2024.lrec-main)
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| Challenge: | Existing datasets such as MixATIS and MixSNIPS have limitations in their formulation. |
| Approach: | They propose a set of multi-intent detection datasets that feature more diverse patterns than their predecessors. |
| Outcome: | The proposed datasets feature more diverse patterns than their predecessors and are more complex and diverse than existing datasets. |
BLN600: A Parallel Corpus of Machine/Human Transcribed Nineteenth Century Newspaper Texts (2024.lrec-main)
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| Challenge: | Historical documents present unique challenges to automated digital transcription technologies, such as optical character recognition (OCR). |
| Approach: | They propose to use a publicly available nineteenth-century newspaper corpus to train and develop OCR and post-OCR correction methodologies for historical newspaper machine transcription. |
| Outcome: | The proposed corpus will be useful for training and development of OCR and post-OCR correction methodologies for historical newspaper machine transcription. |
Bootstrapping UMR Annotations for Arapaho from Language Documentation Resources (2024.lrec-main)
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| Challenge: | Uniform Meaning Representation (UMR) is a graph-based semantic labeling system . it is based on the AMR family and is designed to be uniformly applicable to typologically diverse languages. |
| Approach: | They propose methods for bootstrapping UMR annotations for a given language from existing resources and typical language documentation products. |
| Outcome: | The proposed method generates enough basic structure in UMR graphs to automate labeling to a significant extent. |
BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses (2024.lrec-main)
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| Challenge: | Existing pre-trained language models lack diversity and linguistic challenges in task-oriented dialogues. |
| Approach: | They propose a self-bootstrapping dialogue pre-training model called BootTOD . it learns task-oriented dialogue representations via a framework . |
| Outcome: | The proposed model outperforms strong TOD baselines on diverse dialogue tasks. |
Born a BabyNet with Hierarchical Parental Supervision for End-to-End Text Image Machine Translation (2024.lrec-main)
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| Challenge: | Existing research on text image machine translation (TIMT) is divided into two types: Cascade methods combine text image recognition and MT models to recognize source language text images. |
| Approach: | They propose a method which is optimized with hierarchical parental supervision to improve translation performance. |
| Outcome: | The proposed method significantly outperforms existing methods on synthetic and real-world tests on both synthetic and realistic images. |
BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation (2024.lrec-main)
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| Challenge: | Medical dialogue generation (MDG) has gained increasing attention due to its substantial practical value. |
| Approach: | They propose a method which explicitly models MDG’s multi-step reasoning process and iteratively enhances this reasoning process. |
| Outcome: | The proposed method outperforms state-of-the-art methods across objective and subjective evaluations on two publicly available datasets. |
Breakthrough from Nuance and Inconsistency: Enhancing Multimodal Sarcasm Detection with Context-Aware Self-Attention Fusion and Word Weight Calculation. (2024.lrec-main)
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| Challenge: | Existing methods for sarcasm detection rely on feature concatenation to fuse different modalities or model inconsistencies among modalités. |
| Approach: | They propose to use Context-Aware Self-Attention Fusion to integrate local and momentary multimodal information into specific words to illustrate the inconsistencies between connotation and denotation. |
| Outcome: | The proposed method achieves an accuracy of 76.9 and an F1 score of 76.1 on the MUStARD dataset, surpassing the current state-of-the-art IWAN model by 1.7 and 1.6 respectively. |
Bridging Computational Lexicography and Corpus Linguistics: A Query Extension for OntoLex-FrAC (2024.lrec-main)
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| Challenge: | OntoLex is the dominant community standard for machine-readable lexical resources . it is currently extended with a designated module for Frequency, Attestations and Corpus-based Information . |
| Approach: | They propose a module for Frequency, Attestations and Corpus-based Information for OntoLex . the module enables RDF-based web services to exchange corpus queries dynamically . |
| Outcome: | The proposed module addresses the incorporation of corpus queries for linking dictionaries with corpus engines and enabling RDF-based web services to exchange corpus query data dynamically. |
Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model (2024.lrec-main)
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| Challenge: | Existing fact-checking systems rely on extensive preprocessing and rule-based transformations, leading to potential context loss or misleading encodings. |
| Approach: | They propose a simple yet powerful model that nullifies the need for modality conversion, thereby preserving the original evidence’s context. |
| Outcome: | The proposed model nullifies the need for modality conversion, preserving the original evidence’s context. |
Bridging the Code Gap: A Joint Learning Framework across Medical Coding Systems (2024.lrec-main)
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| Challenge: | Existing methods for automating medical coding focus on a single coding system . however, there are still challenges to overcome in coding. |
| Approach: | They propose a joint learning framework for Across Medical coding systems which jointly learns different coding system through multi-task learning. |
| Outcome: | The proposed framework improves the performance of the MIMIC-IV ICD-9 and MIMICIV I CD-10 datasets. |
Bring Invariant to Variant: A Contrastive Prompt-based Framework for Temporal Knowledge Graph Forecasting (2024.lrec-main)
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| Challenge: | Existing methods for temporal knowledge graph forecasting are insufficient structural contexts to learn effective representations. |
| Approach: | They propose a Contrastive Prompt-based framework with Entity background information for TKG forecasting that brings time-invariant entity background information to time-variant structural information. |
| Outcome: | The proposed framework is effective and stays competitive in inference with limited structural information. |
Building a Broad Infrastructure for Uniform Meaning Representations (2024.lrec-main)
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Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, Jens E. L. Van Gysel, Meagan Vigus, Nianwen Xue, Jin Zhao
| Challenge: | This paper reports the first release of the UMR data set for six languages . it includes annotations for six different languages that vary greatly in terms of their linguistic properties and resource availability. |
| Approach: | They report the first release of the UMR data set for six languages . they describe on-going efforts to enlarge the data set and extend it to other languages - including Navajo, Navájo, and Sanapaná . |
| Outcome: | The first release of the UMR data set includes annotations for six languages . the language dataset is available for free and can be extended to other languages if needed . |
Building a Database of Conversational Routines (2024.lrec-main)
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| Challenge: | The Routinicon is a constructicographic resource for the description of conversational routines in Russian language. |
| Approach: | They propose to use the Routinicon to collect and systematically describe conversational routines in Russian language. |
| Outcome: | The proposed resource is a natural extension of the Russian Constructicon and Pragmaticon projects. |
Building a Data Infrastructure for a Mid-Resource Language: The Case of Catalan (2024.lrec-main)
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Aitor Gonzalez-Agirre, Montserrat Marimon, Carlos Rodriguez-Penagos, Javier Aula-Blasco, Irene Baucells, Carme Armentano-Oller, Jorge Palomar-Giner, Baybars Kulebi, Marta Villegas
| Challenge: | Aina Project aims to provide Catalan with the resources needed to keep its relevance in AI/NLP applications. |
| Approach: | They propose a set of strategies to consider when improving technology support for a mid- or low-resource language . they propose annotated datasets and a framework to make models ready to use . |
| Outcome: | The Aina Project aims to provide Catalan with the necessary resources to keep its relevance in AI/NLP-related industry and research. |
Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer (2024.lrec-main)
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| Challenge: | Document-level Relation Extraction (DocRE) is the task of extracting all semantic relationships from a document. |
| Approach: | They propose to transfer an English document to Japanese to promote DocRE in other languages. |
| Outcome: | The proposed model reduces the human edit steps by 50% compared with the previous approach. |
Building MUSCLE, a Dataset for MUltilingual Semantic Classification of Links between Entities (2024.lrec-main)
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| Challenge: | In this paper we present a dataset for MUltilingual Lexical Relation Classification (LRC) systems with 27K pairs of universal concepts selected from Wikidata, a large and highly multilingual factual Knowledge Graph (KG). |
| Approach: | They propose a dataset for MUltilingual lexico-semantic Classification of Links between Entities using 27K pairs of universal concepts selected from Wikidata. |
| Outcome: | The proposed dataset bridges lexical and conceptual semantics, avoids linguistic memorization, is domain-balanced across entities, and enables enrichment and hierarchical information retrieval. |
Building Question-Answer Data Using Web Register Identification (2024.lrec-main)
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| Challenge: | Recent advances in web register (genre) identification have created a shortage of QA datasets for English and Finnish. |
| Approach: | They propose a machine learning-based method for extracting QA pairs from web-scale data using XLM-R and a multilingual CORE web register corpus . they then develop a NER-style token classifier to identify the QA text spans within these documents. |
| Outcome: | The proposed method is adaptable to any language given the availability of language models and extensive web data, but it is limited to English and Finnish. |
CAGK: Collaborative Aspect Graph Enhanced Knowledge-based Recommendation (2024.lrec-main)
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| Challenge: | Existing KG-based recommendations have low link rates, redundant knowledge in KG, and low ratings and negative aspect sentiment. |
| Approach: | They propose a model that integrates auxiliary information such as social networks, user or item attributes, images, contextual data, etc. |
| Outcome: | The proposed model improves on two widely used benchmark datasets, Amazon-book and Yelp2018. |
CALAMR: Component ALignment for Abstract Meaning Representation (2024.lrec-main)
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| Challenge: | Abstract meaning representation (AMR) graphs represent semantic structure in a syntactic independent way. |
| Approach: | They propose a method for graph alignment that can support summarization and evaluation. |
| Outcome: | The proposed method produces graphs that explain what is summarized through their alignments, which can be used to train graph based summarization learners. |
Calibrating LLM-Based Evaluator (2024.lrec-main)
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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Existing models for large language models lack the ability to calibrate their outputs towards human preference. |
| Approach: | They propose a multi-stage, gradient-free approach to calibrate an LLM-based evaluator toward human preference. |
| Outcome: | The proposed approach improves correlation with expert evaluation on multiple text quality evaluation datasets. |
CAM 2.0: End-to-End Open Domain Comparative Question Answering System (2024.lrec-main)
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Ahmad Shallouf, Hanna Herasimchyk, Mikhail Salnikov, Rudy Alexandro Garrido Veliz, Natia Mestvirishvili, Alexander Panchenko, Chris Biemann, Irina Nikishina
| Challenge: | Comparative Question Answering is a Natural Language Processing task that combines Question Answers and Argument Mining. |
| Approach: | They propose a system for answering comparative questions called CAM 2.0 and a public leaderboard called CompUGE that unifies existing datasets under a single easy-to-use evaluation suite. |
| Outcome: | The proposed system is compared with previous web-form-based systems . it features question identification, object and aspect labeling, stance classification, summarization . the proposed system has a user-friendly interface and is available for free on the web . |
CAMAL: A Novel Dataset for Multi-label Conversational Argument Move Analysis (2024.lrec-main)
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| Challenge: | Existing models that combine CNN and LSTM structures with speaker ID graphs improve the F1-score of our baseline models to detect speakers’ intents by a large margin. |
| Approach: | They propose a conversational multi-label corpus of teaching transcripts for Conversational Argument Move AnaLysis (CAMAL) the dataset includes 165 discussion transcripts facilitated by pre-service teachers and students . |
| Outcome: | The proposed model improves the F1-score of the baseline model to detect speakers’ intents by a large margin. |
Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic (2024.lrec-main)
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| Challenge: | Camel Morph MSA is the largest open-source Modern Standard Arabic morphological analyzer and generator. |
| Approach: | They present Camel Morph MSA, the largest open-source Arabic morphological analyzer and generator. |
| Outcome: | The analysis can produce 1.45B analyses and 535M unique diacritizations, almost an order of magnitude larger than SAMA on a 10B word corpus. |
CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data (2024.lrec-main)
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| Challenge: | Clinical data in hospitals are unstructured and therefore need to be extracted from medical reports to conduct clinical studies. |
| Approach: | They propose a dedicated French biomedical model based on a public French biomedicine dataset. |
| Outcome: | The proposed model improves 2.54 points of F1-score on biomedical named entity recognition tasks. |
CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese (2024.lrec-main)
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| Challenge: | Despite numerous efforts in ad text generation, the aspect of diversifying a text has received limited attention, particularly in non-English languages like Japanese. |
| Approach: | They present a dataset for ad text generation in Japanese using annotators to examine the capabilities of recent NLG models. |
| Outcome: | The proposed dataset includes 3,980 ad texts written by experts taking into account various aspects of ade appeals. |
Can Factual Statements Be Deceptive? The DeFaBel Corpus of Belief-based Deception (2024.lrec-main)
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| Challenge: | if a person firmly believes in a non-factual statement, there is no inherent intention to deceive. |
| Approach: | They propose to use the DeFaBel corpus to study the relationship between deception and factuality based on belief to generate arguments supporting statements . |
| Outcome: | The DeFaBel corpus contains 1031 texts in german, out of which 643 are deceptive and 388 are non-deceptive. |
Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles (2024.lrec-main)
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| Challenge: | Using large language models (LLMs) to detect propaganda from text is a challenge for the development of sophisticated models. |
| Approach: | They propose to use a large propaganda dataset to identify propagandistic content in text, visual, or multimodal languages to improve their models. |
| Outcome: | The proposed model performs better on a large propaganda dataset than the existing models on skewed datasets. |
Can Humans Identify Domains? (2024.lrec-main)
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Maria Barrett, Max Müller-Eberstein, Elisa Bassignana, Amalie Brogaard Pauli, Mike Zhang, Rob van der Goot
| Challenge: | Textual domain is a crucial property within the Natural Language Processing community due to its effects on downstream model performance. |
| Approach: | They examine the level of human disagreement and the relative difficulty of each annotation task by training classifiers to perform the same task. |
| Outcome: | The authors show that human proficiency in identifying related intrinsic textual properties is low and that disagreements are high. |
Can Language Models Learn Embeddings of Propositional Logic Assertions? (2024.lrec-main)
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| Challenge: | Existing methods for automating reasoning can no longer be used for natural language tasks. |
| Approach: | They propose to use transformer-based language models to reason about knowledge expressed in natural language rather than using LMs to perform reasoning directly. |
| Outcome: | The proposed approach is feasible to some extent, but lacks robustness. |
Can Large Language Models Automatically Score Proficiency of Written Essays? (2024.lrec-main)
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| Challenge: | Automated essay scoring (AES) is one of the earliest research problems in natural language processing. |
| Approach: | They propose to use large language models to analyze and score written essays using four different prompts. |
| Outcome: | The proposed models show comparable performance on four different prompts and a slight advantage over the state-of-the-art models. |
Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences (2024.lrec-main)
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| Challenge: | scholarly databases fail to aggregate, compare, contrast, and contextualize existing studies in service to a targeted research question. |
| Approach: | They propose to use large language models to discern evidence in support or refute of specific hypotheses based on abstracts. |
| Outcome: | The proposed method outperforms state-of-the-art methods and highlights opportunities for future research. |
Can Large Language Models Learn Translation Robustness from Noisy-Source In-context Demonstrations? (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have been used for machine translation, but their robustness remains a challenge, as they struggle to translate sentences in the presence of noise even when using similarity-based in-context learning methods. |
| Approach: | They propose a scheme for studying machine translation robustness on LLMs by using noisy-source demonstration examples. |
| Outcome: | The proposed model can learn robustness from noisy-source demonstration examples, thereby improving translation performance on noisy sentences. |
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning? (2024.lrec-main)
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| Challenge: | Existing models that pretrain for cross-lingual tasks do not improve cross-linguistic learning. |
| Approach: | They propose to employ machine translation as a continued training objective to enhance language representation learning by bridging multilingual pretraining and cross-lingual applications. |
| Outcome: | The proposed model performance is compared with existing models and their latent representations. |
Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs? (2024.lrec-main)
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| Challenge: | Multiple-choice questions (MCQs) are widely used in the evaluation of large language models (LLMs) however, there are concerns about whether MCQ can truly measure LLM’s capabilities. |
| Approach: | They propose to use multiple choice questions to evaluate large language models (LLMs) to assess their capabilities. |
| Outcome: | The proposed methods show that MCQs are less reliable than LFGQs in terms of expected calibration error. |
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought (2024.lrec-main)
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| Challenge: | Existing frameworks for guiding a language model in reasoning tasks are limited by their tendency to generate low-quality rationales that are repetitive and vacuous. |
| Approach: | They propose a framework that leverages a lightweight language model for guiding a black-box large LM in reasoning tasks. |
| Outcome: | The proposed framework outperforms baselines in answer prediction accuracy. |
Can We Identify Stance without Target Arguments? A Study for Rumour Stance Classification (2024.lrec-main)
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| Challenge: | Existing target-aware models underperform in cases where the context of the target is crucial. |
| Approach: | They propose a framework to enhance reasoning with the targets and propose 'target-aware' models without awareness of the target. |
| Outcome: | The proposed framework achieves state-of-the-art on two benchmark datasets. |
Can We Learn Question, Answer, and Distractors All from an Image? A New Task for Multiple-choice Visual Question Answering (2024.lrec-main)
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| Challenge: | Existing studies focus on generating QADs from image and question, but a novel task is needed to generate meaningful questions, correct answers, and challenging distractors. |
| Approach: | They propose a task to generate QADs from images and encode images together . they use contrastive learning to ensure consistency of QAD generated and tested . |
| Outcome: | Empirical evaluations on the benchmark dataset validate the performance of the proposed task. |
CARE: Co-Attention Network for Joint Entity and Relation Extraction (2024.lrec-main)
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| Challenge: | Existing joint entity and relation extraction methods suffer from feature confusion or inadequate interaction between the two subtasks. |
| Approach: | They propose a Co-Attention network for joint entity and relation extraction that adopts a parallel encoding strategy to learn separate representations for each subtask. |
| Outcome: | The proposed model outperforms existing models on three datasets . it uses a parallel encoding strategy to learn separate representations for each subtask . |
CareCorpus: A Corpus of Real-World Solution-Focused Caregiver Strategies for Personalized Pediatric Rehabilitation Service Design (2024.lrec-main)
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| Challenge: | Pediatric rehabilitation services focus on functional skills and participation, defined as attendance and involvement in home, school, and community activities. |
| Approach: | They propose to use a dataset of 780 real-world strategies written by caregivers to sort caregiver strategies for use in designing pediatric rehabilitation care plans. |
| Outcome: | The proposed model can be used to sort caregiver strategies for use in designing pediatric rehabilitation care plans. |
CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions (2024.lrec-main)
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| Challenge: | CASIMIR dataset contains multiple revisions of 15,646 scientific articles . authors question the relevance of current evaluation methods for text revision . |
| Approach: | They propose a textual resource on the revision step of the writing process of scientific articles. |
| Outcome: | The proposed dataset contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews. |
Categorial Grammar Induction with Stochastic Category Selection (2024.lrec-main)
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| Challenge: | categorial grammar inducers have been used to learn from raw data, but they use shortcuts to ensure branching behavior. |
| Approach: | They propose a grammar inducer that learns from raw data and does not rely on bias terms . they show a recall-homogeneity of 0.48 on a corpus of English child-directed speech . |
| Outcome: | The proposed model achieves a recall-homogeneity of 0.48 on a corpus of English child-directed speech . |
Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: A Case Study on Hateful Memes (2024.lrec-main)
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| Challenge: | Causal analyses define semantics, while gradient-based methods are essential to eXplainable AI (XAI), interpreting the model’s ‘black box’. |
| Approach: | They propose to integrate causal analysis and XAI to integrate a model's mechanisms into their analysis by integrating a dataset of hateful meme detection models. |
| Outcome: | The proposed model can detect hateful memes using intersectionality principles and summarized attention scores highlight distinct behaviors of three Transformer models. |
CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models (2024.lrec-main)
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| Challenge: | a dataset of Chinese large language models is used to measure societal biases . many studies have shown that LLMs exhibit harmful societal biased outputs despite human data . |
| Approach: | They present a Chinese Bias Benchmark dataset that includes over 100K questions constructed by human experts and generative language models. |
| Outcome: | The proposed dataset covers stereotypes and societal biases in 14 social dimensions related to Chinese culture and values. |
CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering (2024.lrec-main)
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| Challenge: | Recent advances in artificial intelligence highlight the potential of language models in psychological health support. |
| Approach: | They propose a method to enhance the precision and efficacy of psychological support through large language models. |
| Outcome: | The proposed model generates professional and structured responses in Chinese psychological health Q&A tasks, showcasing its practicality and quality. |
CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting (2024.lrec-main)
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Yuang Li, Yinglu Li, Min Zhang, Chang Su, Jiawei Yu, Mengyao Piao, Xiaosong Qiao, Miaomiao Ma, Yanqing Zhao, Hao Yang
| Challenge: | End-to-end automatic speech recognition systems struggle to recognize rare name entities such as personal names, organizations and terminologies that are not frequently encountered in the training data. |
| Approach: | They propose a convolutional neural network-based ASR system that performs open-vocabulary keyword-spotting before the decoder to match the features between the entities and the utterances. |
| Outcome: | The proposed system significantly improves mixed-error-rate (MER) and entity recall compared to the original Whisper model on three internal datasets and two publicly available datasets. |
CEPT: A Contrast-Enhanced Prompt-Tuning Framework for Emotion Recognition in Conversation (2024.lrec-main)
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| Challenge: | Emotion recognition in conversation research suffers from data imbalance and the presence of similar linguistic expressions for different emotions. |
| Approach: | They propose a Contrast-Enhanced Prompt-Tuning framework that transforms an ERC task into a Masked Language Modeling task and generates the emotion for each utterance in the conversation. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on all three benchmark datasets and excels in recognizing minority emotions. |
CE-VDG: Counterfactual Entropy-based Bias Reduction for Video-grounded Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing methods to reduce question-related bias in video-grounded dialogue generation (VDG) however, the dataset often contains inherent bias, which can cause VDG models to learn spurious correlations between questions and answers. |
| Approach: | They propose to extend the counterfactual reasoning from the information entropy perspective to the generative task, which can effectively reduce the question-related bias in the auto-regressive generation task. |
| Outcome: | The proposed method can reduce question-related bias in the auto-regressive generation task by using counterfactual entropy as an external loss. |
ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting (2024.lrec-main)
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| Challenge: | Existing CoT synthesis approaches focus on simpler reasoning tasks and result in inconsistent CoT prompts. |
| Approach: | They propose a framework for automatic generation of superior CoT prompts based on three major evolution strategies . they propose 'step-level debating' method where multiple debaters discuss each reasoning step to arrive at the correct answer. |
| Outcome: | The proposed framework can generate superior CoT prompts from a CoT dataset. |
ChainNet: Structured Metaphor and Metonymy in WordNet (2024.lrec-main)
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| Challenge: | In a typical lexicon, word senses are encoded as a list, without inter-sense relations. |
| Approach: | They propose a lexical resource which explicitly identifies the senses of a word's senses by expressing how they are derived from one another. |
| Outcome: | The proposed resource expresses how senses in the Open English Wordnet are derived from one another. |
Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations (2024.lrec-main)
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| Challenge: | Graph Neural Networks (GNNs) are used to train neural networks to detect fake news based on context-based methods. |
| Approach: | They propose to combine the two by applying pre-training of Graph Neural Networks (GNNs) in the domain of context-based fake news detection. |
| Outcome: | The proposed methods show that transfer learning does not lead to significant improvements over training a model from scratch in the domain of context-based fake news detection. |
Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes (2024.lrec-main)
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| Challenge: | Gender stereotypes are pervasive beliefs about individuals based on their gender that shape societal attitudes, behaviours, and even opportunities. |
| Approach: | They propose eleven strategies to automatically counteract gender stereotypes by generating gender-based counter-stereotypes from a questionnaire to male and female participants. |
| Outcome: | The proposed strategies were perceived as offensive and/or implausible by the raters . humour, perspective-taking, counter-examples, and empathy for the speaker were perceived to be less effective. |
Characteristic AI Agents via Large Language Models (2024.lrec-main)
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| Challenge: | Commercial products have been devoted to creating character-driven chatbots using large language models, but academic research in this area remains relatively scarce. |
| Approach: | They investigate the performance of LLMs in constructing characteristic AI agents by simulating real-life individuals across different settings. |
| Outcome: | The proposed benchmark compared LLMs with real-life individuals in different settings and includes evaluation metrics. |
Character-level Language Models for Abbreviation and Long-form Detection (2024.lrec-main)
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| Challenge: | Abbreviations and long forms are textual elements that are present in scientific communication . non-recognition of abbreviation and long form can lead to a negative impact on information retrieval . |
| Approach: | They propose to train and test language models for automatically identifying abbreviations and long forms . they use existing datasets annotated with abbrevations and their associated long forms to test them . |
| Outcome: | The proposed model can detect abbreviations and long forms on biomedical data . the proposed model improves on a previously untested dataset with biomedically-annotated datasets . |
Charles Translator: A Machine Translation System between Ukrainian and Czech (2024.lrec-main)
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Martin Popel, Lucie Polakova, Michal Novák, Jindřich Helcl, Jindřich Libovický, Pavel Straňák, Tomas Krabac, Jaroslava Hlavacova, Mariia Anisimova, Tereza Chlanova
| Challenge: | a system for translating between Ukrainian and Czech was developed in the spring of 2022 . the system was not available at the time in the required quality . |
| Approach: | They propose a machine translation system between Ukrainian and Czech to reduce the impact of the Russian-Ukrainian war on individuals and society. |
| Outcome: | The proposed system translates directly between Ukrainian and Czech, compared to other systems that use English as a pivot. |
Charting the Linguistic Landscape of Developing Writers: An Annotation Scheme for Enhancing Native Language Proficiency (2024.lrec-main)
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| Challenge: | An annotation task was designed to capture orthographic, grammatical, lexical, semantic, and discursive patterns exhibited by college native English speakers participating in developmental education (DevEd) courses. |
| Approach: | They propose an annotation task to capture orthographic, grammatical, lexical, semantic, and discursive patterns exhibited by college native English speakers participating in developmental education courses. |
| Outcome: | The proposed annotation task captures orthographic, grammatical, lexical, semantic, and discursive patterns exhibited by college native English speakers participating in developmental education courses. |
ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization (2024.lrec-main)
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| Challenge: | Existing methods for chart summarization lack visual-language matching and reasoning ability. |
| Approach: | They propose a method which synthesizes deep analysis based on chains of thought and strategies of context retrieval to improve the logical coherence and accuracy of the generated summaries. |
| Outcome: | The proposed method outperforms 8 state-of-the-art models over 7 evaluation metrics and can significantly reduce time and cognitive resources required. |
ChatASU: Evoking LLM’s Reflexion to Truly Understand Aspect Sentiment in Dialogues (2024.lrec-main)
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| Challenge: | Existing studies on interactive ASU ignore the coreference issue for opinion targets while this phenomenon is ubiquitous in interactive scenarios especially dialogues, limiting the ASU performance. |
| Approach: | They propose a Chat-based Aspect Sentiment Understanding task that integrates various NLP tasks with the chat paradigm and propose 'trusted self-reflexion' approach with ChatGLM as backbone to address aspect coreference issue. |
| Outcome: | The proposed task outperforms state-of-the-art baselines and shows that it is highly effective. |
ChatEL: Entity Linking with Chatbots (2024.lrec-main)
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| Challenge: | Entity Linking (EL) is a challenging task in natural language processing . existing approaches focus on creating elaborate contextual models that are unwieldy and difficult to train . |
| Approach: | They propose a framework to prompt LLMs to return accurate results for Entity Linking . they use a three-step framework to generate a set of EL models that can be open-source . |
| Outcome: | The proposed framework improves the average F1 performance across 10 datasets by more than 2%. |
ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models (2024.lrec-main)
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| Challenge: | acquiring and representing commonsense in machines has posed a long-standing challenge (Li et al., 2021; Zhang e t al, 2022; Zhou e al. 2023) . |
| Approach: | They use a commonsense-based LLM to evaluate ChatGPT's commonsensing abilities by analyzing 11 datasets and generating knowledge descriptions. |
| Outcome: | The proposed model can achieve good QA accuracies while still struggling with certain domains of datasets. |
ChatGPT Rates Natural Language Explanation Quality like Humans: But on Which Scales? (2024.lrec-main)
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| Challenge: | Traditionally, evaluating NLEs through gathering human judgments is a tedious task due to the subjective nature of human evaluations. |
| Approach: | They examine the alignment between ChatGPT and human assessments across multiple scales and compare them using paired comparisons and dynamic prompting. |
| Outcome: | The proposed model aligns better with humans in coarser scales and provides semantically similar examples in the prompt. |
ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness (2024.lrec-main)
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| Challenge: | Recent advances in interactive large language models like ChatGPT have revolutionized various domains, but their behavior in natural and role-play settings remains underexplored. |
| Approach: | They analyze ChatGPT interactions in a normal way and a role-play setting to examine its behavior in conversational settings. |
| Outcome: | The proposed dataset shows that chatGPT behaves in natural and role-play settings with different user motives and model naturalness. |
ChatUIE: Exploring Chat-based Unified Information Extraction Using Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models have shown impressive performance in general chat, but their domain-specific capabilities have certain limitations. |
| Approach: | They propose a unified information extraction framework built upon ChatGLM that incorporates domain-specific modeling to extract structured information from natural language. |
| Outcome: | The proposed framework significantly improves the performance of information extraction tasks with a slight decrease in chatting ability. |
CHICA: A Developmental Corpus of Child-Caregiver’s Face-to-face vs. Video Call Conversations in Middle Childhood (2024.lrec-main)
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Dhia Elhak Goumri, Abhishek Agrawal, Mitja Nikolaus, Hong Duc Thang Vu, Kübra Bodur, Elias Emmar, Cassandre Armand, Chiara Mazzocconi, Shreejata Gupta, Laurent Prévot, Benoit Favre, Leonor Becerra-Bonache, Abdellah Fourtassi
| Challenge: | Existing studies of language-in-interaction focus on the two ends of the developmental spectrum, i.e., early childhood and adulthood, leaving a gap in our knowledge about how development unfolds, especially across middle childhood. |
| Approach: | They propose to use CHICA to analyze child-caregiver conversations at home . they use mobile, lightweight eye-tracking and head motion detection to optimize the naturalness of the recordings. |
| Outcome: | The proposed corpus of child-caregiver conversations at home was compared with a previous corpus based on a set of conversations between children aged 7, 9, and 11 years old. |
Chinese Morpheme-informed Evaluation of Large Language Models (2024.lrec-main)
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| Challenge: | Existing evaluations of large language models focused on the perspective of various tasks or abilities. |
| Approach: | They propose to evaluate large language models from a linguistic perspective and use morpheme to measure morphology and syntax. |
| Outcome: | The proposed model outperforms ChatGPT in Chinese scenarios with a morpheme-informed benchmark and human exam questions. |
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training (2024.lrec-main)
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| Challenge: | Pretrained language models (PLMs) have been successful in addressing word boundaries in Chinese sequence labeling tasks, but they rarely consider boundary information explicitly. |
| Approach: | They propose a method to integrate unsupervised boundary information into Chinese BERT's pre-training objectives and a supervised boundary-aware PLM. |
| Outcome: | The proposed model outperforms the vanilla version on Chinese sequence labeling tasks and in broader Chinese natural language understanding tasks. |
CHisIEC: An Information Extraction Corpus for Ancient Chinese History (2024.lrec-main)
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| Challenge: | Historical and cultural heritage preservation is an important branch of digital humanities, where the rich tapestry of the past meets the cutting-edge tools of the digital age. |
| Approach: | They present a dataset to evaluate NER and RE tasks in ancient Chinese history . they use four distinct entity types and twelve relation types to identify them . |
| Outcome: | The "Chinese Historical Information Extraction Corpus" is a dataset from 13 dynasties spanning over 1830 years . the dataset encompasses four distinct entity types and twelve relation types . |
Chitchat as Interference: Adding User Backstories to Task-Oriented Dialogues (2024.lrec-main)
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| Challenge: | During task-oriented dialogues, human users naturally introduce chitchat that is beyond the immediate scope of the task, interfering with the flow of the conversation. |
| Approach: | They use few-shot prompting to augment a multi-task chitchat dataset with user backstories to address this issue without the need for expensive manual data creation. |
| Outcome: | The proposed model can be used for training and can move the task forward in the same turn, as confirmed by human evaluation. |
Choice-75: A Dataset on Decision Branching in Script Learning (2024.lrec-main)
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| Challenge: | Existing studies only consider scripts as linear developments of events, ignoring the potential branches that arise due to people’s circumstantial choices. |
| Approach: | They propose a benchmark that challenges intelligent systems to make decisions given descriptive scenarios, containing 75 scripts and more than 600 scenarios. |
| Outcome: | The proposed benchmarks show that they perform well in hard scenarios, but there is still significant headroom in hard ones. |
C-Journal: A Journaling Application for Detecting and Classifying Cognitive Distortions Using Deep-Learning Based on a Crowd-sourced Dataset (2024.lrec-main)
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| Challenge: | Cognitive distortions are negatively biased thinking patterns and erroneous self-statements resulting from and leading to logical errors in one’s internal reasoning. |
| Approach: | They propose to provide an open-source labeled dataset of 14 cognitive distortions consisting of 34370 entries collected via crowd-sourcing, user questionnaires, and re-purposing emotions dataset from social media. |
| Outcome: | The proposed model was built into a free journaling and mood-tracking mobile application that pinpoints cognitive distortions to the users. |
Claim-Centric and Sentiment Guided Graph Attention Network for Rumour Detection (2024.lrec-main)
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| Challenge: | Social media exerts a substantial influence on individuals' day-to-day existence, a new study shows . the rapid propagation of false information and fake news is a critical aspect of rumour detection . |
| Approach: | They propose a model that takes into account the claim in the source tweet and includes tweet sentiment along with the propagation graph. |
| Outcome: | The proposed model outperforms existing models and improves on sentiment labels. |
Clarifying Completions: Evaluating How LLMs Respond to Incomplete Questions (2024.lrec-main)
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| Challenge: | iCRs are a complex phenomenon with different forms, readings and functions and can occur on different levels of communication on Clark's 'Clark's' . they are often multi-modal and can be generated on different level of communication. |
| Approach: | They collect, release and analyse a corpus of 3000 human produced iCRs and use them to probe the incremental processing capability of state of the art LLMs. |
| Outcome: | The proposed model can generate contextually appropriate iCRs at larger LLM sizes and only when prompted with examples from the corpus. |
Classifying Social Media Users before and after Depression Diagnosis via Their Language Usage: A Dataset and Study (2024.lrec-main)
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| Challenge: | Mental illness can negatively impact individuals’ quality of life as it is considered one of the causes of years lived with disability and it is related to high suicide rates. |
| Approach: | They collect first dataset of textual posts by same users before and after being diagnosed with depression and build multiple predictive models based on Transformers and BERT. |
| Outcome: | The proposed model can be used to detect depression and suicidal thoughts in users who are not diagnosed with depression or suicide. |
Class-Incremental Few-Shot Event Detection (2024.lrec-main)
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| Challenge: | Existing methods to deal with new class of events with only a few labeled instances are challenging . old knowledge forgetting and new class overfitting are two problems in this task. |
| Approach: | They propose a task called class-incremental few-shot event detection to solve old knowledge forgetting and new class overfitting problems. |
| Outcome: | The proposed method reduces old knowledge forgetting and new class overfitting problems on two benchmark datasets. |
CLASSLA-web: Comparable Web Corpora of South Slavic Languages Enriched with Linguistic and Genre Annotation (2024.lrec-main)
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| Challenge: | Using a similar crawling setup, the corpora are comparable across the entire South Slavic language space. |
| Approach: | They propose to collect 13 billion tokens of texts from 26 million documents . they are linguistically annotated with a CLASSLA-Stanza pipeline and enriched with document-level genre information via a Transformer-based multilingual classifier. |
| Outcome: | The corpora are linguistically annotated with the state-of-the-art CLASSLA-Stanza linguistic processing pipeline and enriched with document-level genre information via the Transformer-based multilingual X-GENRE classifier. |
CLAUSE-ATLAS: A Corpus of Narrative Information to Scale up Computational Literary Analysis (2024.lrec-main)
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| Challenge: | XIX and XX century English novels annotated automatically contain 41,715 labeled clauses . a new approach to analyze novels based on clauses captures structural patterns within books, as well as qualitative differences between them. |
| Approach: | They propose to use a corpus of XIX and XX century English novels annotated automatically to study stories as sequences of eventive, subjective and contextual information. |
| Outcome: | The proposed method captures structural patterns within books, as well as qualitative differences between them. |
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments (2024.lrec-main)
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| Challenge: | Existing knowledge for reasoning about partially observed scenes is limited . lucian et al. show that pre-trained vision language models are not adequate for VQA . |
| Approach: | They propose a benchmark for reasoning-intensive visual question answering . they use logical constraints to leverage knowledge to generate plausible answers . |
| Outcome: | The proposed model performs better than pre-trained models on CLEVR-POC . the proposed model is based on a neuro-symbolic model with a visual perception network and a formal logical reasoner . |
CLFFRD: Curriculum Learning and Fine-grained Fusion for Multimodal Rumor Detection (2024.lrec-main)
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| Challenge: | Existing multimodal rumor detection models overlook sample difficulty and order when training . Existing models overlook text-level difficulty, image-level and multimodal difficulty when training samples . |
| Approach: | They propose a curriculum learning framework that uses fine-grained fusion to detect rumors . they propose fusion-based methods that combine text and images to enhance semantic cohesion . |
| Outcome: | The proposed framework outperforms state-of-the-art models on English and Chinese benchmark datasets. |
CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)
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Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks. |
| Approach: | They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process. |
| Outcome: | The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless" |
CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean (2024.lrec-main)
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| Challenge: | Existing benchmark datasets for Korean cultural and linguistic knowledge are derived from the English counterparts through translation, so they overlook cultural contexts. |
| Approach: | They propose to use Korean cultural and linguistic intelligence to assess Korean model performance by providing fine-grained annotations of cultural and cultural knowledge. |
| Outcome: | The proposed dataset includes 1,995 QA pairs and is based on 1,992 Korean exams and textbooks. |
Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles (2024.lrec-main)
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| Challenge: | Educational crosswords are characterized by less cryptic and more factual clues than traditional puzzles. |
| Approach: | They propose to use a dataset to generate educational clues for Large Language Models (LLMs) they use Wikipedia to gather information associated with relevant keywords and use it to generate clues. |
| Outcome: | The proposed approach generates educational clues from a dataset containing 44,075 examples with text-keyword pairs associated with three distinct crossword clues. |
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation (2024.lrec-main)
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| Challenge: | Metaphors are a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. |
| Approach: | They propose a large-scale high quality annotated Chinese Metaphor Corpus . they use a set of guidelines to ensure the accuracy and consistency of their annotations . |
| Outcome: | The proposed corpus generates metaphors that resonate more with real-world intuition. |
CMNEE:A Large-Scale Document-Level Event Extraction Dataset Based on Open-Source Chinese Military News (2024.lrec-main)
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| Challenge: | Current research focuses on the general news or financial domains, with relatively few studies for military domain. |
| Approach: | They propose to annotate Chinese military news events from documents using a schema for the military domain. |
| Outcome: | The proposed dataset is large-scale, document-level open-source for the military domain . it contains 17,000 documents and 29,223 events, which are all manually annotated . |
CM-Off-Meme: Code-Mixed Hindi-English Offensive Meme Detection with Multi-Task Learning by Leveraging Contextual Knowledge (2024.lrec-main)
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| Challenge: | Existing studies on detecting offensive memes have focused on identifying them as implicit and explicit . detecting memes requires contextual knowledge, but there is no such dataset for the code-mixed Hindi-English domain. |
| Approach: | They propose an end-to-end multitask model that integrates contextual knowledge and psycho-linguistic knowledge to detect offensive memes. |
| Outcome: | The proposed model is able to detect offensive memes and explicit memes in a large-scale dataset. |
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite (2024.lrec-main)
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| Challenge: | Recent advances in conversational IR systems have seen a resurgent interest in conversation . generative query rewrite generates reconstructed query based on the conversation history . |
| Approach: | They propose to use unlabeled data to make further improvements using contrastive co-training paradigm. |
| Outcome: | The proposed model is robust to noise and language style shift under few-shot and zero-shot scenarios. |
CoANZSE Audio: Creation of an Online Corpus for Linguistic and Phonetic Analysis of Australian and New Zealand Englishes (2024.lrec-main)
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| Challenge: | CoANZSE Audio is a searchable online corpus of 195 million words of geo-located YouTube transcripts of local government channels. |
| Approach: | They describe the methods used to create the corpus from open-source tools and the architecture of the CoANZSE Audio website. |
| Outcome: | The corpus contains 195-million-word transcripts of local government channels . it is one of the first large, free, fully searchable online corpora containing data suitable for acoustic phonetic analyses in addition to lexical, grammatical, and discourse properties of Australian and New Zealand Englishes. |
Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Existing fine-tuning techniques for information retrieval systems require learning query representations and query-document relations. |
| Approach: | They propose a method that bridges pre-training and fine-tuning by learning query representations and query-document relations in coarse-tuned models. |
| Outcome: | The proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. |
CoBaLD Annotation: The Enrichment of the Enhanced Universal Dependencies with the Semantical Pattern (2024.lrec-main)
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| Challenge: | a new annotation format is developed to support morphological, syntactic and semantic markup . the format is based on the Compreno semantics, which is a simplified version of the standard . |
| Approach: | They propose a new annotation format that combines Enhanced UD morphosyntax and Compreno semantic pattern to enrich the UD annotation with word meanings and labels for semantic relations between words. |
| Outcome: | The proposed format is aimed at morphological, syntactic and especially semantic markup . the proposed format reduces the number of semantic fields denoting lexical meanings . |
CoCoMIC: Code Completion by Jointly Modeling In-file and Cross-file Context (2024.lrec-main)
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Yangruibo Ding, Zijian Wang, Wasi U. Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, Bing Xiang
| Challenge: | Pre-trained language models (LMs) for code have shown promising performance in code completion tasks but ignore the rich semantics in other files within the same project. |
| Approach: | They propose a framework that jointly learns the in-file and cross-file context on top of code LMs and a static-analysis-based tool that locates and retrieves the most relevant project-level cross- file context for code completion. |
| Outcome: | The proposed framework improves existing code LMs with a 33.94% relative increase in exact match and 28.69% in identifier matching when the cross-file context is provided. |
Code Defect Detection Using Pre-trained Language Models with Encoder-Decoder via Line-Level Defect Localization (2024.lrec-main)
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| Challenge: | Recent code Pre-trained Language Models (PLMs) have shown great success in code defect detection tasks. |
| Approach: | They propose a method that integrates line-level defect localization into a unified training process to identify which lines contain defects. |
| Outcome: | The proposed method significantly improves performance on four benchmark datasets for code defect detection. |
Code-Mixed Probes Show How Pre-Trained Models Generalise on Code-Switched Text (2024.lrec-main)
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| Challenge: | Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. |
| Approach: | They propose to use pre-trained language models to generalise to code-switched text . they use a dataset of well-formed naturalistic code-witched texts and parallel translations into the source languages to examine their results. |
| Outcome: | The proposed model generalises to code-switched text, shedding light on their ability to generalise representations to CS corpora. |
Code-Mixed Text Augmentation for Latvian ASR (2024.lrec-main)
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| Challenge: | a new study attempts to tackle code-mixed speech recognition by improving the language model of a hybrid system. |
| Approach: | They propose an inflected transliteration and phonetic transcription model for code-mixed Latvian sentences . they leverage a large human-translated English-Latvian parallel text corpus to generate synthetic Latvian phrases . |
| Outcome: | The proposed system improves on a human-translated English-Latvian parallel text corpus . the results show that the proposed system can generate code-mixed Latvian sentences . |
Cognitive Information Bottleneck: Extracting Minimal Sufficient Cognitive Language Processing Signals (2024.lrec-main)
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| Challenge: | Existing methods to extract only task-relevant information from cognitive processing signals are lacking in the field of NLP. |
| Approach: | They propose a method that extracts only task-relevant information from cognitive processing signals. |
| Outcome: | The proposed method outperforms existing methods in compressing cognitive signals and enhances performance on downstream tasks. |
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)
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| Challenge: | Existing IE tools lack multi-task support and automatic updates for KG and EKG construction. |
| Approach: | They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine. |
| Outcome: | The proposed tool improves annotation quality, efficiency, and stability simultaneously. |
Collecting and Analyzing Dialogues in a Tagline Co-Writing Task (2024.lrec-main)
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| Challenge: | Currently, most studies on dialogue systems focus on problemsolving dialogues and relatively little research has been done on systems that can engage in creative collaboration with users. |
| Approach: | They designed a tagline co-writing task in which two people collaborate to create taglines via text chat and collected dialogue logs, editing logs and questionnaire results. |
| Outcome: | The proposed task involved a tagline co-writing task in which two people collaborate to create taglines via text chat, and collected dialogue logs, editing logs and questionnaire results. |
Collecting Human-Agent Dialogue Dataset with Frontal Brain Signal toward Capturing Unexpressed Sentiment (2024.lrec-main)
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| Challenge: | Multimodal information such as text and audiovisual data has been used for emotion/sentiment estimation during human-agent dialogues. |
| Approach: | They present a method for dealing with eye-blink noise for frontal EEGs denoising. |
| Outcome: | The proposed method improves sentiment estimation performance when used with other modalities by multimodal fusion, although it only has three channels. |
Collecting Linguistic Resources for Assessing Children’s Pronunciation of Nordic Languages (2024.lrec-main)
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Anne Marte Haug Olstad, Anna Smolander, Sofia Strömbergsson, Sari Ylinen, Minna Lehtonen, Mikko Kurimo, Yaroslav Getman, Tamás Grósz, Xinwei Cao, Torbjørn Svendsen, Giampiero Salvi
| Challenge: | Using annotated corpora of languages is difficult for children learning a foreign language . most effort is directed to the most popular languages and adult learners . |
| Approach: | They collect annotated corpora of languages spoken by children in three Nordic countries . they hope to make the data available for future research . |
| Outcome: | The collected data will be used to develop and evaluate computer assisted pronunciation assessment systems for non-native children learning a Nordic language (L2) and for L1 children with speech sound disorder (SSD). |
Combining Discourse Coherence with Large Language Models for More Inclusive, Equitable, and Robust Task-Oriented Dialogue (2024.lrec-main)
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| Challenge: | Large language models (LLMs) are capable of generating well-formed responses, but they struggle in goal-oriented settings. |
| Approach: | They propose a discourse-aware multimodal task-oriented dialogue system that combines discourse theories with offline LLM generation. |
| Outcome: | The proposed system reduces misunderstandings in the dialect of African-American Vernacular English from 93% to 57%. |
COMET for Low-Resource Machine Translation Evaluation: A Case Study of English-Maltese and Spanish-Basque (2024.lrec-main)
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| Challenge: | Trainable metrics for machine translation evaluation have been scoring the highest correlations with human judgements in the meta-evaluations. |
| Approach: | They run a crowd-based evaluation campaign to evaluate COMET-22 and fine-tune it to improve its performance. |
| Outcome: | The proposed system outperforms BLEU and other lexical overlap metrics in the meta-evaluations. |
COMICORDA: Dialogue Act Recognition in Comic Books (2024.lrec-main)
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| Challenge: | Existing work on dialogue act recognition from images is limited to speech balloon segmentation and optical character recognition. |
| Approach: | They propose a novel DA recognition approach for comic books using speech balloon segmentation, optical character recognition and DA classification. |
| Outcome: | The proposed method achieves 98% average precision for speech balloon segmentation and exceeds 70% accuracy for the DA recognition task. |
Common European Language Data Space (2024.lrec-main)
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Georg Rehm, Stelios Piperidis, Khalid Choukri, Andrejs Vasiļjevs, Katrin Marheinecke, Victoria Arranz, Aivars Bērziņš, Miltos Deligiannis, Dimitris Galanis, Maria Giagkou, Katerina Gkirtzou, Dimitris Gkoumas, Annika Grützner-Zahn, Athanasia Kolovou, Penny Labropoulou, Andis Lagzdiņš, Elena Leitner, Valérie Mapelli, Hélène Mazo, Simon Ostermann, Stefania Racioppa, Mickaël Rigault, Leon Voukoutis
| Challenge: | the Common European Language Data Space (LDS) is an integral part of the EU data strategy, which aims at developing a single market for data. |
| Approach: | the Common European Language Data Space (LDS) is an integral part of the EU data strategy . its decentralised technical infrastructure and governance scheme are currently being developed by the LDS project . |
| Outcome: | the Common European Language Data Space (LDS) is an integral part of the EU data strategy, which aims at developing a single market for data. |
Common Ground Tracking in Multimodal Dialogue (2024.lrec-main)
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Ibrahim Khalil Khebour, Kenneth Lai, Mariah Bradford, Yifan Zhu, Richard A. Brutti, Christopher Tam, Jingxuan Tu, Benjamin A. Ibarra, Nathaniel Blanchard, Nikhil Krishnaswamy, James Pustejovsky
| Challenge: | In dialogue modeling, there is considerable attention on “dialogue state tracking” (DST) but “common ground tracking” identifies the shared belief space held by all participants in a task-oriented dialogue: the task-relevant propositions all participants accept as true. |
| Approach: | They propose a method for automatically identifying the current set of shared beliefs and ”questions under discussion” of a group with a shared goal. |
| Outcome: | The proposed method predicts moves toward building common ground relative to ground truth in a multimodal interaction with an AI. |
Comparative Analysis of Sign Language Interpreting Agents Perception: A Study of the Deaf (2024.lrec-main)
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| Challenge: | Prior research on sign language recognition has shown encouraging outcomes in achieving highly accurate and dependable automatic sign language generation. |
| Approach: | They propose to compare a state-of-the-art sign language generation system with a skilled sign language interpreter to gain insights into usability of such metrics for deaf signers. |
| Outcome: | The proposed system is compared with a skilled interpreter to gain insights into usability of such metrics for deaf signers and how deafic signers perceive signing agents. |
Comparing Static and Contextual Distributional Semantic Models on Intrinsic Tasks: An Evaluation on Mandarin Chinese Datasets (2024.lrec-main)
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| Challenge: | Distributional Semantics has undergone significant changes with the introduction of contextualized distributional models. |
| Approach: | They compare static and contextual distributional models for Mandarin Chinese . they find that static models are stronger for some of the classical tasks . |
| Outcome: | The proposed models perform better on some of the classical tasks that consider word meaning independent of context, while contextualized models excel in identifying semantic relations between word pairs and categorization of words into abstract semantic classes. |
Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech Recognition (2024.lrec-main)
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| Challenge: | Recent advances have been achieved in Visual Speech Recognition (VSR) despite the lack of data, there is no clear comparison between different types of decoders for certain languages and tasks. |
| Approach: | They focused on how the conventional DNN-HMM decoder behaves depending on the amount of data used for their estimation. |
| Outcome: | The proposed model improves the CTC/Attention model in data-scarcity scenarios while requiring less training time and fewer parameters. |
Comparison of the Intimacy Process between Real and Acting-based Long-term Text Chats (2024.lrec-main)
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| Challenge: | Recent open-domain chatbots can generate natural responses over multiple turns using large-scale language models, but they do not address the speaker intimacy process and thus cannot sustain natural dialogue over multiple days and weeks. |
| Approach: | They propose to train systems with multi-session chat data to simulate relationship-building between speakers. |
| Outcome: | The proposed multi-session chat data can simulate relationship-building between speakers but has not been tested in Japanese. |
Complex Word Identification: A Comparative Study between ChatGPT and a Dedicated Model for This Task (2024.lrec-main)
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| Challenge: | Existing methods to assess lexical complexity are used to evaluate the difficulty of vocabulary for language learners. |
| Approach: | They propose to use pre-trained language models to assess the complexity of a word based on its context. |
| Outcome: | The proposed method outperforms the best systems in SemEval-2021. |
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding (2024.lrec-main)
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Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer, Kamyar Arzideh, Giulia Baldini, Jan Trienes, Max Hasin, Jeanette Bewersdorff, Cynthia S. Schmidt, Marie Bauer, Kaleb E. Smith, Jiang Bian, Yonghui Wu, Jörg Schlötterer, Torsten Zesch, Peter A. Horn, Christin Seifert, Felix Nensa, Jens Kleesiek, Christoph M. Friedrich
| Challenge: | Pre-trained language models can struggle in specialized domains such as medicine . existing generalpurpose pre-tried models can be used and refined through further pre-training on domainspecific unlabeled data. |
| Approach: | They pre-trained German medical language models on 2.4B tokens from translated public data and 3B token of German clinical data. |
| Outcome: | The proposed models outperform clinical models on various downstream tasks in germany . the authors show that continuous pre-training can match or exceed clinical models trained from scratch . |
Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases (2024.lrec-main)
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| Challenge: | linguists have suggested that some languages are "cooler" than others because of their contexts. |
| Approach: | They propose to omit plurality and definiteness markers in Chinese noun phrases . they build a corpus of Chinese NPs accompanied by its context . |
| Outcome: | The proposed model predicts the plurality and definiteness of Chinese noun phrases (NPs) it shows that speakers drop plurality markers very frequently, and that they are more likely to drop pronouns . |
CONAN-MT-SP: A Spanish Corpus for Counternarrative Using GPT Models (2024.lrec-main)
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María Estrella Vallecillo Rodríguez, Maria Victoria Cantero Romero, Isabel Cabrera De Castro, Arturo Montejo Ráez, María Teresa Martín Valdivia
| Challenge: | a new study evaluates the performance of GPT-based models to generate CNs for hate speech in Spanish . a growing number of social interactions through digital platforms have led to inappropriate behavior . |
| Approach: | They propose to use GPT-based models to generate CNs for Hate Speech in Spanish . they use the DeepL API to automatically translate the HS segment into Spanish based on the original CN pairs translated into spanish . |
| Outcome: | The proposed models outperform human models in most instances, the authors say . the results will be made available to the research community . |
Conceptual Pacts for Reference Resolution Using Small, Dynamically Constructed Language Models: A Study in Puzzle Building Dialogues (2024.lrec-main)
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| Challenge: | Existing large language models can be fine-tuned offline but are large and resource-intensive. |
| Approach: | They propose to use a simple reference resolver to simulate a conceptual pact process over time with different conversation pairs. |
| Outcome: | The proposed model performs better than a pre-trained model with exhaustive retraining after each prediction, while being more transparent, faster and less resource-intensive. |
ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition (2024.lrec-main)
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Ruizhe Huang, Mahsa Yarmohammadi, Jan Trmal, Jing Liu, Desh Raj, Leibny Paola Garcia, Alexei V. Ivanov, Patrick Ehlen, Mingzhi Yu, Dan Povey, Sanjeev Khudanpur
| Challenge: | Existing work on contextual speech recognition (ASR) systems focuses on recognizing words that are not frequently seen in training data, such as rare words, but word error rate on rare words remains over 20%. |
| Approach: | They propose to use public-domain earnings calls and supplementary materials to evaluate contextual ASR approaches grounded on real-world applications. |
| Outcome: | The proposed frameworks are noisier than artificially synthesized contexts that contain the ground truth, yet still make great room for future improvement of contextual ASR technology. |
Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing prompt-based NER models fail to detect entity boundaries, causing performance degradation. |
| Approach: | They propose a model which consists of a BART encoder and a parabiotic decoder and propose ' boundary expansion strategy' to enhance the model's capability in entity type classification. |
| Outcome: | The proposed model can achieve significant performance gains over state-of-the-art models. |
CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English (2024.lrec-main)
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| Challenge: | a glass ceiling for named entity recognition systems has been suggested for 2021 . however, the performance of the most popular NER benchmarks has plateaued since then . we investigate what NER models are still struggling with . |
| Approach: | They perform a fine-grained evaluation of the model outputs by adding document annotations to the CoNLL-03 English dataset to identify lingering errors. |
| Outcome: | The proposed model is able to correct errors and guide future work. |
Connecting Language Technologies with Rich, Diverse Data Sources Covering Thousands of Languages (2024.lrec-main)
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Daan van Esch, Sandy Ritchie, Sebastian Ruder, Julia Kreutzer, Clara Rivera, Ishank Saxena, Isaac Caswell
| Challenge: | Existing data sources for many thousands of languages are rich and diverse . Efforts are ongoing to extend technology to many more of the world's languages . |
| Approach: | They provide an overview of some of the major online data sources available for thousands of languages. |
| Outcome: | The proposed language technologies are based on the data available for thousands of languages. |
Constructing a Dependency Treebank for Second Language Learners of Korean (2024.lrec-main)
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| Challenge: | a manually annotated syntactic treebank is available for second language learners . the dataset includes 7,530 sentences (66,982 words; 129,333 morphemes) |
| Approach: | They propose to manually annotate syntactic treebanks based on Universal Dependencies from Korean written data. |
| Outcome: | The proposed dataset includes 7,530 sentences and 129,333 morphemes from Korean learners. |
Constructing Indonesian-English Travelogue Dataset (2024.lrec-main)
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| Challenge: | low-resource language research often hampered due to under-representation of how it is being used in reality. |
| Approach: | They propose to use a dataset comprising both Indonesian and English from personal travelogue articles . they used named and nominal expressions of four entity types related to travel . |
| Outcome: | The proposed dataset is more representative of how Indonesian language is being used in reality. |
Constructing Korean Learners’ L2 Speech Corpus of Seven Languages for Automatic Pronunciation Assessment (2024.lrec-main)
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| Challenge: | Multilingual L2 speech corpora for automatic speech assessment are currently available, but lack comprehensive annotations of L2 from non-native speakers of various languages. |
| Approach: | They propose to use Korean learners’ L2 speech corpus of seven languages to develop automatic speech assessment. |
| Outcome: | The proposed corpus contains 1,200 hours of L2 speech data from Korean learners (400 hours for English, 200 hours each for Japanese and Chinese, 100 hours each in French, German, Spanish, and Russian). |
Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation (2024.lrec-main)
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Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Joshua M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly
| Challenge: | Prior studies have shown that sequence-to-sequence models learn to hallucinate when the conditioning data has poor correlation with the sequence being produced. |
| Approach: | They construct a dataset that pairs Knowledge Graphs (KG) and text together and compare their results to a cyclic evaluation model. |
| Outcome: | The proposed model performs better on cyclic generation of KGs than on KG-T, but less well on synchronization of KTs. |
Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons (2024.lrec-main)
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| Challenge: | In this paper, we examine the ability of large language models (LLMs) to identify different meanings in sentences that are superficially similar. |
| Approach: | They propose a challenge dataset for NLP with large lexical overlap which minimises the possibility of models discerning entailment solely based on token distinctions. |
| Outcome: | The proposed model fails to distinguish between constructions with three classes of adjectives which cannot be distinguished by surface features. |
Context-Aware Non-Autoregressive Document-Level Translation with Sentence-Aligned Connectionist Temporal Classification (2024.lrec-main)
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| Challenge: | Existing studies employ autoregressive translation (AT) methods to encode sentences . however, the AT methods struggle with error accumulation when the length of sentences increases. |
| Approach: | They propose a context-aware non-autoregressive framework with the sentence-aligned connectionist temporal classification loss for document-level neural machine translation. |
| Outcome: | The proposed framework achieves 46X speedup on three benchmarks compared to strong baselines. |
Context Matters: Enhancing Metaphor Recognition in Proverbs (2024.lrec-main)
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| Challenge: | Figurative language interpretation requires models to navigate beyond literal meaning and delve into underlying semantics of the figurative expressions. |
| Approach: | They propose to use GPT-3.5 to perform word-level metaphor detection in a zero-shot setting to examine its performance. |
| Outcome: | The proposed model performs well in identifying word-level metaphors in English proverbs in zero-shot setting. |
Context Shapes Emergent Communication about Concepts at Different Levels of Abstraction (2024.lrec-main)
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| Challenge: | Concept-level reference game allows speakers to communicate concepts at different levels of abstraction and in different contexts. |
| Approach: | They use a symbolic dataset that disentangles concept type and context to study the influence of these factors on the emerging language. |
| Outcome: | The proposed model disentangles concept type and context to study the communication of concepts at different levels of abstraction and in different contexts. |
Contextualizing Generated Citation Texts (2024.lrec-main)
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| Challenge: | Abstractive citation text generation is usually framed as an infilling task . however, examining a recent LED-based citation generation system, we find that many of the generated citations are generic summaries of the reference paper’s main contribution, ignoring the citation context’s focus on a different topic. |
| Approach: | They propose a modification to the citation text generation task by training the generation model to generate a citation given a reference paper and the context window around the target. |
| Outcome: | The proposed model can generate citations based on the entire context window, including the target citation. |
Contextual Modeling for Document-level ASR Error Correction (2024.lrec-main)
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| Challenge: | Existing work on document-level ASR error correction ignores contextual information . however, there are limited studies on incorporating contextual information into AEC . |
| Approach: | They propose a context-aware method that retrieves contextual information from a datastore . they use two English and two Chinese datasets to model document-level AEC . |
| Outcome: | The proposed model can utilize contextual information to improve document-level AEC . the data store containing contextual information provides even better results . |
Continual Few-shot Event Detection via Hierarchical Augmentation Networks (2024.lrec-main)
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| Challenge: | Existing methods for continual few-shot event detection use labeled data, but in real-world applications, new event types emerge continually. |
| Approach: | They propose a memory-based framework for continual few-shot event detection . they incorporate prototypical augmentation into the memory set to memorize previous event types . |
| Outcome: | The proposed method outperforms existing methods in multiple continual few-shot event detection tasks. |
Continual Reinforcement Learning for Controlled Text Generation (2024.lrec-main)
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| Challenge: | Controlled Text Generation (CTG) aims to steer text generation towards texts possessing a desired attribute. |
| Approach: | They propose an algorithm that steers the generation of continuations of a given context . they propose a Continual Learning problem to learn at every step to steer next-word generation . |
| Outcome: | The proposed algorithm is based on a plug-and-play language model and exhibits promising results. |
Continued Pre-training on Sentence Analogies for Translation with Small Data (2024.lrec-main)
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| Challenge: | Using 10 times fewer instances, CPoA can achieve gains of +1.4 and +1.3 BLEU points over the original model. |
| Approach: | They propose to train models with analogical abilities on sentence analogies retrieved from corpus . they use a weighting scalar to adjust the influence of closer analogies while diminishing impact of far ones . |
| Outcome: | The proposed approach improves translation performance on a low-resource translation task in german-upper sorbian . it uses 10 times fewer instances to achieve gains of +1.4 and +1.3 BLEU points over the original model . |
Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog (2024.lrec-main)
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| Challenge: | Existing topic models assume that there are only 0/1-state relationships between the two parties in social networks, but the relationship status in real life is more complicated. |
| Approach: | They propose a topic model that leverages unsupervised learning to mine hidden topics in document collections using multi-grained text. |
| Outcome: | The proposed model can be applied to microblog with multi-grained text to realize the representation of the relationship state and make up for the context and structural information lost by previous representation methods. |
ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure (2024.lrec-main)
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| Challenge: | Existing methods for identifying metaphoric expressions in text relied on manual effort to identify the basic and contextual meanings of words. |
| Approach: | They propose a model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence. |
| Outcome: | The proposed model outperforms methods that rely on embeddings or integrate only basic definitions and other external knowledge. |
Contribution of Move Structure to Automatic Genre Identification: An Annotated Corpus of French Tourism Websites (2024.lrec-main)
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| Challenge: | a concept of move structure has been overlooked in genre analysis, but it is not widely used in natural language processing. |
| Approach: | They propose to incorporate move structure into a neural architecture for automatic genre identification. |
| Outcome: | The proposed approach can increase performance and reduce computational power. |
Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
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| Challenge: | Lexically diverse paraphrases are crucial in data augmentation because they enhance the linguistic diversity of the corpus. |
| Approach: | They propose a controllable model for semantic and lexical similarities by attaching tags to the head of the input sentence. |
| Outcome: | The proposed model can paraphrase an input sentence according to the tags specified. |
Controllable Sentence Simplification in Swedish Using Control Prefixes and Mined Paraphrases (2024.lrec-main)
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| Challenge: | Automated Text Simplification (ATS) systems aim to facilitate readability and comprehension by reducing linguistic complexity. |
| Approach: | They propose to use a dataset of Swedish paraphrases to train ATS models utilizing prefix-tuning with control prefixes to provide more control over the simplification. |
| Outcome: | The proposed model improves on the baseline model and compares with previous models. |
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Existing studies present tokens, examples, and hints for corrections, but do not directly explain the reasons in natural language. |
| Approach: | They propose a method called controlled generation with Prompt Insertion that uses Large Language Models to explain the reasons for corrections in natural language. |
| Outcome: | The proposed method can explain the reasons for corrections in natural language by guiding the LLMs to generate explanations for all correction points. |
ControversialQA: Exploring Controversy in Question Answering (2024.lrec-main)
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| Challenge: | Existing studies on controversy define it based on vague assumptions of its relation to sentiment . experimental results show controversy detection is essential and challenging . |
| Approach: | They propose a question-answering dataset that defines content controversy by user perception . they show controversy detection is essential and challenging . |
| Outcome: | The proposed dataset defines controversy by user perception, i.e., votes from plenty of users. |
Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units (2024.lrec-main)
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| Challenge: | Successful conversations often rest on common understanding, says a researcher . despite recent advances in dialog systems, there is a noticeable deficit in their grounding capabilities . |
| Approach: | They propose to use a framework to build conversational grounding in dialogs . they propose to analyze two dialog corpora using grounding acts and grounding units . |
| Outcome: | The proposed model shows that language models are not enough to ground dialogs with machines . the proposed model can be used to test the performance of existing Language Models . |
Converting Legacy Data to CLDF: A FAIR Exit Strategy for Linguistic Web Apps (2024.lrec-main)
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| Challenge: | a number of web applications that enabled comparative linguistics research became obsolete . cross-linguistic data formats (CLDF) are available for use in linguistic research . |
| Approach: | a new standard allows researchers to convert legacy linguistic web apps into FAIR data . the standard uses W3C recommendations Model for Tabular Data and Metadata on the Web and MetaData Vocabulary for Tabulary . |
| Outcome: | a new standard can be used to convert legacy linguistic web apps into FAIR datasets . the standard is built on the W3C recommendations Model for Tabular Data and Metadata on the Web and MetaData Vocabulary for Tabulary on the web . |
CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions (2024.lrec-main)
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| Challenge: | CookingSense is a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes. |
| Approach: | They introduce CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes. |
| Outcome: | The proposed system improves retrieval augmented language models and food decision support systems. |
CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning (2024.lrec-main)
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| Challenge: | Existing methods for boosting ICD coding performance lack a model for complex code relations . current methods overlook the importance of context in clinical notes . |
| Approach: | They propose a contextualized and flexible framework to enhance learning of ICD code relations . they use clinical notes to model all possible code relations using a dependent learning paradigm . |
| Outcome: | The proposed approach improves on six public ICD coding datasets compared to state-of-the-art models. |
CORI: CJKV Benchmark with Romanization Integration - a Step towards Cross-lingual Transfer beyond Textual Scripts (2024.lrec-main)
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| Challenge: | Naively assuming English as a source language may hinder cross-lingual transfer . despite recent advances in cross-linguistic research, most studies have restricted themselves to two major assumptions . |
| Approach: | They propose to integrate Romanized transcription beyond textual scripts to capture contact between these languages . they propose to use a benchmark dataset to further encourage in-depth studies of language contact . |
| Outcome: | The proposed method allows for enhanced cross-lingual representations and effective zero-shot cross-linguistic transfer. |
Corpus Creation and Automatic Alignment of Historical Dutch Dialect Speech (2024.lrec-main)
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| Challenge: | The Dutch Dialect Database contains dialectal variations of Dutch recorded in the second half of the twentieth century. |
| Approach: | They propose to create a corpus containing audio recordings and orthographic transcriptions of Dutch dialects recorded in the second half of the 20th century. |
| Outcome: | The Dutch Dialect Database contains dialectal variations recorded all over the Netherlands in the second half of the twentieth century. |
Corpus Services: A Framework to Curate XML Corpus Data (2024.lrec-main)
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| Challenge: | Existing corpora on Samoyedic ( Uralic) languages include the INEL Kamas Corpus and the INL Selkup Corpus . |
| Approach: | They describe the Corpus Services framework, a collection of Java validation tools for language corpora compiled in XML-based data formats. |
| Outcome: | The proposed framework is integrated into the curation and publication workflows for EXMARaLDA-driven corpora of Northern Eurasian languages, as developed by the long-term project INEL . |
Correcting Language Model Bias for Text Classification in True Zero-Shot Learning (2024.lrec-main)
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| Challenge: | Experimental results show that pre-trained language models outperform standard prompt learning in zero-shot settings. |
| Approach: | They propose a pipeline for annotating and filtering examples from unlabeled examples . they propose 'model bias validation' method that utilizes unlabed examples as validation set . |
| Outcome: | The proposed approach outperforms standard prompt learning on six text classification tasks. |
Correcting Pronoun Homophones with Subtle Semantics in Chinese Speech Recognition (2024.lrec-main)
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| Challenge: | Chinese speech recognition is becoming prevalent due to the similar semantic context of the entities and the overlap of Chinese pronunciation. |
| Approach: | They propose three models to address common confusion issues in Chinese speech recognition . they implement a language model, a LSTM model with semantic features and a rule-based assisted Ngram model . |
| Outcome: | The proposed models achieve highest recognition rate for “T” correction with improvements from 70% in the popular voice input methods up to 90%. |
Correlations between Multilingual Language Model Geometry and Crosslingual Transfer Performance (2024.lrec-main)
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| Challenge: | Pre-trained multilingual language models represent multiple languages in a single vector space, a feature hypothesized to enable impressive crosslingual transfer capabilities. |
| Approach: | They propose to use a multilingual representation space that sorts axes based on their language-separability to determine whether geometric distances between languages correlate with crosslingual transfer performance. |
| Outcome: | The proposed measures do not generalize well across models, layers, and tasks. |
Cost-Effective Discourse Annotation in the Prague Czech–English Dependency Treebank (2024.lrec-main)
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| Challenge: | a method for obtaining a high-quality annotation of explicit discourse relations is a resource-demanding task. |
| Approach: | They propose a method for obtaining a high-quality annotation of explicit discourse relations in the Czech part of the Prague Czech–English Dependency Treebank. |
| Outcome: | The proposed method solves the problem of identifying discrepancies between the annotations in the Czech part of the Penn Treebank. |
Counterfactual Dialog Mixing as Data Augmentation for Task-Oriented Dialog Systems (2024.lrec-main)
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| Challenge: | High-quality training data for Task-Oriented Dialog systems is costly to come by if no corpora are available. |
| Approach: | They propose a data augmentation technique that generates realistic synthetic dialogs via counterfactuals to increase the amount of training data. |
| Outcome: | The proposed technique achieves state-of-the-art on a benchmark dataset and lower resource setting. |
Creating Terminological Resources in the Digital Age for Less-resourced Languages (2024.lrec-main)
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| Challenge: | Multilingual terminological resources are limited in less resourced languages, limiting knowledge spread in less-resourced languages . linguists and terminologists must use natural language processing tools to maximize resources . less-represented languages suffer from a lack of available linguistic resources - a survey shows . |
| Approach: | They propose a method to maximize the open access catalan terminology available . authors propose linguists and terminologists supervise the project and translate it into catalane . |
| Outcome: | The proposed method maximizes the catalan terminology currently available in open access . the results are supervised by linguists and terminologists experts before being publicly available to the public. |
Creation and Analysis of an International Corpus of Privacy Laws (2024.lrec-main)
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Sonu Gupta, Geetika Gopi, Harish Balaji, Ellen Poplavska, Nora O’Toole, Siddhant Arora, Thomas Norton, Norman Sadeh, Shomir Wilson
| Challenge: | a corpus of 1,043 privacy laws, regulations, and guidelines covers 183 jurisdictions . prior efforts to study privacy law in the form of privacy policies have lacked a large-scale collection . |
| Approach: | They propose a corpus of 1,043 privacy laws, regulations, and guidelines covering 183 jurisdictions. |
| Outcome: | The Privacy Law Corpus covers 1,043 privacy laws, regulations, and guidelines covering 183 jurisdictions. |
Croatian Idioms Integration: Enhancing the LIdioms Multilingual Linked Idioms Dataset (2024.lrec-main)
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| Challenge: | Existing datasets that include idioms from English, German, Italian, Portuguese and Russian do not include a comprehensive representation of idiomatic expressions in Croatian. |
| Approach: | They propose to extend existing RDF-based multilingual representation of idioms to include 1,042 Croatian idiomes in an Ontolex Lemon format. |
| Outcome: | The proposed resource includes 1,042 Croatian idioms in an Ontolex Lemon format to foster translation initiatives and facilitate intercultural exchange. |
CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization (2024.lrec-main)
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| Challenge: | Cross-lingual summarization (CLS) has attracted increasing interest due to the availability of large-scale web-mined datasets and the advancements of multilingual language models. |
| Approach: | They propose a dataset of cross-lingual code-switched summaries in Chinese and English . they show that leveraging existing CLS resources does not improve performance . |
| Outcome: | The proposed method does not improve on CroCoSum, indicating the limited generalizability of existing approaches. |
Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish (2024.lrec-main)
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| Challenge: | Currently, most of the research on misinformation is focused on the English language . however, there is a scarcity of datasets for other languages, including Turkish . |
| Approach: | They propose a dataset that spans multiple domains and incorporates evidence from three Turkish fact-checking organizations. |
| Outcome: | The proposed dataset has the potential to advance research in the Turkish language. |
Cross-lingual Named Entity Corpus for Slavic Languages (2024.lrec-main)
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| Challenge: | This work presents a corpus manually annotated with named entities for six Slavic languages . |
| Approach: | They propose to manually annotate a corpus of names for six Slavic languages . they use a transformer-based neural network architecture to train multilingual models . |
| Outcome: | The corpus consists of 5,017 documents on seven topics . each entity is described by a category, a lemma, and a unique cross-lingual identifier. |
Cross-Lingual NLU: Mitigating Language-Specific Impact in Embeddings Leveraging Adversarial Learning (2024.lrec-main)
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| Challenge: | Low-resource languages and computational expenses pose significant challenges in the domain of large language models. |
| Approach: | They propose a novel approach that uses adversarial techniques to mitigate the impact of language-specific information in contextual embeddings generated by large multilingual language models. |
| Outcome: | The proposed approach excels in zero-shot scenarios for Latin languages like Spanish, but fails to perform for languages distant from English, such as Thai and Persian. |
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity (2024.lrec-main)
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| Challenge: | Using labeled NLI datasets for learning sentence embeddings leads to improved performance for natural language understanding tasks. |
| Approach: | They compare two data augmentation techniques for learning better sentence embeddings . they use a cross-lingual transfer technique that exploits English resources as training data to yield non-English sentence embeds as zero-shot inference . |
| Outcome: | The proposed techniques yield better performance on Japanese and Korean sentences. |
Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets (2024.lrec-main)
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| Challenge: | Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities for downstream tasks such as Named Entity Recognition (NER) challenges persist in MLLM implementations that are not cross-linguistically robust. |
| Approach: | They evaluate two well-known MLLMs on 13 pairs of languages with a geographic, genetic, or borrowing relationship. |
| Outcome: | The proposed models show that they can leverage information acquired in a source language and apply it to a target language. |
CrossTune: Black-Box Few-Shot Classification with Label Enhancement (2024.lrec-main)
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| Challenge: | Training large-scale language models requires substantial computation resources . current research focuses on adapting black-box models to downstream tasks using prompt optimization . |
| Approach: | They propose a label-enhanced cross-attention network called CrossTune to improve the generalization of the model. |
| Outcome: | The proposed approach outperforms the state-of-the-art black-box tuning method by 5.7% on average. |
Cross-type French Multiword Expression Identification with Pre-trained Masked Language Models (2024.lrec-main)
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| Challenge: | Multiword expressions (MWEs) have linguistic features that distinguish them from regular word groupings. |
| Approach: | They propose a combination of two systems that learn verbal multiword expressions and non-verbal MWEs to improve performance on a cross-type dataset . |
| Outcome: | The proposed system improves the F1 score on a french treebank with VMWEs and nVMWES training data. |
CSSWiki: A Chinese Sentence Simplification Dataset with Linguistic and Content Operations (2024.lrec-main)
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| Challenge: | Existing datasets for sentence simplification focus on English, but limited in Chinese . SS tasks are aimed at improving readability and making sentences more accessible for readers . |
| Approach: | They propose an open-source dataset for Chinese sentence simplification based on Wikipedia . they analyze differences in annotation scheme and data statistics between datasets . |
| Outcome: | The proposed dataset contains 1.6k source sentences paired with their simplified versions. |
CTSM: Combining Trait and State Emotions for Empathetic Response Model (2024.lrec-main)
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| Challenge: | Empathetic response generation attempts to empower dialogue systems to perceive speakers’ emotions and generate empathetic responses accordingly. |
| Approach: | They propose to combine trait and state emotions for Empathetic Response Model to enable dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates more empathetic responses. |
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages (2024.lrec-main)
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Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, Hieu Man, Nghia Trung Ngo, Franck Dernoncourt, Ryan A. Rossi, Thien Huu Nguyen
| Challenge: | Existing training datasets for large language models are often not fully disclosed. |
| Approach: | They propose a multilingual dataset with 6.3 trillion tokens in 167 languages . they use a pipeline of multiple stages to achieve the best quality for model training . |
| Outcome: | The proposed dataset is cleaned and deduplicated to achieve the best quality for model training . lack of transparency has hindered research on attributing and addressing hallucination and bias issues . 6.3 trillion tokens in 167 languages are used to train multilingual LLMs . |
Curation of Benchmark Templates for Measuring Gender Bias in Named Entity Recognition Models (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) models are susceptible to gender bias . benchmark datasets are curated specifically for a given NLP task . |
| Approach: | They propose to filter out benchmark templates with a higher probability of detecting gender bias in NER models. |
| Outcome: | The proposed method is based on masked token prediction and tested in English and german using the corresponding fine-tuned BERT base model. |
CuRIAM: Corpus Re Interpretation and Metalanguage in U.S. Supreme Court Opinions (2024.lrec-main)
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| Challenge: | judicial opinions use language to comment on or draw attention to other language . a recent case involving a federal anti-discrimination law requires that justices determine the meaning of just one word or phrase in a specific context. |
| Approach: | They identify 9 categories prominent in metalinguistic discussions, including key terms, definitions, and different kinds of sources. |
| Outcome: | The results show that the annotated concepts are well-defined and frequent, and that they differ between majority, concurring, and dissenting opinions. |
Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition (2024.lrec-main)
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| Challenge: | Existing models for multimodal Emotion Recognition in conversation (ERC) use text as the main modality for emotion recognition. |
| Approach: | They propose a Directed Acyclic Graph (DAG) approach that integrates textual, acoustic, and visual features within a unified framework. |
| Outcome: | The proposed model outperforms baseline models on the IEMOCAP and MELD datasets. |
CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval (2024.lrec-main)
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| Challenge: | Existing methods to assess Statutory Article Retrieval (SAR) are vague and underspecified, resulting in a lack of clarity and a gap between legal expertise and public comprehension. |
| Approach: | They propose a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR) it employs a curriculum-based negative sampling strategy guiding the model to focus on easier negatives initially and progressively tackle more difficult ones. |
| Outcome: | The proposed approach surpasses static methods and can be used to assess the difficulty of the model. |
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling (2024.lrec-main)
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| Challenge: | Existing topic models rely on bag-of-words (BOW) representations to capture word order information. |
| Approach: | They propose a neural topic model that integrates contextualized word embeddings from BERT to learn the topic vector of a document without BOW information. |
| Outcome: | The proposed model generates more coherent and meaningful topics compared to existing models while accommodating unseen words in newly encountered documents. |
CyberAgressionAdo-v2: Leveraging Pragmatic-Level Information to Decipher Online Hate in French Multiparty Chats (2024.lrec-main)
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| Challenge: | Using a hierarchical tagset, cyberbullying narratives are described in the dataset CyberAgressionAdo-V1 . resulting dataset comprises 19 conversations that have been manually annotated . |
| Approach: | They propose a new tagset that includes tags marking pragmatic-level information occurring in cyberbullying situations. |
| Outcome: | The proposed tagset includes tags marking pragmatic-level information occurring in cyberbullying situations. |
Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks (2024.lrec-main)
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| Challenge: | 3.1K reviews are manually annotated for aspect-based sentiment analysis (ABSA) ABSA is a fine-grained task that aims to identify the sentiment associated with each aspect or characteristic of a text. |
| Approach: | They propose a new Czech dataset for aspect-based sentiment analysis . the new dataset is built upon the older Czech dataset . authors provide 24M reviews without annotations suitable for unsupervised learning . |
| Outcome: | The proposed dataset is built upon the older dataset, but is specifically designed for more complex tasks. |
DACL: Disfluency Augmented Curriculum Learning for Fluent Text Generation (2024.lrec-main)
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| Challenge: | Disfluency-aware language models are traditionally trained on fluent, written text corpora. |
| Approach: | They propose a Disfluency Augmented Curriculum Learning approach to tackle disfluency . they use CL coupled with synthetically augmented disfluent texts of various levels . |
| Outcome: | The proposed model surpasses existing techniques in word-based precision (by up to 1%) and has shown favorable recall and F1 scores. |
DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods (2024.lrec-main)
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| Challenge: | Social scientists increasingly use demographically stratified social media data to study attitudes, beliefs, and behavior of the general public. |
| Approach: | They validated the DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures. |
| Outcome: | The best XLM-based classifier improves upon the commonly used competitor M3 by up to 53% F1. |
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition (2024.lrec-main)
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| Challenge: | End-to-end automatic speech recognition systems suffer from mistranscription of domain-specific phrases, such as named entities. |
| Approach: | They propose a named entity correction model that leverages phonetic con-fusion to mitigate phonetic confusion. |
| Outcome: | The proposed model outperforms the existing model on AISHELL-1 and Homophone datasets. |
DanteLLM: Let’s Push Italian LLM Research Forward! (2024.lrec-main)
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| Challenge: | Existing models for large language processing in the English language are limited in resources and evaluation tools for non-English languages. |
| Approach: | They propose a benchmark and an open LLM Leaderboard to evaluate LLMs’ performance in Italian and propose 'DanteLLM' it is the most performant LLM in the world, with improvements of up to 6 points . |
| Outcome: | The proposed model outperforms existing models in Italian and offers a blueprint for the development and evaluation of LLMs in other languages. |
DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays (2024.lrec-main)
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Nils-Jonathan Schaller, Andrea Horbach, Lars Ingver Höft, Yuning Ding, Jan Luca Bahr, Jennifer Meyer, Thorben Jansen
| Challenge: | Existing corpora focus on specific out-of-school domains, such as legal documents. |
| Approach: | They present a digital argumentation instruction for science corpus on 4589 essays written by 1839 german secondary school students. |
| Outcome: | The proposed corpus is annotated according to a fine-grained annotation scheme on 4589 essays written by 1839 german secondary school students. |
Data Collection Pipeline for Low-Resource Languages: A Case Study on Constructing a Tetun Text Corpus (2024.lrec-main)
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| Challenge: | Labadain Crawler is a data collection pipeline designed to automate and optimize the process of constructing textual corpora from the web, with a specific target to low-resource languages. |
| Approach: | They propose a data collection pipeline built on top of Nutch, an open-source web crawler and data extraction framework, and a tokenizer and identifier for Tetun. |
| Outcome: | The proposed pipeline is based on Nutch, an open-source web crawler and data extraction framework, and is tested with Tetun, one of Timor-Leste’s official languages. |
Data Drift in Clinical Outcome Prediction from Admission Notes (2024.lrec-main)
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Paul Grundmann, Jens-Michalis Papaioannou, Tom Oberhauser, Thomas Steffek, Amy Siu, Wolfgang Nejdl, Alexander Loeser
| Challenge: | a pivotal dataset for clinical NLP research was released in 2016 . public access to such datasets is limited due to privacy and ethical concerns . |
| Approach: | They propose a novel clinical outcome prediction dataset based on MIMIC-IV . they provide initial insights into the performance of models trained on MIDIC-III . |
| Outcome: | The proposed dataset aims to probe the robustness and generalization of clinical outcome prediction models . the study focuses on challenges tied to evolving documentation standards and changing codes in the ICD taxonomy . |
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks (2024.lrec-main)
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| Challenge: | Attention pruning techniques have been developed to identify and exploit sparseness . previous work has taken pioneering steps to discover and explain the sparsity in attention patterns . |
| Approach: | They propose a framework that observes attention patterns in a fixed dataset and generates a global sparseness mask. |
| Outcome: | The proposed approach saves 90% of computations and maintains quality of results. |
Dataset for Identification of Homophobia and Transphobia for Telugu, Kannada, and Gujarati (2024.lrec-main)
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| Challenge: | There has been a rise in homophobic and transphobic content targeting LGBT+ individuals on social media platforms. |
| Approach: | They propose to use a dataset to automatically identify homophobic and transphobic content within comments collected from YouTube for three languages. |
| Outcome: | The proposed dataset will identify homophobic and transphobic content within comments collected from YouTube in Telugu, Kannada, and Gujarati. |
Dataset of Quotation Attribution in German News Articles (2024.lrec-main)
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| Challenge: | Lack of annotated data for quotation attribution in news articles severely limits the quality and usability of possible systems. |
| Approach: | They propose a dataset for quotation attribution in German news articles using WIKINEWS and manually annotated quotes from 1000 articles. |
| Outcome: | The proposed dataset provides curated, high-quality annotations across 1000 documents (250,000 tokens) in a fine-grained annotation schema enabling various downstream uses for the dataset. |
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding (2024.lrec-main)
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| Challenge: | Existing methods for decoding target language are degenerate, hallucinating or empty. |
| Approach: | They propose a method that tunes down the Softmax temperature to reduce autoregressive over-smoothness by label smoothing the output distributions. |
| Outcome: | The proposed method improves MBR in various settings. |
DDxGym: Online Transformer Policies in a Knowledge Graph Based Natural Language Environment (2024.lrec-main)
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Benjamin Winter, Alexei Gustavo Figueroa Rosero, Alexander Loeser, Felix Alexander Gers, Nancy Katerina Figueroa Rosero, Ralf Krestel
| Challenge: | specialized OpenAI Gym environment for clinical differential diagnosis is limited by data access due to privacy concerns. |
| Approach: | They propose a specialized OpenAI Gym environment for clinical differential diagnosis . they frame the task as a natural-language-based reinforcement learning problem . |
| Outcome: | The proposed model improves over baselines and improves on existing models. |
Dealing with Data Scarcity in Spoken Question Answering (2024.lrec-main)
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| Challenge: | erroneous automatic speech recognition transcriptions and data scarcity hinder spoken QA models . paper focuses on using limited annotated data to improve spoken qa performance . |
| Approach: | They propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. |
| Outcome: | The proposed model produces question-answer pairs from unannotated data with 5.5% relative gain over the model trained with annotated datasets. |
Debiasing Multi-Entity Aspect-Based Sentiment Analysis with Norm-Based Data Augmentation (2024.lrec-main)
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| Challenge: | Recent research has explored strategies for reduce measurable biases in NLP predictions while maintaining prediction accuracy on held-out test sets. |
| Approach: | They propose to augment training data with norm-based language templates derived from previous language resources to reduce biases in NLP models. |
| Outcome: | The proposed model reduces topical bias to less than half while maintaining prediction quality on held-out test sets. |
Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition (2024.lrec-main)
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| Challenge: | Using an emotion-annotated dataset, we aim to provide a resource for the scientific community to study the emotional intricacies of classical literature. |
| Approach: | They propose to provide an emotion-annotated dataset for classical literature and Western mythology using a multivariate time series and a deep learning masked language model. |
| Outcome: | The proposed dataset reveals compelling patterns and phenomena within the Iliad's emotional landscape. |
DECM: Evaluating Bilingual ASR Performance on a Code-switching/mixing Benchmark (2024.lrec-main)
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| Challenge: | Code-switched (CSW) speech is a linguistic phenomenon that occurs when spoken utterances switch languages between sentences. |
| Approach: | They propose to use a dataset to evaluate German-English CSW speech . they show that the dataset includes splits with varying degrees of CSW . |
| Outcome: | The proposed dataset includes spontaneous speech from diverse domains, enabling realistic CSW evaluation in German-English. |
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs (2024.lrec-main)
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Chenxi Sun, Hongzhi Zhang, Zijia Lin, Jingyuan Zhang, Fuzheng Zhang, Zhongyuan Wang, Bin Chen, Chengru Song, Di Zhang, Kun Gai, Deyi Xiong
| Challenge: | Large language models have demonstrated exceptional capability in natural language understanding and generation, but their generation speed is limited by the inherently sequential nature of their decoding process. |
| Approach: | They propose a method that accelerates decoding process without sacrificing quality . they propose lexical unit decoding, which can be integrated with other methods . |
| Outcome: | The proposed method significantly reduces decoding time while maintaining quality while maintaining output quality. |
Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs (2024.lrec-main)
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| Challenge: | a new method is being developed to probe internal linguistic characteristics in neural language models layer by layer . |
| Approach: | They propose a method that uses minimal pairs benchmark to probe internal linguistic characteristics in neural language models layer by layer. |
| Outcome: | The proposed method captures grammaticality labels in language models layer by layer . it is based on the cognitive neurosciences of the brain and its representations as "neural activations". |
Decompose, Prioritize, and Eliminate: Dynamically Integrating Diverse Representations for Multimodal Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing research on multi-modal Named Entity Recognition (MNER) does not integrate all multi-modal representations to provide rich contextual information to improve NER. |
| Approach: | They propose an iterative reasoning framework that integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate" . they propose to use hierarchically connected fusion layers to prioritize transitions from "easy-to-hard" and "coarse-to fine" |
| Outcome: | The proposed framework integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate". |
Deconstructing In-Context Learning: Understanding Prompts via Corruption (2024.lrec-main)
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| Challenge: | Prior work examined how modifying different elements of the prompt can affect model performance, but this limited number of elements made replication challenging. |
| Approach: | They decompose the entire prompt into four components: task description, demonstration inputs, labels, and inline instructions provided for each demonstration. |
| Outcome: | The proposed model is robust to minor prompt modifications, but its underlying pre-trained backbone is brittle . previous studies focused on models with fewer than 15 billion parameters or exclusively examined black-box models like GPT-3 or PaLM, making replication challenging. |
DEEM: Dynamic Experienced Expert Modeling for Stance Detection (2024.lrec-main)
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| Challenge: | Existing work on stance detection tasks using large language models shows promising results, but it may not be able to provide detailed background knowledge. |
| Approach: | They propose a method which leverages the generated experienced experts and lets LLMs reason in a semi-parametric way. |
| Outcome: | The proposed method outperforms methods with self-consistency reasoning and reduces bias. |
Deep Learning Based Named Entity Recognition Models for Recipes (2024.lrec-main)
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Ayush Agarwal, Janak Kapuriya, Shubham Agrawal, Akhil Vamshi Konam, Mansi Goel, Rishabh Gupta, Shrey Rastogi, Niharika Niharika, Ganesh Bagler
| Challenge: | Named entity recognition is a technique for extracting information from unstructured data with known labels. |
| Approach: | They use named entity recognition to annotate ingredients from recipe data . they use a clustering-based approach to annnotate 88,526 phrases . |
| Outcome: | The proposed method improves on a dataset of 88,526 phrases from RecipeDB . the fine-tuned spaCy-transformer performs better than the previous methods . |
Deep Reinforcement Learning-based Dialogue Policy with Graph Convolutional Q-network (2024.lrec-main)
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| Challenge: | Existing methods for deep reinforcement learning lack the ability to learn the relationship between dialogue states and actions. |
| Approach: | They propose a graph-structured dialogue policy framework for task-oriented dialogue systems that uses bipartite graphs to construct two different bipartites and generate user-related and knowledge-related subgraphs. |
| Outcome: | The proposed framework significantly improves the effectiveness and stability of dialogue policies. |
Deep Reinforcement Learning with Hierarchical Action Exploration for Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing approaches to improve dialogues with random sampling are inefficient due to the large number of eligible responses with high action values. |
| Approach: | They propose a dual-granularity Q-function that extracts actions based on a grained hierarchy . they use offline RL and learn from multiple reward functions designed to capture emotional nuances in human interactions. |
| Outcome: | The proposed approach outperforms baselines across automatic metrics and human evaluations. |
DeFaktS: A German Dataset for Fine-Grained Disinformation Detection through Social Media Framing (2024.lrec-main)
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| Challenge: | Distinctively curated across various news topics, DeFaktS offers an unparalleled insight into disinformation’s diverse characteristics. |
| Approach: | They propose to annotate every structural component and semantic element of a news piece, eliminating the need for external knowledge sources. |
| Outcome: | The proposed dataset contains 105,855 posts with 20,008 meticulously labeled tweets and eliminates the need for external knowledge sources. |
DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)
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| Challenge: | Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model . |
| Approach: | They propose a large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments. |
| Outcome: | The proposed dataset has over 56,000+ events and 242,000+ arguments. |
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing studies focus on cross-modal attention at the fusion stage, but modality features generated by disparate uni-encoders reside in their own spaces, leading to a decline in the quality of cross-modulation and decision-making. |
| Approach: | They propose a framework to align navigation-related modalities before fusion by cross-modal contrastive learning. |
| Outcome: | The proposed framework integrates with the majority of existing models, resulting in improved navigation performance on various VLN benchmarks, including R2R, R4R, and CVDN. |
Demonstration Retrieval-Augmented Generative Event Argument Extraction (2024.lrec-main)
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| Challenge: | Experimental results show that our method outperforms all strong baselines and can be generalized to various datasets. |
| Approach: | They propose a generative EAE that uses event knowledge-injected generator and demonstration retriever to generate event arguments from training data. |
| Outcome: | The proposed method outperforms baselines and can be generalized to various datasets. |
Denoising Labeled Data for Comment Moderation Using Active Learning (2024.lrec-main)
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| Challenge: | Large contextualized language models (LLMs) are becoming ubiquitous in natural language processing due to their performance and adaptability to diverse tasks. |
| Approach: | They propose to use active learning methods to denoise textual data for model training by sampling the most informative examples with noisy labels with active learning. |
| Outcome: | The proposed method reduces the cost of reannotation by reducing noise in noisy examples. |
Denoising Table-Text Retrieval for Open-Domain Question Answering (2024.lrec-main)
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| Challenge: | Existing studies in table-text open-domain question answering have problems with false-positive labels in training datasets. |
| Approach: | They propose a denoised table-text retriever that discards false positives from training datasets . they integrate table-level ranking information into the retriever to assist in finding evidence . |
| Outcome: | The proposed method outperforms baselines on retrieval recall and QA tasks. |
Dependencies over Times and Tools (DoTT) (2024.lrec-main)
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Andy Luecking, Giuseppe Abrami, Leon Hammerla, Marc Rahn, Daniel Baumartz, Steffen Eger, Alexander Mehler
| Challenge: | Using the examples of English and German, we examine how parsers trained on modern variants of these languages can be transferred to older language levels without loss. |
| Approach: | They develop a treebank of diachronic corpora enriched with dependency annotations using 3 parsers, 6 pre-trained language models, 5 newly trained models for German, and two tag sets. |
| Outcome: | The proposed treebank covers the time period from 1800 until today and is based on the DependencyAnnotator annotation tool. |
Depth Aware Hierarchical Replay Continual Learning for Knowledge Based Question Answering (2024.lrec-main)
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| Challenge: | Continual learning models adapt well to the latest data but lose ability to remember past data due to changes in the data source. |
| Approach: | They propose a hierarchical replay framework that allows models to keep a small memory of previous learned data that uses replay. |
| Outcome: | The proposed model outperforms previous continual learning methods in mitigating catastrophic forgetting. |
Depth-Wise Attention (DWAtt): A Layer Fusion Method for Data-Efficient Classification (2024.lrec-main)
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| Challenge: | Language Models pretrained on large textual data can encode different types of knowledge simultaneously. |
| Approach: | They propose a method to re-surface intermediate layer features from non-final layers by combining them with a concatenation-based layer fusion method. |
| Outcome: | The proposed method outperforms the baseline model on large datasets and shows 3.68 9.73% gain. |
Deriving Entity-Specific Embeddings from Multi-Entity Sequences (2024.lrec-main)
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| Challenge: | Existing methods toward entity-specific prediction involve redundant computation or post-processing outside of the transformer. |
| Approach: | They propose a method for deriving entity-specific embeddings from a multi-entity sequence completely within the transformer, with a loose definition of entity amenable to many problem spaces. |
| Outcome: | The proposed method can be applied to emotion recognition in conversation and player performance projection in baseball and achieve SOTA in both. |
DET: A Dual-Encoding Transformer for Relational Graph Embedding (2024.lrec-main)
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| Challenge: | Existing approaches to graph representation only consider the local neighbors, sacrificing the Transformer’s ability to attend to elements at any distance. |
| Approach: | They propose a dual-encoding Transformer architecture that uses a structural encoder and a semantic encoder to seek for semantically relevant nodes. |
| Outcome: | The proposed architecture achieves superior performance compared to state-of-the-art attention-based methods on complex relational graphs like KGs and citation networks. |
Detecting Conceptual Abstraction in LLMs (2024.lrec-main)
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| Challenge: | a novel approach to detecting noun abstraction within a large language model is proposed . a first step towards the explainability of conceptual abstraction in LLMs is shown . |
| Approach: | They propose a method to detect noun abstraction within a large language model . they instantiate taxonomic relationships and analyze attention matrices produced by BERT . |
| Outcome: | The proposed approach can detect hypernymy in a large language model . the results are a first step towards the explainability of conceptual abstraction in LLMs . |
Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation (2024.lrec-main)
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Sugyeong Eo, Jungwoo Lim, Chanjun Park, DaHyun Jung, Seonmin Koo, Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim
| Challenge: | Recent machine translation systems overcome language barriers for a wide range of users, yet they carry the risk of catastrophic meaning deviations. |
| Approach: | They introduce a culture-aware "Politeness" type for detecting critical translation errors . they also provide multiclass labels for critical error detection and critical error type classification . |
| Outcome: | Empirical results show that the proposed method outperforms baselines in both tasks. |
Detecting Cybercrimes in Accordance with Pakistani Law: Dataset and Evaluation Using PLMs (2024.lrec-main)
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| Challenge: | Roman Urdu is a widely used language in Pakistan but lacks sufficient resources and tools for text-based cybercrime detection. |
| Approach: | They propose to use a benchmark dataset for text-based cybercrime detection in Roman Urdu to improve the performance of pre-trained language models. |
| Outcome: | The proposed model achieves the highest performance on all metrics. |
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics (2024.lrec-main)
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Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann, Nithum Thain, Erin MacMurray van Liemt, Kathleen Meier-Hellstern, Lucas Dixon
| Challenge: | a growing audience of users is engaging with LLM-driven chatbots. |
| Approach: | They propose a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia’s Neutral Point of View principle. |
| Outcome: | The proposed methods detect errors in the tuned LLM responses even when no training data is available. |
Detecting Impact Relevant Sections in Scientific Research (2024.lrec-main)
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| Challenge: | Impact assessment is an evolving area of research that aims at measuring and predicting the potential effects of projects or programs. |
| Approach: | They propose a framework for automatically assessing the impact of scientific research by identifying pertinent sections in project reports that indicate potential impacts. |
| Outcome: | The proposed method achieves accuracy scores up to 0.81 and is generalizable to scientific research from different domains and languages. |
Detecting Loanwords in Emakhuwa: An Extremely Low-Resource Bantu Language Exhibiting Significant Borrowing from Portuguese (2024.lrec-main)
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| Challenge: | Existing corpora in African languages reveal significant spelling inconsistencies, contributing to poor-quality textual data when encountered in written form. |
| Approach: | They propose a supervised method to identify loanwords in Portuguese . they employ traditional machine learning algorithms incorporating handcrafted features . |
| Outcome: | The proposed method achieves the F1-score of 93% in Emakhuwa, borrowed from Portuguese. |
Detecting Offensive Language in an Open Chatbot Platform (2024.lrec-main)
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Hyeonho Song, Jisu Hong, Chani Jung, Hyojin Chin, Mingi Shin, Yubin Choi, Junghoi Choi, Meeyoung Cha
| Challenge: | Existing efforts to automatically filter offensive language are vulnerable to users’ deliberate text manipulation tactics, such as misspelling words. |
| Approach: | They propose a contrastive learning model that embeds chat content with a random masking strategy to detect offensive language in open-domain chat conversations. |
| Outcome: | The proposed model outperforms existing models in detecting offensive language in open-domain chat conversations while also showing robustness against users’ deliberate text manipulation tactics when using offensive language. |
Detecting Sexual Content at the Sentence Level in First Millennium Latin Texts (2024.lrec-main)
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| Challenge: | a traditional approach to corpus building involves constructing a corpus centered around specific themes, such as colors. |
| Approach: | They propose to use deep learning methods to accelerate corpus building in humanities . they propose to integrate metadata embeddings into the model to improve accuracy . |
| Outcome: | The proposed method outperforms token-based searches in the humanities and linguistics field. |
Detection, Diagnosis, and Explanation: A Benchmark for Chinese Medial Hallucination Evaluation (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant progress in recent years, but their practical use is hindered by their tendency to generate hallucinations. |
| Approach: | They propose to use ICD-10 and MeSH to evaluate LLMs' ability to detect medical hallucinations and make accurate diagnoses in noisy environments. |
| Outcome: | The proposed benchmark can be used to evaluate LLMs’ ability to detect medical hallucinations, make accurate diagnoses in noisy conditions, and provide plausible explanations. |
Developing a Benchmark for Pronunciation Feedback: Creation of a Phonemically Annotated Speech Corpus of isiZulu Language Learner Speech (2024.lrec-main)
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Alexandra O’Neil, Nils Hjortnaes, Francis Tyers, Zinhle Nkosi, Thulile Ndlovu, Zanele Mlondo, Ngami Phumzile Pewa
| Challenge: | Existing corpora for computer-assisted pronunciation training (CAPT) do not apply well to research in pronunciation feedback. |
| Approach: | They propose to create a corpus of isiZulu language learner speech that has been annotated for phoneme errors and suprasegmental errors in tone. |
| Outcome: | The proposed corpus is comprised of gold standard recordings from isiZulu teachers and recordings from students that have been annotated for pronunciation errors. |
Developing a Rhetorical Structure Theory Treebank for Czech (2024.lrec-main)
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| Challenge: | a paper on the Czech RST Discourse Treebank is the first version of a textual annotation system based on the Rhetorical Structure Theory . document is annotated using the RST, a global coherence model proposed by Mann and Thompson . |
| Approach: | They introduce the first version of the Czech RST Discourse Treebank . paper presents an annotation process and provides corpus statistics and evaluation . |
| Outcome: | The paper presents the first version of the Czech RST Discourse Treebank . the treebank includes two gold annotations representing divergent interpretations . |
Development and Evaluation of Pre-trained Language Models for Historical Danish and Norwegian Literary Texts (2024.lrec-main)
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| Challenge: | et al., 2019) develop and evaluate the first pre-trained language models specifically tailored for historical Danish and Norwegian texts. |
| Approach: | They develop and evaluate pre-trained language models specifically tailored for historical Danish and Norwegian texts. |
| Outcome: | The proposed model outperforms models trained on historical Danish and Norwegian literature in two downstream NLP tasks. |
Development of Community-Oriented Text-to-Speech Models for Māori ‘Avaiki Nui (Cook Islands Māori) (2024.lrec-main)
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Jesin James, Rolando Coto-Solano, Sally Akevai Nicholas, Joshua Zhu, Bovey Yu, Fuki Babasaki, Jenny Tyler Wang, Nicholas Derby
| Challenge: | Text-to-speech synthesis is used to transform text into a synthesized voice for a specific language. |
| Approach: | They describe the development of a text-to-speech system for Mori ‘Avaiki Nui (Cook Islands Mi) they used two approaches to train the system, the HMM-system MaryTTS and the deep learning system FastSpeech2 . |
| Outcome: | The proposed system is based on the HMM-system MaryTTS and the deep learning system FastSpeech2 . the ground truth voice had the highest quality, but the fastspeech 2 voice had a significantly higher quality than the MaryTTs synthesized recordings. |
DGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation (2024.lrec-main)
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| Challenge: | Existing methods for generating abstracts involve collecting domain data and training corresponding models to complete the task of text summarization. |
| Approach: | They propose a method to train language models based on domain datasets and a Dynamic Graph of Thought (DGoT) which inherits the advantages of existing GoT prompt approach while reducing model reasoning cost. |
| Outcome: | The proposed method saves the cost of model training and improves reliability due to the hallucination problem of LLMs. |
DGS-Fabeln-1: A Multi-Angle Parallel Corpus of Fairy Tales between German Sign Language and German Text (2024.lrec-main)
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Fabrizio Nunnari, Eleftherios Avramidis, Cristina España-Bonet, Marco González, Anna Hennes, Patrick Gebhard
| Challenge: | a parallel corpus of German text and videos containing fairy tales interpreted into the German Sign Language (DGS) is the first corpus filmed from 7 angles and one of the few sign language corpora globally which have been filmed simultaneously. |
| Approach: | They present a parallel corpus of German fairy tales interpreted by a native DGS signer. |
| Outcome: | The proposed corpus is the first semi-naturally expressed DGS that has been filmed from 7 angles and where the listener has been simultaneously filmed. |
Dialogue Systems Can Generate Appropriate Responses without the Use of Question Marks?– a Study of the Effects of “?” for Spoken Dialogue Systems – (2024.lrec-main)
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Tomoya Mizumoto, Takato Yamazaki, Katsumasa Yoshikawa, Masaya Ohagi, Toshiki Kawamoto, Toshinori Sato
| Challenge: | Existing systems for spoken dialogues do not append question marks to recognized queries . lack of punctuation marks in spoken dialogue can negatively impact comprehension . |
| Approach: | They investigate the impact of question marks on spoken dialogue systems . they analyze examples to determine which types of utterances have the impact . |
| Outcome: | The proposed method shows that question marks have a significant impact on spoken dialogue systems. |
DiaSet: An Annotated Dataset of Arabic Conversations (2024.lrec-main)
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Abraham Israeli, Aviv Naaman, Guy Maduel, Rawaa Makhoul, Dana Qaraeen, Amir Ejmail, Dina Lisnanskey, Julian Jubran, Shai Fine, Kfir Bar
| Challenge: | DiaSet is a dataset of dialectical Arabic speech manually transcribed and annotated for two downstream tasks. |
| Approach: | They propose to manually transcribe and annotate Arabic speech for sentiment analysis and named entity recognition. |
| Outcome: | The proposed dataset encapsulates the Palestine dialect, predominantly spoken in Palestine, Israel, and Jordan. |
Did You Get It? A Zero-Shot Approach to Locate Information Transfers in Conversations (2024.lrec-main)
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| Challenge: | Existing models do not provide an efficient way to locate information that enters the common ground. |
| Approach: | They propose a method based on segmentation of a conversation into themes followed by their summarization and obtain the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. |
| Outcome: | The proposed method is based on the segmentation of a conversation into themes followed by their summarization and obtains the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. |
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction (2024.lrec-main)
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Unggi Lee, Sungjun Yoon, Joon Seo Yun, Kyoungsoo Park, YoungHoon Jung, Damji Stratton, Hyeoncheol Kim
| Challenge: | Existing studies have focused on incorporating the difficulty information into knowledge tracing models, but few studies have explored the potential of difficulty estimation. |
| Approach: | They propose a difficulty-centered contrastive learning method and a Large Language Model-based framework for difficulty prediction to improve the performance of knowledge tracing models. |
| Outcome: | The proposed methods demonstrate enhanced performance of knowledge tracing models while ignoring the complex relationship between language and difficulty. |
Diffusion Based Counterfactual Augmentation for Dual Sentiment Classification (2024.lrec-main)
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| Challenge: | Existing methods to augment sentiment models have failed to mitigate spurious association problem inherent in the original data. |
| Approach: | They propose a framework for enhancing sentiment models using an antonymous paradigm and contrastive learning to generate high-quality samples. |
| Outcome: | The proposed framework achieves state-of-the-art performance on four benchmark datasets. |
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space (2024.lrec-main)
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| Challenge: | Existing studies have tried to introduce discrete or Gaussian-based latent variables to address the one-to-many problem, but the diversity is limited. |
| Approach: | They propose a diffusion model to enhance the diversity of dialogue generation by using continuous latent variables instead of discrete ones. |
| Outcome: | The proposed model greatly enhances diversity of dialog response while keeping the coherence. |
DimA: A Parameter-efficient Fine-tuning Method with Knowledge Transfer Based on Transformer (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) have demonstrated impressive performance across various downstream tasks, but fine-tuning is computationally expensive and storage-intensive. |
| Approach: | They propose a parameter-efficient method called DimA which enhances the transformer architecture by increasing the dimensionality. |
| Outcome: | The proposed method achieves state-of-the-art results in GLUE and XSUM tasks while utilizing less than 1% of the original model’s parameters. |
Disambiguating Homographs and Homophones Simultaneously: A Regrouping Method for Japanese (2024.lrec-main)
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| Challenge: | Using a method that re-groups surface forms into clusters representing synonyms, we examine how accurate such disambiguation can be. |
| Approach: | They propose to regroup homographs and homophones into clusters and use them to disambiguate them. |
| Outcome: | The proposed method is applied post-hoc to trained word embeddings in Japanese. |
DiscoGeM 2.0: A Parallel Corpus of English, German, French and Czech Implicit Discourse Relations (2024.lrec-main)
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| Challenge: | DiscoGeM 2.0 is a crowdsourced, parallel corpus of 12,834 implicit discourse relations . implicit discourse relationships are highly ambiguous and can have various interpretations . |
| Approach: | They propose a crowdsourced annotation method that can be extended to other languages . they propose to annotate 12,834 implicit discourse relations in German, German, French and Czech data . |
| Outcome: | The proposed method can be extended to other languages and reveals that implicit relations inferred in one language may differ from those inferted in the translation. |
Discourse Structure for the Minecraft Corpus (2024.lrec-main)
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| Challenge: | a discourse annotated version of the Minecraft Dialogue Corpus is a new linguistic resource for human-computer interaction . a recent study shows that inferring excecutable actions from language is difficult in the Minecraft setting . |
| Approach: | They propose a discourse annotated version of the Minecraft Dialogue Corpus . they train a parser with a novel "2 pass architecture" that gives excellent results . |
| Outcome: | The proposed model performs well on attachment prediction and relation labeling tasks especially long distance attachments. |
Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | Existing methods for prompt-based finetuning are expensive and difficult to use. |
| Approach: | They propose a prompt-based method that uses a pretrained language model to discriminate between original and replacement tokens. |
| Outcome: | The proposed method outperforms state-of-the-art prompt-based few-shot methods on 10 widely-used text classification tasks. |
Disentangling Pretrained Representation to Leverage Low-Resource Languages in Multilingual Machine Translation (2024.lrec-main)
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| Challenge: | Multilingual neural machine translation requires an enormous dataset, leaving the low-resource language (LRL) underdeveloped. |
| Approach: | They evaluated five languages using a parallel corpus of 1,000 instances each and found a zero-shot improvement of 7.4 from the baseline score of 7.1 to a score of 15.5 at best. |
| Outcome: | The proposed model improves performance in the linguistically diverse country of Indonesia by 7.4 from baseline score of 7.1 to 15.5 at best. |
DISRPT: A Multilingual, Multi-domain, Cross-framework Benchmark for Discourse Processing (2024.lrec-main)
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Chloé Braud, Amir Zeldes, Laura Rivière, Yang Janet Liu, Philippe Muller, Damien Sileo, Tatsuya Aoyama
| Challenge: | DISRPT is a multilingual, multi-domain, and cross-framework benchmark dataset for discourse processing. |
| Approach: | They present a multilingual, multi-domain, and cross-framework benchmark dataset for discourse processing that includes 13 languages and 24 corpora covering about 4 millions tokens and around 250,000 discourse relation instances from 4 discourse frameworks. |
| Outcome: | The DISRPT dataset includes data from 24 corpora covering about 4 millions tokens and around 250,000 discourse relation instances from 4 discourse frameworks. |
Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization (2024.lrec-main)
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| Challenge: | Existing methods for code summarization are limited in resources and require atomic commands and category constraints to enhance code representations. |
| Approach: | They propose a framework that leverages limited atomic commands and category constraints to enhance code representations. |
| Outcome: | The proposed framework outperforms baseline methods in a number of domains and demonstrates superiority over competing frameworks. |
Distillation with Explanations from Large Language Models (2024.lrec-main)
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| Challenge: | Large language models (LLMs) like ChatGPT and GPT-4 have made remarkable progress in various NLP tasks while also providing explanations alongside their answers. |
| Approach: | They propose to use Large language models (LLMs) to generate more accurate answers and corresponding free-text explanations by combining ground truth labels and answers-explanations generated by LLMs. |
| Outcome: | The proposed method achieves improved predictive performance and generates explanations that exhibit greater alignment with the model’s task outputs. |
Distill, Fuse, Pre-train: Towards Effective Event Causality Identification with Commonsense-Aware Pre-trained Model (2024.lrec-main)
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| Challenge: | Existing methods to detect causal relationships in unstructured texts ignore trivial knowledge which may prejudice performance. |
| Approach: | They propose a pipeline to build a commonsense-aware pre-trained model which integrates reliable task-specific knowledge from commonsens graphs. |
| Outcome: | The proposed pipeline integrates reliable task-specific knowledge from commonsense graphs. |
Distilling Causal Effect of Data in Continual Few-shot Relation Learning (2024.lrec-main)
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| Challenge: | Existing methods for learning relational patterns from data are prone to catastrophic forgetting issues due to limited number of samples and continual training mode. |
| Approach: | They propose a unified causal framework for CFRL to restore causal effects from old data . they establish two additional causal paths from old to predictions by colliding with old data separately in the old feature space. |
| Outcome: | The proposed method is superior to existing state-of-the-art methods in CFRL task settings. |
Distractor Generation Using Generative and Discriminative Capabilities of Transformer-based Models (2024.lrec-main)
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| Challenge: | Multiple Choice Questions (MCQs) are used to test language learners' comprehension and knowledge. |
| Approach: | They propose an automatic distractor generation approach which generates correct and incorrect answer options and then discriminates potential correct options from distractors. |
| Outcome: | The proposed approach outperforms previous models on multiple choice questions and reading comprehension questions. |
Distribution Aware Metrics for Conditional Natural Language Generation (2024.lrec-main)
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| Challenge: | Existing metrics for conditional natural language generation rely on pairwise comparisons between a single generated text and the best-matching reference. |
| Approach: | They propose a family of meta-metrics that build on existing pairwise distance functions to evaluate conditional natural language generation models. |
| Outcome: | The proposed method evaluates the ability of a model to generate text matching diversity in references in visual description and summarization. |
Diversifying Question Generation over Knowledge Base via External Natural Questions (2024.lrec-main)
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| Challenge: | Existing methods on knowledge base question generation focus on refining the quality of a single generated question. |
| Approach: | They propose a new diversity evaluation metric which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. |
| Outcome: | The proposed model outperforms pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT. |
DMON: A Simple Yet Effective Approach for Argument Structure Learning (2024.lrec-main)
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| Challenge: | Argument structure learning (ASL) involves examining relationships between sentences in unstructured text. |
| Approach: | They propose a dual-tower multi-scale cOnvolution neural network to analyze relationships between arguments in a text. |
| Outcome: | The proposed approach outperforms state-of-the-art models on three domain argument mining datasets. |
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs (2024.lrec-main)
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| Challenge: | Existing work on document visual question answering fails to capture the differences and correlations between elements of a document and associated questions. |
| Approach: | They propose a document-visual question-answering challenge that exploits element-level semantics and employs hierarchical Graph structures to capture differences and correlations between elements. |
| Outcome: | The proposed model surpasses the state-of-the-art method and large language model in terms of Exact Match (EM) metric, demonstrating exceptional effectiveness. |
DOC-RAG: ASR Language Model Personalization with Domain-Distributed Co-occurrence Retrieval Augmentation (2024.lrec-main)
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| Challenge: | Extensive experiments on three user-specific speech-to-text tasks show that DOC-RAG significantly outperforms strong baselines with an 8-15% improvement in terms of perplexity and a 4-7% reduction in terms in terms . of Word Error Rates. |
| Approach: | They propose a domain-distributed co-occurrence augmentation approach to improve automatic speech recognition of rare word patterns in unseen domains by using n-gram co-existence distributions. |
| Outcome: | Experiments on three user-specific speech-to-text tasks show that DOC-RAG outperforms baselines with an 8-15% improvement in terms of perplexity and a 4-7% reduction in terms in terms . of Word Error Rates. |
DocScript: Document-level Script Event Prediction (2024.lrec-main)
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Puneet Mathur, Vlad I. Morariu, Aparna Garimella, Franck Dernoncourt, Jiuxiang Gu, Ramit Sawhney, Preslav Nakov, Dinesh Manocha, Rajiv Jain
| Challenge: | Existing script event prediction frameworks such as ChatGPT and FlanT5 lack the ability to learn long-range dependencies between events. |
| Approach: | They propose a novel script event prediction task which aims to predict the next event from a candidate list of narrative events in long-form documents. |
| Outcome: | The proposed architecture can learn sequential ordering between events at the document scale. |
Document-Level Event Extraction via Information Interaction Based on Event Relation and Argument Correlation (2024.lrec-main)
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| Challenge: | Document-level Event Extraction (DEE) is a vital task in NLP . current approaches overlook intricate relationships among events and subtle correlations among arguments within a document . |
| Approach: | They propose a document-level event extraction tool that integrates event relationships and argument correlation graphs to model the relationship among events. |
| Outcome: | The proposed network outperforms existing models and large language models in terms of F1-score across two benchmark datasets. |
Document Set Expansion with Positive-Unlabeled Learning Using Intractable Density Estimation (2024.lrec-main)
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| Challenge: | Existing approaches to Document Set Expansion (DSE) rely on the unrealistic assumption of knowing the class prior for positive samples in the collection. |
| Approach: | They propose a novel method that utilizes intractable density estimation models to learn the class prior for positive samples in the collection. |
| Outcome: | The proposed method is based on a set of examples from PubMed and Covid datasets in a transductive setting. |
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) require significant computational resources for deployment and use. |
| Approach: | They propose to use low-bit quantization methods to reduce memory footprint and increase inference rate to improve performance of Large Language Models. |
| Outcome: | The proposed methods can reduce the memory footprint and increase the inference rate of LLMs. |
Does ChatGPT Know That It Does Not Know? Evaluating the Black-Box Calibration of ChatGPT (2024.lrec-main)
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| Challenge: | Recent performance of ChatGPT in downstream tasks is questionable, but does it know that it does not know? |
| Approach: | They propose to use three types of proxy confidence to evaluate ChatGPT's black-box calibration ability. |
| Outcome: | The proposed model exhibits a positive correlation with accuracy in TruthfulQA and a negative correlation in the ModAr dataset. |
Does the Generator Mind Its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer (2024.lrec-main)
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| Challenge: | Existing studies have focused on examining hallucinations stemming from static input, such as in summarization or machine translation. |
| Approach: | They propose a knowledge-augmented generator that produces information that remains grounded in contextual knowledge regardless of alterations in the context. |
| Outcome: | The proposed method is designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context. |
Does the Language Matter? Curriculum Learning over Neo-Latin Languages (2024.lrec-main)
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| Challenge: | Curriculum Learning (CL) is emerging as a useful technique to reduce the cost of pre-training Large Language Models. |
| Approach: | They propose to organize training examples from the simplest to the most complex . they then test the approach to Italian and French to determine the complexity of examples . |
| Outcome: | The proposed method can be exported to other languages without adaptation. |
Do Language Models Care about Text Quality? Evaluating Web-Crawled Corpora across 11 Languages (2024.lrec-main)
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Rik van Noord, Taja Kuzman, Peter Rupnik, Nikola Ljubešić, Miquel Esplà-Gomis, Gema Ramírez-Sánchez, Antonio Toral
| Challenge: | Large, curated, web-crawled corpora play a vital role in training language models . however, relatively little attention has been given to the quality of these corporata . |
| Approach: | They compare four of the currently most relevant large, web-crawled corpora across eleven lower-resourced European languages to evaluate their quality. |
| Outcome: | The CC100 corpus achieves the highest scores on the tests in 11 lower-resourced European languages. |
Do Large Language Models Understand Mansplaining? Well, Actually... (2024.lrec-main)
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| Challenge: | Gender bias has been studied by the NLP community, but other variations of it, such as mansplaining, have received little attention. |
| Approach: | They propose to analyze a corpus of 886 mansplaining stories experienced by women and examine how Large Language Models can understand and identify mansplaiting. |
| Outcome: | The proposed models reproduce some of the social patterns behind mansplaining situations by praising men for giving unsolicited advice to women. |
Domain Adaptation for Dense Retrieval and Conversational Dense Retrieval through Self-Supervision by Meticulous Pseudo-Relevance Labeling (2024.lrec-main)
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| Challenge: | Recent studies have shown that dense retrieval models generalize less well than interaction-based models on out-of-distribution data sets. |
| Approach: | They propose to combine query-generation approach with self-supervision approach in which pseudo-relevance labels are automatically generated on the target domain. |
| Outcome: | The proposed approach is based on a T5-3B model for pseudo-positive labeling and hard negatives on conversational dense retrieval models. |
Domain-Agnostic Adapter Architecture for Deception Detection: Extensive Evaluations with the DIFrauD Benchmark (2024.lrec-main)
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| Challenge: | Existing research focuses predominantly on specific fields, which results in the need for clarity on linguistic markers associated with deception. |
| Approach: | They propose a domain-independent fraud detection benchmark with 100,000 honest and misleading statements in seven domains and a parameter-efficient finetuning adapter to improve tuning methods. |
| Outcome: | The proposed adapter outperforms all competition on the DIFrauD benchmark and is able to predict the performance of the proposed model. |
Domain-aware and Co-adaptive Feature Transformation for Domain Adaption Few-shot Relation Extraction (2024.lrec-main)
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| Challenge: | Existing approaches to relation extraction focus on the source domain, which makes it difficult to accurately transfer useful knowledge to the target domain. |
| Approach: | They propose a domain-aware and co-adaptive feature transformation approach to address these issues by leveraging the target domain distribution features to guide the domain-based feature transformations. |
| Outcome: | The proposed method outperforms existing models and achieves state-of-the-art performance on a benchmark dataset. |
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing approaches to domain adaptation fail to generalize well on unknown test data. |
| Approach: | They propose a backdoor adjustment-based causal model to disentangle domain-specific and domain-invariant representations that play essential roles in tackling domain shift. |
| Outcome: | The proposed model disentangles domain-specific and domain-invariant representations that play essential roles in tackling domain shift. |
Domain Transferable Semantic Frames for Expert Interview Dialogues (2024.lrec-main)
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| Challenge: | a dataset of interview dialogues with experts in the domains of culinary and gardening domains is used to structure domain-specific knowledge in expert interviews. |
| Approach: | They analyze interview dialogues with experts in the culinary and gardening domains to understand their domain knowledge structure. |
| Outcome: | The proposed framework is effective in eliciting critical skills in domains, the authors show . they use domain-agnostic labels to identify domain-specific knowledge in interviews . |
Do Neural Language Models Inferentially Compose Concepts the Way Humans Can? (2024.lrec-main)
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| Challenge: | a new study shows that language models and humans may rely on different approaches to represent and compose lexical items across sentence structure. |
| Approach: | They propose to use a dataset to test the performance of neural language models and humans on inferentially driven conceptual compositions. |
| Outcome: | The proposed model elicits probability estimates for a noun in a minimally composed phrase . RoBERTa, BERT-large, and GPT-2 exhibited the closest resemblance to human responses . |
DORE: A Dataset for Portuguese Definition Generation (2024.lrec-main)
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| Challenge: | Definition modelling (DM) is the task of automatically generating a dictionary definition of a specific word. |
| Approach: | They propose to create a dataset for definition modelling for Portuguese with 100,000 definitions and evaluate several deep learning based DM models on the dataset. |
| Outcome: | The proposed dataset will facilitate research and study of Portuguese in wider contexts. |
DOSA: A Dataset of Social Artifacts from Different Indian Geographical Subcultures (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) are increasingly being integrated with social applications . large data sets are limited in their representation of information and do not capture knowledge from the Web . |
| Approach: | They propose a gamified framework that uses collective sensemaking to collect artifacts from 19 different Indian geographic subcultures and benchmark four popular LLMs. |
| Outcome: | The proposed framework is based on 260 participants from 19 different Indian geographic subcultures and shows that it can be used across regional sub-cultures. |
DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation (2024.lrec-main)
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| Challenge: | Existing methods for learning relational knowledge are replay-based and prioritize data uniformly . a pronounced bias towards new tasks can be caused by the introduction of new tasks . |
| Approach: | They propose a framework that decouples the process of prior information preservation and new knowledge acquisition. |
| Outcome: | Extensive experiments show that the framework outperforms baselines across two datasets. |
Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering (2024.lrec-main)
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Yuan Gao, Yiheng Zhu, Yuanbin Cao, Yinzhi Zhou, Zhen Wu, Yujie Chen, Shenglan Wu, Haoyuan Hu, Xinyu Dai
| Challenge: | Open Domain Multi-Hop Question Answering (ODMHQA) is one of the most challenging tasks in Natural Language Processing (NLP) |
| Approach: | They propose a mechanism that leverages the intrinsic capabilities of Large Language Models to judge whether the generated answers are off-topic. |
| Outcome: | The proposed method reduces the occurrence of off-topic answers by nearly 13%, improving the performance in Exact Match (EM) by nearly 3% compared to the baseline method without the Dr3 mechanism. |
DRAMA: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering (2024.lrec-main)
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| Challenge: | TableTextQA tasks require tabular and textual data, gaining increasing attention . however, row-based approaches suffer from limitations such as lack of interaction between rows . |
| Approach: | They propose a method that incorporates an interaction mechanism among multiple rows . Empirical results demonstrate that the proposed method is effective . |
| Outcome: | Empirical results show that the proposed model is effective on tabFact and HybridQA datasets. |
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain (2024.lrec-main)
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Yanis Labrak, Adrien Bazoge, Oumaima El Khettari, Mickael Rouvier, Pacome Constant Dit Beaufils, Natalia Grabar, Béatrice Daille, Solen Quiniou, Emmanuel Morin, Pierre-Antoine Gourraud, Richard Dufour
| Challenge: | Existing benchmarks for pre-trained language models are limited to only a few languages . a limited number of tasks are evaluated on non-standardized protocols . |
| Approach: | They propose to aggregate diverse downstream tasks into a benchmark to assess PLMs' qualities . they evaluate 8 pre-trained masked language models on general and biomedical-specific data . |
| Outcome: | The proposed benchmark assesses pre-trained language models on 20 diversified tasks. |
Dual Complex Number Knowledge Graph Embeddings (2024.lrec-main)
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| Challenge: | Existing knowledge graph embedding methods fail to model non-commutative composition patterns . extending to such sophisticated spaces increases the amount of parameters, which greatly reduces the parameter efficiency. |
| Approach: | They propose a new knowledge graph embedding method that maps entities to the dual complex number space and represents relations as rotations in 2D space via dual complex multiplication. |
| Outcome: | Experiments on multiple benchmark knowledge graphs show that the proposed method improves link prediction and path query answering. |
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triple Extraction (ASTE) is an advanced natural language processing task. |
| Approach: | They propose a Dual Encoder: Exploiting the potential of Syntactic and Semantic model which maximizes syntactical and semantic relationships among words. |
| Outcome: | The proposed model surpasses the current state-of-the-art on public benchmarks and shows that it is highly efficient. |
DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented Dialogues (2024.lrec-main)
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| Challenge: | User Simulators are used to train task-oriented dialogue systems . traditional training paradigms rely on human-engineered agendas resulting in generated responses that lack diversity and spontaneity. |
| Approach: | They propose a framework that leverages large language models to generate diverse responses . they use two LLMs to generate and verify responses, which are preferred by users . |
| Outcome: | The proposed framework produces responses that exhibit diversity and are preferred by human users. |
Dynamic Knowledge Prompt for Chest X-ray Report Generation (2024.lrec-main)
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| Challenge: | Existing methods for radiology report generation fail to incorporate prior knowledge . data bias, sparse features of chest X-ray image make it difficult to generate reports . |
| Approach: | They propose a dynamically integrated framework for chest X-ray report generation that incorporates pulmonary lesion knowledge at the instance-level. |
| Outcome: | The proposed framework can dynamically incorporate pulmonary lesion knowledge at instance-level to facilitate report generation. |
Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation (2024.lrec-main)
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| Challenge: | In this paper, we analyze the problem of multi-reward reinforcement learning to optimize for multiple text qualities for natural language generation. |
| Approach: | They propose to use multi-reward reinforcement learning to optimize for multiple text qualities for natural language generation by using bandits. |
| Outcome: | The proposed techniques outperform existing naive and bandit baselines, showcasing their potential for enhancing language models. |
Dynamic Spatial-Temporal Aggregation for Skeleton-Aware Sign Language Recognition (2024.lrec-main)
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| Challenge: | Current sign language recognition methods use spatial graphs and temporal modules to capture spatial and temporal features, but their spatial graph modules are typically built on fixed graph structures. |
| Approach: | They propose a new spatial architecture that captures input-sensitive joint relationships and a temporal module to model multi-scale temporal information to capture complex human dynamics. |
| Outcome: | The proposed method achieves state-of-the-art accuracy on four large-scale SLR benchmarks. |
EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance Detection (2024.lrec-main)
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| Challenge: | EcoVerse is an annotated English Twitter dataset of 3,023 tweets . mainstream NLP tasks dominate the scene, but environmental impacts remain unstudied . |
| Approach: | They propose an annotation scheme for Eco-Relevance Classification, Stance Detection and an original approach for Environmental Impact Analysis. |
| Outcome: | The proposed scheme produces consistent annotations of high quality . the dataset is made freely available to stimulate further research . |
ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights (2024.lrec-main)
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| Challenge: | Prior case retrieval datasets do not simulate a realistic setting because they use complete case documents while only masking references to prior cases. |
| Approach: | They propose a prior case retrieval dataset based on judgements from the European Court of Human Rights which explicitly separate facts from arguments and exhibit precedential practices. |
| Outcome: | The proposed datasets do not simulate a realistic setting and expose queries to spurious patterns left behind by citation masks, potentially short-circuiting a comprehensive understanding of case facts and legal principles. |
EDDA: An Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection (2024.lrec-main)
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| Challenge: | Existing methods for enhancing text or data are limited by lack of logical connections between generated texts and training data. |
| Approach: | They propose an encoder-decoder data augmentation framework that combines large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships. |
| Outcome: | The proposed framework significantly improves over state-of-the-art methods on benchmark datasets while enabling interpretable rationale-based learning. |
EDEN: A Dataset for Event Detection in Norwegian News (2024.lrec-main)
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Samia Touileb, Jeanett Murstad, Petter Mæhlum, Lubos Steskal, Lilja Charlotte Storset, Huiling You, Lilja Øvrelid
| Challenge: | EDEN is the first dataset annotated with event information at the sentence level for the Norwegian language. |
| Approach: | They propose to annotate Norwegian news text and transcribed speech using ACE event schema. |
| Outcome: | The proposed dataset is the first annotated dataset for Norwegian, with a language-specific annotation process. |
Educational Dialogue Systems for Visually Impaired Students: Introducing a Task-Oriented User-Agent Corpus (2024.lrec-main)
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Elisa Di Nuovo, Manuela Sanguinetti, Pier Felice Balestrucci, Luca Anselma, Cristian Bernareggi, Alessandro Mazzei
| Challenge: | a corpus of real-world dialogues between visually impaired users and an agent is described . the corpus is part of a larger research project aimed at developing tools for easier access to educational content for visually impaired people. |
| Approach: | They describe a corpus consisting of real-world dialogues between users and a task-oriented conversational agent . they report the results of a classification experiment on the annotated corpus and an additional experiment to assess the annotation capabilities of three large language models. |
| Outcome: | The proposed corpus is part of a larger research project aimed at improving visual aids for visually impaired users. |
EEE-QA: Exploring Effective and Efficient Question-Answer Representations (2024.lrec-main)
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| Challenge: | Current approaches to question answering rely on pre-trained language models like RoBERTa. |
| Approach: | They propose a pooling approach that embeds all answer candidates with the question . they also propose enabling cross-reference between answer choices . |
| Outcome: | The proposed methods improve throughput and memory efficiency with little sacrifice in performance. |
Eesthetic: A Paralex Lexicon of Estonian Paradigms (2024.lrec-main)
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| Challenge: | Eesthetic is a comprehensive Estonian noun and verb lexicon . it documents 5475 nouns inflecting for 28 paradigm cells and 5076 verbs inflection for 51 cells. |
| Approach: | They propose to use Ekilex to generate an Estonian noun and verb lexicon with a set of rules for automatic transcription. |
| Outcome: | The Estonian lexicon is based on the Ekilex database and is openly accessible . it contains a total of 452885 inflected forms and is structured and formatted as a set of CSV tables linked by formal relationships. |
Effective Distillation of Table-based Reasoning Ability from LLMs (2024.lrec-main)
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| Challenge: | Existing work on table-based reasoning distillation has focused on smaller models with limited performance. |
| Approach: | They propose a table-based reasoning distillation approach to distill LLMs into smaller models . their results show that a 220 million parameter model fine-tuned using distilled data improves performance . |
| Outcome: | The proposed model improves on a scientific table-to-text generation dataset and surpasses specific LLMs. |
Effective Integration of Text Diffusion and Pre-Trained Language Models with Linguistic Easy-First Schedule (2024.lrec-main)
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| Challenge: | Existing noise schedules in text diffusion models do not take into account the linguistic differences among tokens, which violates the easy-first policy for text generation. |
| Approach: | They propose to use a pre-trained decoder to convert denoised embedding vectors into natural language instead of the widely used rounding operation. |
| Outcome: | The proposed model outperforms existing models on the E2E dataset and five controllable tasks on the discrete nature of text data. |
Efficiency and Effectiveness in Task-Oriented Dialogue: On Construction Repetition, Information Rate, and Task Success (2024.lrec-main)
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| Challenge: | Repetition of constructions in task-oriented dialogue can have negative and positive effects on information rate and delivery, but is also predictive of task success. |
| Approach: | They investigate the role that efficiency and effectiveness play in speakers’ repetition of shared word sequences, or constructions, in task-oriented dialogue. |
| Outcome: | The results show that repeating constructions has negative and positive effects on information rate and delivery and that information rate managing strategies are predictive of task success. |
Efficient AMR Parsing with CLAP: Compact Linearization with an Adaptable Parser (2024.lrec-main)
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| Challenge: | Abstract Meaning Representation (AMR) parsers face efficiency challenges because of their large model size and computational time, which limit their accessibility within the research community. |
| Approach: | They propose a novel linearization system that simplifies encoding and reduces the number of tokens by between 40% and 50%. |
| Outcome: | The proposed system reduces the number of tokens by 40% and 50% while maintaining high performance while reducing training and inference times. |
Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering (2024.lrec-main)
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Kexuan Sun, Nicolaas Paul Jedema, Karishma Sharma, Ruben Janssen, Jay Pujara, Pedro Szekely, Alessandro Moschitti
| Challenge: | Existing approaches to QA over textual data are based on a "retrieve-then-generate" pipeline. |
| Approach: | They propose a "triple-level" labeling strategy that infers fine-grained labels and trains a re-ranker to improve relevance of retrieved triples. |
| Outcome: | The proposed pipeline improves on prior KGQA systems by 5.56% Exact Match. |
EFTNAS: Searching for Efficient Language Models in First-Order Weight-Reordered Super-Networks (2024.lrec-main)
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| Challenge: | Depending on the size of transformer-based models, they can be restricted from deployment in resource-constrained environments. |
| Approach: | They propose to combine neural architecture search and network pruning techniques to generate and train weight-sharing super-networks that contain efficient transformer-based models. |
| Outcome: | The proposed model achieves high-performing, high-performance subnetworks on the general language understanding evaluation and the Stanford Question Answering Dataset. |
Eliciting Motivational Interviewing Skill Codes in Psychotherapy with LLMs: A Bilingual Dataset and Analytical Study (2024.lrec-main)
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Xin Sun, Jiahuan Pei, Jan de Wit, Mohammad Aliannejadi, Emiel Krahmer, Jos T.P. Dobber, Jos A. Bosch
| Challenge: | Motivational interviewing (MI) is an essential, directive, client-centered counseling technique. |
| Approach: | They propose a bilingual dataset of MI conversations in English and Dutch . they propose an approach to elicit MISC expertise from Large language models . |
| Outcome: | The proposed approach yields results aligned with expert annotations and maintains consistent performance across languages. |
ELLEN: Extremely Lightly Supervised Learning for Efficient Named Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) tasks require an amount of annotations that are unrealistic for many real-world applications. |
| Approach: | They propose a semi-supervised named entity recognition method that blends language models with linguistic rules. |
| Outcome: | The proposed method outperforms most existing semi-supervised methods under the same supervision settings commonly used in the literature. |
EMAD: A Bridge Tagset for Unifying Arabic POS Annotations (2024.lrec-main)
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| Challenge: | Existing tagsets for Arabic are difficult to combine due to the diversity of their features. |
| Approach: | They propose an Arabic Extended Morphological Analysis and Disambiguation Tagset which facilitates conversion and unification of Arabic tagsets. |
| Outcome: | The proposed tagset facilitates conversion and unification of different tagsetes used to annotate Arabic corpora. |
Emancipating Event Extraction from the Constraints of Long-Tailed Distribution Data Utilizing Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for EE depend on manual annotations, which are expensive and scarce. |
| Approach: | They propose to transform the event extraction task into multi-turn dialogues and a novel method for generating high-quality data. |
| Outcome: | The proposed methods significantly improve existing models’ performance with various paradigms and structures, especially on tail types. |
EMOLIS App and Dataset to Find Emotionally Close Cartoons (2024.lrec-main)
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| Challenge: | EMOLIS Dataset contains annotated emotional transcripts of scenes from Walt Disney cartoons at the same time as physiological signals from spectators. |
| Approach: | They propose to use annotated emotional transcripts of scenes from Walt Disney cartoons to generate an emotional distance between videos. |
| Outcome: | The proposed dataset is suitable for all audiences and autistic people who have difficulties to recognize and express emotions. |
EmoProgress: Cumulated Emotion Progression Analysis in Dreams and Customer Service Dialogues (2024.lrec-main)
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| Challenge: | Emotion analysis often involves categorization of isolated textual units, but these are parts of longer discourses, like dialogues or stories. |
| Approach: | They propose a novel annotation setup for emotion categorization corpora that allows to annotate the emotion up to the annotated sentence. |
| Outcome: | The proposed annotation setup allows to answer the question which emotion is presumably experienced at a specific moment in time. |
EmoPrompt-ECPE: Emotion Knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction (2024.lrec-main)
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| Challenge: | Existing methods for Emotion-cause pair extraction (ECPE) do not distinguish between the emotion-caused pairs that belong to different types of emotions, limiting their applicability. |
| Approach: | They propose an Emotion-cause pair extraction method which integrates the implicit knowledge of cause clauses into a prompt template and extends the emotion labels to categories with an external emotion word base. |
| Outcome: | The proposed method extracts all potential emotion clauses and corresponding cause clauses from unannotated documents. |
Emotags: Computer-Assisted Verbal Labelling of Expressive Audiovisual Utterances for Expressive Multimodal TTS (2024.lrec-main)
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| Challenge: | We show that ascribing verbal descriptions to expressive audiovisual utterances is efficient and efficient. |
| Approach: | They propose a web app for ascribing verbal descriptions to expressive audiovisual utterances. |
| Outcome: | The proposed system can be deployed at a large scale to efficiently collect relevant verbal descriptions. |
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions (2024.lrec-main)
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| Challenge: | Emotion analysis (EA) is a rapidly growing field in natural language processing . there is no consensus on scope, direction, or methods for EA . |
| Approach: | They review 154 relevant NLP papers on emotion analysis from the last decade . they ask: how are EA tasks defined in NLP? what are the most prominent emotion frameworks and which emotions are modeled? |
| Outcome: | The authors examine 154 relevant NLP papers on emotion analysis from the last decade . they find that there is no consensus on scope, direction, or methods . |
Emotion Recognition in Conversation via Dynamic Personality (2024.lrec-main)
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| Challenge: | Existing approaches to ERC focus on conversational contexts, but focus on static personality. |
| Approach: | They propose a model that considers the dynamic personality of speakers during conversations. |
| Outcome: | The proposed model outperforms existing models on three benchmark conversational datasets. |
EmoTrans: Emotional Transition-based Model for Emotion Recognition in Conversation (2024.lrec-main)
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| Challenge: | Emotions are causally transmitted among communication participants, facilitating comprehension of intricate changes in emotional states during the conversation. |
| Approach: | They propose an Emotional Transition-based Emotion Recognizer that captures ET features in an emotional conversation by concatenating the most recent utterances with their corresponding speakers. |
| Outcome: | The proposed model is sensitive to emotions and captures ET features in the sample. |
EmpCRL: Controllable Empathetic Response Generation via In-Context Commonsense Reasoning and Reinforcement Learning (2024.lrec-main)
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| Challenge: | Existing studies lack the perception of fine-grained dialogue emotion propagation, and have limitations in reasoning about the intentions of users on cognition, which affect the quality of empathetic response. |
| Approach: | They propose to use commonsense reasoning and reinforcement learning to generate empathetic response based on in-context commonsensing and contextual reasoning to broaden cognitive boundaries. |
| Outcome: | The proposed model outperforms state-of-the-art models in automatic and human evaluation. |
Empowering Low-Resource Regional Languages with Lexicons : A Comparative Study of NLP Tools for Morphosyntactic Analysis (2024.lrec-main)
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| Challenge: | a lack of human and financial resources makes integrating lexicon information to low-resource languages challenging. |
| Approach: | They propose to use a bilingual lexicon to integrate lexical information to low-resource language . they compare a lexiconal approach to a neural approach that uses a larger lexicone . |
| Outcome: | The proposed approach improves POS tagging while using different lexicon sizes. |
Empowering Oneida Language Revitalization: Development of an Oneida Verb Conjugator (2024.lrec-main)
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| Challenge: | Oneida is a polysynthetic North American Indigenous language . currently, there are only 45 native speakers in Canada and 102 worldwide . |
| Approach: | They propose to use the Gramble framework to develop a digital Oneida verb conjugator that can demonstrate its users the correct conjugations of verbs and let learners generate practice materials tailored to their unique learning trajectories. |
| Outcome: | The proposed system can demonstrate its users the correct conjugations of verbs and can also let learners generate practice materials tailored to their unique learning trajectories. |
Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings (2024.lrec-main)
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| Challenge: | Existing approaches to augment LLMs with Knowledge Graphs (KGs) Knowledge-intensive tasks are prone to errors and require a large amount of knowledge to be understood. |
| Approach: | They propose a framework for augmenting LLMs through Knowledge Graphs (KGs) they propose KGs can be used to enhance performance in knowledge-intensive tasks . |
| Outcome: | Experimental results show that a small domain-specific KG can benefit from a performance boost in downstream tasks when linked to a substantial general-purpose KG. |
Empowering Tree-structured Entailment Reasoning: Rhetorical Perception and LLM-driven Interpretability (2024.lrec-main)
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| Challenge: | Existing models for science question answering lack a framework for entailment trees . ambiguities and similarities between science facts complicate the fact retrieval process . |
| Approach: | They propose a framework for building entailment trees for science question answering . they propose to infuse knowledge that bridges the gap between reasoning types and rhetorical relations . |
| Outcome: | The proposed framework improves retrieval capabilities, understanding relationships and generating intermediate conclusions. |
Emstremo: Adapting Emotional Support Response with Enhanced Emotion-Strategy Integrated Selection (2024.lrec-main)
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| Challenge: | Emstremo aims to achieve strategic control of emotional alignment by perceiving and responding to the user’s emotions. |
| Approach: | They propose to integrate strategies and emotions into a conversational emotional support agent called Emstremo that aims to achieve strategic control of emotional alignment by perceiving and responding to the user’s emotions. |
| Outcome: | Emstremo achieves strategic control of emotional alignment by perceiving and responding to the user’s emotions. |
Encoding Gesture in Multimodal Dialogue: Creating a Corpus of Multimodal AMR (2024.lrec-main)
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| Challenge: | Abstract Meaning Representation (AMR) was designed to represent sentence meaning in English text, but recent research has explored its adaptation to broader domains, including documents, dialogues, spatial information, cross-lingual tasks, and gesture. |
| Approach: | They propose to annotate a multimodal (speech and gesture) AMR corpus in a task-based setting and capture coreference relationships across modalities. |
| Outcome: | The proposed corpus captures coreference relationships across modalities, enabling fine-grained analysis of how gesture and natural language interact. |
Endowing Neural Language Learners with Human-like Biases: A Case Study on Dependency Length Minimization (2024.lrec-main)
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| Challenge: | Comparing the behavior of models with that of human learners can reveal which aspects affect the emergence of this preference. |
| Approach: | They propose to add three factors to the standard neural-agent language learning and communication framework to make the simulation more realistic. |
| Outcome: | The proposed conditions can contribute to a small but significant learning advantage for listeners of verb-initial languages. |
End-to-end Parsing of Procedural Text into Flow Graphs (2024.lrec-main)
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| Challenge: | Existing flow graph parsers lack sufficient annotated data to train them . a lack of annotation can cause costly training, and poor flow graph training results in a large improvement. |
| Approach: | They propose a multi-task framework that performs tagging and graph generation simultaneously . they take advantage of the abundance of unlabelled recipes and generate noisy silver annotations . |
| Outcome: | The proposed model can unify the input representation and use compact encoders, resulting in small models with significantly fewer parameters than existing models. |
Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis (2024.lrec-main)
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| Challenge: | Aspect-category-based sentiment analysis (ACSA) is a popular approach for identifying aspect categories and predicting their sentiments. |
| Approach: | They propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) to capture contexts across the whole review and to help the implicit aspect and sentiment identification. |
| Outcome: | The proposed network decouples multiple aspects and sentiment features and achieves state-of-the-art (SOTA) performance. |
Enhanced Facet Generation with LLM Editing (2024.lrec-main)
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| Challenge: | Existing studies have shown that search engines can recognize facets of a user's query. |
| Approach: | They propose to use large language models to enhance the facets of a query to generate facets from a search engine. |
| Outcome: | The proposed model can predict facets by taking only queries as input without a search engine. |
Enhance Robustness of Language Models against Variation Attack through Graph Integration (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) are used in many NLP applications but their vulnerability to adversarial attacks can lead to false or misleading information being distributed. |
| Approach: | They propose a method to incorporate a Chinese character variation graph into pre-trained language models to increase their robustness against character variation attacks in Chinese content. |
| Outcome: | The proposed method outperforms existing language models in combating adversarial attacks in Chinese content. |
Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant advances in code generation through the ‘Chain-of-Thought’ prompting technique. |
| Approach: | They propose a framework which aims to transfer LLMs’ reasoning capabilities to smaller models through distillation. |
| Outcome: | The proposed framework improves the smaller model's code generation performance by over 130% on the APPS benchmark. |
Enhancing Court View Generation with Knowledge Injection and Guidance (2024.lrec-main)
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| Challenge: | Existing methods of natural language generation (NLG) rely on the extensive parameters of pretrained language models (PLMs) but their effectiveness may be compromised by insufficient domain-specific knowledge. |
| Approach: | They propose a knowledge-injected prompt encoder to incorporate domain knowledge during the training stage, thereby reducing computational overhead. |
| Outcome: | The proposed approach outperforms established baselines on real-world data in responsivity of claims and in the ability to transfer domain knowledge. |
Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information (2024.lrec-main)
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| Challenge: | Existing cross-document event coreference resolution models lack the ability to capture long-distance dependencies. |
| Approach: | They propose to construct document-level Rhetorical Structure Theory trees and cross-document Lexical Chains to model structural and semantic information of documents. |
| Outcome: | The proposed model outperforms baseline models on English and Chinese datasets by large margins. |
Enhancing Distantly Supervised Named Entity Recognition with Strong Label Guided Lottery Training (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) requires a limited quantity of strongly labeled data . weakly labeles can be acquired through distant supervision, but can cause noise . |
| Approach: | They propose a noise-robust learning framework where safe parameters can be identified . they conduct extensive experiments on multiple datasets and show it outperforms the state-of-the-art methods. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on weakly labeled data. |
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation (2024.lrec-main)
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| Challenge: | Existing methods to augment textual data are limited due to the discrete characteristics of the textual dataset. |
| Approach: | They propose a decision-boundary-aware data augmentation strategy to enhance robustness using pretrained language models by shifting latent features closer to the decision boundary and reconstruction to generate an ambiguous version with a soft label. |
| Outcome: | The proposed method performs better than existing methods and is extensible with curriculum data augmentation. |
Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation (2024.lrec-main)
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Ge Gao, Jongin Kim, Sejin Paik, Ekaterina Novozhilova, Yi Liu, Sarah T. Bonna, Margrit Betke, Derry Tanti Wijaya
| Challenge: | Existing methods for classifying discrete emotions from news headlines have been limited to using headlines. |
| Approach: | They propose to use people’s free-text explanations to classify emotions elicited by news headlines to generate emotion explanations from headlines. |
| Outcome: | The proposed method improves on methods that only use headlines and train a pretrained model for explanation generation. |
Enhancing Few-Shot Topic Classification with Verbalizers. a Study on Automatic Verbalizer and Ensemble Methods (2024.lrec-main)
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Quang Anh Nguyen, Nadi Tomeh, Mustapha Lebbah, Thierry Charnois, Hanene Azzag, Santiago Cordoba Muñoz
| Challenge: | Pretrained language models are increasingly being used for many tasks. |
| Approach: | They propose to use verbalizers to help interpret masked word distributions into output predictions. |
| Outcome: | The proposed approach outperforms models trained with individual templates while using significantly less resources. |
Enhancing Hindi Feature Representation through Fusion of Dual-Script Word Embeddings (2024.lrec-main)
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| Challenge: | Pretrained language models often neglect the integration of different scripts within a language, constraining their ability to capture richer semantic information. |
| Approach: | They propose a dual-script enhanced feature representation method for Hindi . they combine features from Devanagari and Romanized Hindi Roberta . |
| Outcome: | The proposed method improves model performance across multiple natural language processing tasks. |
Enhancing Image-to-Text Generation in Radiology Reports through Cross-modal Multi-Task Learning (2024.lrec-main)
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| Challenge: | Image-to-text generation relies on independent models for image understanding and natural language generation, which often exhibit a semantic gap between visual and textual information. |
| Approach: | They propose a multi-task learning framework to leverage both visual and non-imaging data for generating radiology reports. |
| Outcome: | The proposed framework improves performance over single-task baselines across language generation metrics and mitigates overfitting in auxiliary tasks. |
Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue (2024.lrec-main)
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| Challenge: | Existing approaches to knowledge retrieval are limited by the knowledge base encoder, but our work focuses on the knowledge-base encoder. |
| Approach: | They propose an approach that utilizes topic modeling on the knowledge base to improve retrieval accuracy and as a result, improve response generation. |
| Outcome: | The proposed approach can improve retrieval and generation performance on two datasets. |
Enhancing Knowledge Selection via Multi-level Document Semantic Graph (2024.lrec-main)
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| Challenge: | Existing methods view knowledge selection as a sentence matching or classification. Existing techniques can’t capture the semantic relationships within complex documents. |
| Approach: | They propose a method that can construct multi-level document semantic graph from the grounding document and store semantic relationships within the documents effectively. |
| Outcome: | The proposed method can store semantic relationships within documents effectively and efficiently and achieve state-of-the-art results on public datasets. |
Enhancing Large Language Models through Transforming Reasoning Problems into Classification Tasks (2024.lrec-main)
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Tarun Raheja, Raunak Sinha, Advit Deepak, Will Healy, Jayanth Srinivasa, Myungjin Lee, Ramana Kompella
| Challenge: | Existing approaches to improve LLMs' reasoning capabilities for constraint satisfaction problems (CSPs) are needed to solve complex tasks. |
| Approach: | They propose a method that leverages the LLM's ability to decide when to call a function from a set of logical-linguistic primitives, each of which can interact with a local “scratchpad” memory and logical inference engine. |
| Outcome: | The proposed method improves the reasoning capabilities of large language models for constraint satisfaction problems by 40% over baselines. |
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. |
| Approach: | They propose to make Large Language Models (LLMs) operating in 0-shot or few-shot settings as efficient as 0- shot text classifiers by leveraging a small number of samples. |
| Outcome: | The proposed model is able to perform better on multiple datasets than existing models on 0-shot or few-shot settings. |
Enhancing Parameter-efficient Fine-tuning with Simple Calibration Based on Stable Rank (2024.lrec-main)
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| Challenge: | Existing methods for lightweight fine-tuning are ineffective in low-resource settings but fail in high-resourced settings, leading to unreliable outcomes. |
| Approach: | They propose a calibration strategy that takes into account the inherent variance of generalization ability in model components and potential changes during the fine-tuning process. |
| Outcome: | The proposed calibration improves GLUE score by 3.1 points over the previous calibration method. |
Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction (2024.lrec-main)
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| Challenge: | Existing methods for keyphrase extraction lack the ability to utilize keyphrase information, which may result in biased results. |
| Approach: | They propose a keyphrase extraction task that leverages the supervised Variational Information Bottleneck to guide the text diffusion process for generating enhanced keyphrase representations. |
| Outcome: | The proposed keyphrase extraction model outperforms existing methods on open domain keyphrase extractor benchmark and scientific domain dataset. |
Enhancing Scientific Document Summarization with Research Community Perspective and Background Knowledge (2024.lrec-main)
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| Challenge: | Scientific paper summarization is the focus of recent research . prevailing summarizing methods involve selective extraction of content from abstract, introduction, and conclusion segments within the target articles. |
| Approach: | They propose a model that incorporates references and citations to capture the impact of the document on the research community. |
| Outcome: | The proposed model generates extractive and abstractive summaries in parallel and improves their performance when considering the standard metrics. |
Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling (2024.lrec-main)
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| Challenge: | Chain of thought (CoT) is used for complex reasoning problems, but hallucinations are a problem in multimodal CoT. |
| Approach: | They propose a method to generate soft negative samples with different semantics to mitigate hallucinations in multimodal CoT. |
| Outcome: | The proposed method mitigates hallucinations in multimodal CoT by using soft negative sampling. |
Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems (2024.lrec-main)
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| Challenge: | Currently, machine translation systems cater to high-resource languages (HRLs), while low-resourced languages (LRLs) like Taiwanese Hokkien are relatively under-explored. |
| Approach: | They propose to use a pre-trained LLaMA 2-7B model specialized in Traditional Mandarin Chinese to leverage orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin China. |
| Outcome: | The proposed model bridges the gap between Taiwanese Hokkien and other low-resource languages by using a pre-trained LLaMA 2-7B model and a monolingual corpus. |
Enhancing Text-to-SQL Capabilities of Large Language Models through Tailored Promptings (2024.lrec-main)
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| Challenge: | Large language models with prompting have achieved encouraging results on many natural language processing tasks due to the absence of task-tailored promptings. |
| Approach: | They propose three promptings specifically designed for Text-to-SQL: SL-prompt, CC-promped, and SL+CC prompt. |
| Outcome: | The proposed promptings achieve execution accuracy of 86.2% and test-suite accuracy of 76% . the granularity of schema linking and the order of clause generation have great impact on performance, which are considered little in previous research. |
Enhancing Translation Ability of Large Language Models by Leveraging Task-Related Layers (2024.lrec-main)
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| Challenge: | Experimental validation shows that adjusting task-related layers significantly improves performance on translation tasks while maintaining stability and accuracy on other tasks. |
| Approach: | They propose to adjust task-related layers in large models to better harness their machine translation capabilities by revealing the structure and characteristics of attention weights through singular value decomposition. |
| Outcome: | The proposed method reduces computational resource consumption and catastrophic forgetting while maintaining stability and accuracy on other tasks. |
Enhancing Unrestricted Cross-Document Event Coreference with Graph Reconstruction Networks (2024.lrec-main)
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| Challenge: | Event Coreference Resolution is a discourse-oriented task that requires a lot of computational power. |
| Approach: | They propose a method to combine traditional mention-pair coreference models with a graph reconstruction algorithm. |
| Outcome: | The proposed method is highly robust in low-data settings and scales with increases in performance for the underlying mention-pair models. |
Enhancing Writing Proficiency Classification in Developmental Education: The Quest for Accuracy (2024.lrec-main)
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| Challenge: | Existing literature raises concerns about automated assessment tools like Accuplacer’s narrow representation of the writing process. |
| Approach: | They propose to use machine-learning to annotate college essays for machine/deep learning. |
| Outcome: | The proposed method improves the classification accuracy of 100 college-intending students’ essays against human raters. |
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic (2024.lrec-main)
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| Challenge: | Experimental evaluations of large language models demonstrate the efficacy of enhanced reasoning by logic. |
| Approach: | They propose a framework that uses symbolic logic to verify and rectify reasoning steps by steps. |
| Outcome: | The proposed framework improves the zero-shot chain-of-thought reasoning ability of large language models by verifying and rectifying the reasoning steps step by step. |
Enough Is Enough! a Case Study on the Effect of Data Size for Evaluation Using Universal Dependencies (2024.lrec-main)
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| Challenge: | a recent study suggests that large datasets require large amounts of simulated data and scores to perform model evaluation. |
| Approach: | They propose to use dependency parsing to optimize annotation efforts by using a sample size of 5,000 tokens for in-language in-domain datasets. |
| Outcome: | The proposed method can be applied to other tasks, including classification and translation tasks. |
Enriching a Time-Domain Astrophysics Corpus with Named Entity, Coreference and Astrophysical Relationship Annotations (2024.lrec-main)
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| Challenge: | Existing corpora for astrophysical natural language processing are limited to Named Entity Recognition tasks, leaving a gap in resource diversity. |
| Approach: | They propose to expand astroECR to cover named entities, coreferences, annotations related to aastrphysical relationships, and normalizing celestial object names. |
| Outcome: | The proposed model extends the time-domain astrophysics corpus to include named entities, coreferences, and annotations related to aastrphysical relationships. |
Enriching Word Usage Graphs with Cluster Definitions (2024.lrec-main)
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| Challenge: | Existing word usage graphs lack human interpretability of senses. |
| Approach: | They propose to enrich existing word usage graphs with cluster labels functioning as sense definitions. |
| Outcome: | The proposed dataset matches the definitions chosen from WordNet by two baseline systems. |
Ensembles of Hybrid and End-to-End Speech Recognition. (2024.lrec-main)
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| Challenge: | Existing methods to combine hybrid and end-to-end ASRs with confidence measures are limited and neither can achieve optimal performance. |
| Approach: | They propose to combine the hybrid Kaldi-based Automatic Speech Recognition system with the end-to-end wav2vec 2.0 XLS-R ASR using confidence measures. |
| Outcome: | The proposed method reduces the word error rate by 14% on the primary test set and 20% on other noisy and imbalanced data. |
EpiGEN: An Efficient Multi-Api Code GENeration Framework under Enterprise Scenario (2024.lrec-main)
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| Challenge: | Existing approaches to large language models fail to meet expectations for code generation tasks . existing approaches are faced with drawbacks of high resource consumption and inadequate handling of multi-API tasks. |
| Approach: | They propose an Efficient multi-Api code GENeration framework that uses private APIs to pre-train LLMs. |
| Outcome: | The proposed framework shows good acceptability and readability on single-GPU tasks compared to fully fine-tuned LLMs with a larger number of parameters. |
EpLSA: Synergy of Expert-prefix Mixtures and Task-Oriented Latent Space Adaptation for Diverse Generative Reasoning (2024.lrec-main)
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| Challenge: | Existing models for diverse generative reasoning struggle to generate multiple unique and plausible results. |
| Approach: | They propose a model based on expert-prefix mixtures and task-oriented latent space adaptation for diverse generative reasoning. |
| Outcome: | The proposed model outperforms existing models on three types of generative reasoning tasks. |
EPOQUE: An English-Persian Quality Estimation Dataset (2024.lrec-main)
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| Challenge: | Existing human labeled QE datasets are limited to limited language pairs . a small subset of the proposed dataset can improve its performance by 8% . |
| Approach: | They propose to use an English-Persian QE dataset with manually annotated direct assessment labels to evaluate translation quality estimation models. |
| Outcome: | The proposed dataset improves on two state-of-the-art QE models by 8% . the proposed dataset contains 1000 translated sentences from English to Persian . |
EROS:Entity-Driven Controlled Policy Document Summarization (2024.lrec-main)
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| Challenge: | a privacy policy is a crucial component of any organization that allows it to legally collect, process, store, and/or distribute personal data. |
| Approach: | They propose to use a policy-document summarization dataset to enforce the summaries to include critical privacy-related entities and organization’s rationale in collecting those entities. |
| Outcome: | The proposed model improves over baselines and qualitatively evaluates the proposed model on human and qualitative data. |
Error Analysis of NLP Models and Non-Native Speakers of English Identifying Sarcasm in Reddit Comments (2024.lrec-main)
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| Challenge: | sarcasm detection remains an issue for both humans and natural language processing models . |
| Approach: | They analysed 300 comments from the FigLang 2020 Reddit Dataset and 39 non-native speakers of English to see if they were sarcastic. |
| Outcome: | The results show that the models and models have similar performance and weaknesses when the comments include political topics or are phrased as questions. |
Error-Robust Retrieval for Chinese Spelling Check (2024.lrec-main)
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| Challenge: | Chinese Spelling Check (CSC) aims to detect and correct spelling errors in Chinese texts . current methods may not fully leverage existing datasets, resulting in insufficient annotated data . |
| Approach: | They propose a plug-and-play retrieval method with error-robust information for Chinese Spelling Check . they employ multimodal representations that fuse phonetic, morphologic, and contextual information . |
| Outcome: | The proposed method improves on the SIGHAN benchmarks on Chinese spelling check (CSC) the proposed method is based on training data and lacks adequate parallel corpora . |
EsCoLA: Spanish Corpus of Linguistic Acceptability (2024.lrec-main)
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| Challenge: | Acceptability is one of the general language understanding evaluation benchmarks (GLUE) probing tasks . EsCoLA consists of 11,174 sentences and their acceptability judgements as found in well-known Spanish reference grammars. |
| Approach: | They propose to use a corpus of linguistic acceptability (ESCoLA) EsCoLA consists of 11,174 sentences and their acceptability judgements . |
| Outcome: | The proposed task is based on 11,174 sentences and their acceptability judgements as found in well-known Spanish reference grammars. |
ESCP: Enhancing Emotion Recognition in Conversation with Speech and Contextual Prefixes (2024.lrec-main)
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| Challenge: | Emotion Recognition in Conversation (ERC) aims to analyze the speaker’s emotional state in a conversation. |
| Approach: | They propose to combine a directed acyclic graph and contextual prefixes to model historical utterances in a conversation and incorporate a contextual prefixed containing sentiment and semantics of historical . |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance on several public benchmarks. |
ESDM: Early Sensing Depression Model in Social Media Streams (2024.lrec-main)
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| Challenge: | Existing approaches to use social media data for depression detection are based on traditional risk detection (TRD) and early risk detection of depression (ERD). |
| Approach: | They propose a model that uses two modules: classification with partial information module (CPI) and decision for classification moment module (DMC) and an early detection loss function. |
| Outcome: | The proposed model outperforms benchmarks in both accuracy and accuracy with evolving partial data. |
Esposito: An English-Persian Scientific Parallel Corpus for Machine Translation (2024.lrec-main)
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| Challenge: | Existing scientific corpus for English-Persian language pairs is lacking . supervised neural machine translation requires millions of parallel sentences . |
| Approach: | They propose a parallel corpus called Esposito which contains 3.5 million parallel sentences . they also propose 'test sets' that might serve as a baseline for future studies . |
| Outcome: | The proposed system improves the baseline on average by 7.6 and 8.4 BLEU scores for English-Persian language pairs. |
Estimating Lexical Complexity from Document-Level Distributions (2024.lrec-main)
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| Challenge: | Existing methods for complexity estimation are limited to entire documents . health assessment tools are too short for existing methods to apply . |
| Approach: | They propose a two-step approach for estimating lexical complexity that does not rely on pre-annotated data. |
| Outcome: | The proposed method is tested on the Norwegian language and compares with other assessment tools. |
Estimating the Causal Effects of Natural Logic Features in Transformer-Based NLI Models (2024.lrec-main)
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| Challenge: | a number of studies have reported high accuracies in NLP tasks due to simple heuristics and dataset artifacts. |
| Approach: | They use a case where two words/terms occur in a shared context to construct a causal diagram . they also investigate the robustness to irrelevant changes and sensitivity to impactful changes of Transformers . |
| Outcome: | The proposed method bolsters the fact that similar benchmark accuracy scores may be observed for models that exhibit very different behaviour. |
Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language We Prompt Them in (2024.lrec-main)
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| Challenge: | Ethical reasoning is a crucial skill for Large Language Models (LLMs). However, moral values are not universal, but rather influenced by language and culture. |
| Approach: | They extend the study of ethical reasoning of LLMs by (CITATION) to a multilingual setup using six languages: English, Spanish, Russian, Chinese, Hindi, and Swahili. |
| Outcome: | The proposed model is based on a multilingual setup in English, Spanish, Russian, Chinese, Hindi, and Swahili. |
EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation (2024.lrec-main)
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Atnafu Lambebo Tonja, Israel Abebe Azime, Tadesse Destaw Belay, Mesay Gemeda Yigezu, Moges Ahmed Ah Mehamed, Abinew Ali Ayele, Ebrahim Chekol Jibril, Michael Melese Woldeyohannis, Olga Kolesnikova, Philipp Slusallek, Dietrich Klakow, Seid Muhie Yimam
| Challenge: | Low-resource languages are lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. |
| Approach: | They propose to use multilingual large language models for five Ethiopian languages and a benchmark dataset to evaluate their performance. |
| Outcome: | The proposed models outperform existing models in five Ethiopian languages and a benchmark dataset for various downstream NLP tasks. |
European Language Grid: One Year after (2024.lrec-main)
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Georg Rehm, Stelios Piperidis, Dimitris Galanis, Penny Labropoulou, Maria Giagkou, Miltos Deligiannis, Leon Voukoutis, Martin Courtois, Julian Moreno-Schneider, Katrin Marheinecke
| Challenge: | The European Language Grid (ELG) is a cloud platform for the whole European Language Technology community. |
| Approach: | The article provides an overview of the current state of ELG in terms of user adoption and number of language resources and technologies available in early 2024. |
| Outcome: | The European Language Grid (ELG) is a cloud platform for the whole European Language Technology community. |
Evaluating Automatic Subtitling: Correlating Post-editing Effort and Automatic Metrics (2024.lrec-main)
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| Challenge: | Existing metrics for automatic subtitling are not yet fully explored. |
| Approach: | They propose to use machine translation metrics to measure post-editing effort in automatic subtitling to collect data on product-, process- and participant-based data. |
| Outcome: | The proposed metrics correlate with measures of post-editing effort in automatic subtitling. |
Evaluating ChatGPT against Functionality Tests for Hate Speech Detection (2024.lrec-main)
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| Challenge: | Large language models like ChatGPT have shown a great promise in detecting hate speech, but they lack the capability to perform in a holistic fashion. |
| Approach: | They evaluate the ChatGPT model's strengths and weaknesses by performing functional tests across 11 languages to uncover their weaknesses. |
| Outcome: | The proposed model performs poorly across 11 languages and is based on functional tests. |
Evaluating Code-Switching Translation with Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown they can match or surpass finetuned models on many natural language processing tasks. |
| Approach: | They propose to use in-context learning and pivot translation to improve code-switching translation. |
| Outcome: | The proposed models show strong ability for cross-lingual understanding in a code-switching setting. |
Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labels (2024.lrec-main)
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| Challenge: | Existing methods to evaluate gender bias in PLMs focus on one label out of three labels, such as neutral. |
| Approach: | They propose a bias evaluation method for PLMs that considers all the three labels of NLI task and then defines a measure based on the corresponding label output. |
| Outcome: | The proposed method can distinguish biased, incorrect inferences from non-biased incorrect infertility better than baseline, resulting in a more accurate bias evaluation. |
Evaluating Generative Language Models in Information Extraction as Subjective Question Correction (2024.lrec-main)
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| Challenge: | Modern large language models (LLMs) perform poorly in elementary tasks like relation extraction and event extraction due to two issues in conventional evaluation methods. |
| Approach: | They propose a method to evaluate large language models by incorporating a human annotation schema. |
| Outcome: | The proposed evaluation method improves matching between model outputs and golden labels. |
Evaluating Performance of Pre-trained Word Embeddings on Assamese, a Low-resource Language (2024.lrec-main)
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| Challenge: | Word embeddings are not explored in high-resource languages such as Assamese, where resources are limited. |
| Approach: | They propose to use assamese pre-trained word embeddings for sequence labeling tasks such as Parts-of-speech and Named Entity Recognition to evaluate their performance. |
| Outcome: | The proposed embeddings outperform the existing methods on Parts-of-speech and Named Entity Recognition tasks. |
Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency (2024.lrec-main)
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| Challenge: | generative LLMs have been known for overcorrection where results obtain higher recall measures than precision measures. |
| Approach: | They propose to use generative LLMs to prompt grammatical error correction using a model based on language proficiency to examine the interaction between LLM's performance and L2 language proficiency. |
| Outcome: | The proposed model improves on zero-shot and few-shot prompting and fine-tuning models for grammatical error correction for learners of English as a foreign language based on the different proficiency levels. |
Evaluating Saliency Explanations in NLP by Crowdsourcing (2024.lrec-main)
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| Challenge: | a crowdsourced method to evaluate saliency methods in NLP is proposed . saliencies are difficult for humans to understand, and can cause psychological harm . |
| Approach: | They propose a method to evaluate saliency methods in NLP by crowdsourcing . they recruited 800 crowd workers and empirically evaluated seven salience methods . |
| Outcome: | The proposed method evaluates saliency methods on two datasets using crowdsourced data . it shows that the results are comparable to existing methods on NLP and CV fields . |
Evaluating Self-Supervised Speech Representations for Indigenous American Languages (2024.lrec-main)
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| Challenge: | a recent study focused on the use of self-supervised learning to learn speech representations for indigenous languages . aaron e. scott: the vast linguistic diversity represented by indigenous languages remains unexplored . by expanding the scope of language processing to include indigenous languages, we can foster linguistic inclusivity, he says . |
| Approach: | They benchmark the efficacy of large-scale self-supervised learning models on indigenous American languages. |
| Outcome: | The proposed model can generalize to real-world data, showing strong performance . evaluators found that the model performed better than monolingual models on indigenous languages . |
Evaluating Shortest Edit Script Methods for Contextual Lemmatization (2024.lrec-main)
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| Challenge: | Modern contextual lemmatizers often rely on automatically induced Shortest Edit Scripts (SES) supervised contextual methods are used to perform lemma classification tasks. |
| Approach: | They propose to use masked language encoders to compute shortest edit Scripts (SES) SES is the number of edit operations to transform a word form into its lemma . |
| Outcome: | The proposed model outperforms language-specific models in all evaluation settings with seven languages of different morphological complexity. |
Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Model (2024.lrec-main)
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| Challenge: | Recent advances in generative language modeling applied to discrete speech tokens presented a new avenue for text-to-speech (TTS) synthesis. |
| Approach: | They propose to use generative language modeling to generate text-to-speech (TTS) outputs by a discrete token-based model. |
| Outcome: | The proposed model is rated higher in naturalness and context appropriateness in listening tests compared to a conventional TTS. |
Evaluating the Efficacy of Large Acoustic Model for Documenting Non-Orthographic Tribal Languages in India (2024.lrec-main)
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| Challenge: | Pre-trained Large Acoustic Models have been shown to improve performance in spoken languages . however, their potential for novel under-resourced languages is not fully known . |
| Approach: | They propose to use pre-trained Large Acoustic Models to document under-resourced languages . they use scripts from languages that hold a prominent presence in the geographical regions . |
| Outcome: | The proposed model can document under-resourced languages in the electronic domain . the model can be used to document languages with a written script . |
Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation (2024.lrec-main)
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Matthias Sperber, Ondřej Bojar, Barry Haddow, Dávid Javorský, Xutai Ma, Matteo Negri, Jan Niehues, Peter Polák, Elizabeth Salesky, Katsuhito Sudoh, Marco Turchi
| Challenge: | a meta-analysis of human evaluation for speech translation has not been conducted . noisy data and segmentation mismatches are challenges for automatic metrics . |
| Approach: | They propose an evaluation strategy based on automatic resegmentation and direct assessment with segment context. |
| Outcome: | The proposed evaluation strategy is robust and scores well-correlated with other types of human judgements. |
Evaluating the Potential of Language-family-specific Generative Models for Low-resource Data Augmentation: A Faroese Case Study (2024.lrec-main)
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| Challenge: | generative language models have shown promising results for translation in zero, one, and fewshot learning settings, among other types of tasks. |
| Approach: | They propose to prompt a generative language model for the Nordic languages for Faroese to English translation in a zero, one, and few-shot setting and challenge its Farose language understanding capabilities on a small dataset. |
| Outcome: | The proposed model can translate Faroese to English in a zero, one, and few-shot setting and then use it to create an annotated Farose semantic textual similarity (STS) dataset. |
Evaluating the Quality of a Corpus Annotation Scheme Using Pretrained Language Models (2024.lrec-main)
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Furkan Akkurt, Onur Gungor, Büşra Marşan, Tunga Gungor, Balkiz Ozturk Basaran, Arzucan Özgür, Susan Uskudarli
| Challenge: | Pretrained language models and large language models are increasingly used to assist in a variety of natural language processing tasks. |
| Approach: | They propose to use pretrained language models and large language models to evaluate their quality in natural language processing. |
| Outcome: | The proposed annotation scheme (2.11) yields sentences with higher success rate than the previous one. |
Evaluating Topic Model on Asymmetric and Multi-Domain Financial Corpus (2024.lrec-main)
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| Challenge: | Recent research attempts to quantify the exposure of market assets to various risks from text and how assets react if the risk materializes itself. |
| Approach: | They propose two new metrics to evaluate the behavior of different types of topic models with respect to pitfalls previously mentioned about document risk distribution extraction. |
| Outcome: | The proposed models can be used to extract unbiased risk information from financial domain data and correct coherence imbalances. |
Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings (2024.lrec-main)
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| Challenge: | Sentence embeddings produced by pretrained language models are high dimensional (ca. 1024-4096) this is problematic when representing large numbers of sentences in memory- or compute-constrained devices. |
| Approach: | They propose to use Principal Component Analysis to reduce the dimensionality of sentence embeddings produced by pretrained language models to reduce their complexity. |
| Outcome: | The proposed methods reduce the dimensionality of sentence embeddings by 50% without incurring significant loss in performance in multiple downstream tasks. |
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations (2024.lrec-main)
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| Challenge: | We compare webcam-based eye-tracking recordings with human-annotated rationales to evaluate importance scores. |
| Approach: | They compare webcam-based eye-tracking recordings with attention-based importance scores for 4 different multilingual Transformer-based language models. |
| Outcome: | The proposed method is comparable to human rationales in linguistic analysis. |
Evaluating Word Expansion for Multilingual Sentiment Analysis of Parliamentary Speech (2024.lrec-main)
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| Challenge: | Recent efforts to create and format data sets of parliamentary speech material have facilitated cross-lingual comparisons and highlighted the need for methods that are computationally efficient and language-agnostic. |
| Approach: | They propose a word expansion method for sentiment lexicon generation that leverages word embeddings and vector similarity to expand synonym seed lists with domain-specific terms from the speech corpora. |
| Outcome: | The proposed method is compared with other multilingual lexica and is highly sensitive to processing and scoring techniques. |
Evaluating Workflows for Creating Orthographic Transcripts for Oral Corpora by Transcribing from Scratch or Correcting ASR-Output (2024.lrec-main)
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| Challenge: | Automated speech recognition systems can reduce transcription effort, but few studies have evaluated this potential. |
| Approach: | They compare efforts for manual transcription vs. automatic correction of ASR-output . they use audio recordings from varying settings to create orthographic transcripts . |
| Outcome: | The proposed methods reduce transcription time by 7 times on average for selected data and transcription conventions compared with corrected transcripts . the more complex the primary data, the more time has to be spent on corrections - the paper concludes a similar study could be conducted in 2022 . |
Evaluation Dataset for Lexical Translation Consistency in Chinese-to-English Document-level Translation (2024.lrec-main)
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| Challenge: | Existing studies on document-level neural machine translation (NMT) assume that all repeated source words should be translated consistently. |
| Approach: | They construct a test set of 310 bilingual news articles to evaluate lexical translation consistency. |
| Outcome: | The proposed test sets show that translation consistency is consistent across multiple languages. |
Evaluation of Really Good Grammatical Error Correction (2024.lrec-main)
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| Challenge: | emergence of large language models has highlighted the shortcomings of evaluation methods . evaluators often use grammatical error correction (GEC) to correct language errors at multiple levels . |
| Approach: | They perform a comprehensive evaluation of various GEC systems using Swedish learner texts . they suggest using human post-editing to analyze amount of change required to reach native-level human performance . |
| Outcome: | The proposed evaluations outperform existing methods for grammatical error correction in Swedish . the results highlight the shortcomings of existing evaluation methods . |
Event-enhanced Retrieval in Real-time Search (2024.lrec-main)
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| Challenge: | Existing embedding-based retrieval models face the "semantic drift" problem . a low adoption rate of retrieval results is evident in real-time search scenarios . |
| Approach: | They propose an embedding-based retrieval approach that enhances real-time retrieval performance by adding contrastive learning to the dual-encoder model. |
| Outcome: | The proposed approach improves the dual-encoder model of traditional EBR. |
Event Extraction in Basque: Typologically Motivated Cross-Lingual Transfer-Learning Analysis (2024.lrec-main)
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| Challenge: | Using a multilingual language model, Event Extraction tasks require humans to follow complicated guidelines and follow complicated rules. |
| Approach: | They propose a multilingual multilingual language model that is trained in a source language and applied to a target language. |
| Outcome: | The proposed model is based on a multilingual event extraction dataset for Basque . it shows that the shared linguistic characteristic between source and target languages does have an impact on transfer quality. |
EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs (2024.lrec-main)
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| Challenge: | Existing frameworks for leveraging background knowledge of narratives are limited. |
| Approach: | They propose a framework to ground free-texts to eventuality-centric KGs for narrative reasoning . their framework is based on a set of probabilistic probabilistic models that are grounded in the real world . |
| Outcome: | The proposed framework outperforms baseline models while providing interpretable evidence. |
Event Representation Learning with Multi-Grained Contrastive Learning and Triple-Mixture of Experts (2024.lrec-main)
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| Challenge: | Existing methods for learning event representations fail to capture hidden feature information between events. |
| Approach: | They propose a method that extends the random masked language model by incorporating a specialized MLM to capture different grammatical structures within events. |
| Outcome: | The proposed method outperforms baselines in hard similarity and transitive sentence similarity tasks, highlighting the superiority of the proposed method. |
Every Verb in Its Right Place? A Roadmap for Operationalizing Developmental Stages in the Acquisition of L2 German (2024.lrec-main)
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| Challenge: | Developmental stages are a linguistic concept claiming that language learning progresses in an ordered, step-like manner. |
| Approach: | They propose to translate a linguistic specification into a computational procedure that can assign clauses to a developmental stage based on verb placement. |
| Outcome: | The proposed system lacks a coherent linguistic specification of developmental stages . it also lacks the ability to translate the specification into a computational procedure based on verb placement. |
Evidence-guided Inference for Neutralized Zero-shot Transfer (2024.lrec-main)
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| Challenge: | Existing knowledge transfer frameworks that use label skewness to neutralize biased language are costly and impractical when it comes to scarcely labeled data. |
| Approach: | They propose a neutralized Knowledge Transfer framework to equip pre-trained language models with neutralized transferability. |
| Outcome: | The proposed framework shows that it can be used to train pre-trained models with neutralized transferability . it is compared with baselines with a zero-shot cross-domain transfer setting . |
EVil-Probe - a Composite Benchmark for Extensive Visio-Linguistic Probing (2024.lrec-main)
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| Challenge: | Visual question answering, image-text retrieval and retrieving image patches that match an expression are some of the tasks visio-linguistic models show impressive performance on. |
| Approach: | They propose a composite benchmark that processes existing probing datasets into a unified format and reorganizes them based on the linguistic categories they probe. |
| Outcome: | The proposed benchmark is challenging for all models as they are sensitive to linguistic categories and only handles nouns. |
EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries (2024.lrec-main)
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| Challenge: | Large Language Models excel at the Winograd Schema Challenge, but struggle with instances that feature minor alterations or rewording. |
| Approach: | They propose an open-source platform that harnesses a human-in-the-loop approach to create a dynamic dataset tailored to such altered WSC instances. |
| Outcome: | The proposed model outperforms existing models in the Winograd Schema Challenge (WSC) a human-in-the-loop approach allows for a dynamic dataset tailored to such altered instances. |
Evolving Knowledge Distillation with Large Language Models and Active Learning (2024.lrec-main)
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| Challenge: | Existing studies have focused on the direct use of large language models for text generation and labeling, without fully exploring their potential to comprehend the target task and acquire valuable knowledge. |
| Approach: | They propose to distill the knowledge of large language models into smaller models by generating annotated data. |
| Outcome: | The proposed method improves the performance of small domain models while enhancing the ability of large language models. |
Examining Temporalities on Stance Detection towards COVID-19 Vaccination (2024.lrec-main)
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| Challenge: | Existing studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. |
| Approach: | They evaluate a range of transformer-based models using chronological and random splits of social media data to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination. |
| Outcome: | The proposed models show that the models performed better with chronological and random splits than with random split models. |
Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets (2024.lrec-main)
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| Challenge: | Past research has indicated that content-based rumor detection models perform less effectively on unseen rumors. |
| Approach: | They propose to use data split strategies to minimize the effects of temporal concept drift in static datasets during the training of rumor detection methods. |
| Outcome: | The proposed model over-relys on the information derived from the rumors’ source post and overlooks the significant role that contextual information can play. |
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have revolutionized the field of natural language processing and artificial intelligence, creating new SOTAs and reaching human-level language understanding performance on a series of tasks and benchmarks. |
| Approach: | They propose to use an algorithm test set sourced from Introduction to Algorithm to assess LLMs' code execution abilities. |
| Outcome: | The proposed model can execute programs described in natural language as long as no heavy numeric computation is involved. |
Experimental versus In-Corpus Variation in Referring Expression Choice (2024.lrec-main)
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| Challenge: | Against our expectations, the divergence is greatest between the corpus and the GPT model. |
| Approach: | They compare the results of three studies to examine how well the corpus can model variation . they find that experimental methodology introduces substantial noise . |
| Outcome: | The results show that the corpus can model variation captured from the corpuse and RE form choices made during experiments. |
Experiments on Speech Synthesis for Teochew, Can Taiwanese Help ? (2024.lrec-main)
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| Challenge: | a recent uptick in interest in Teochew from heritage speakers of the diaspora has led to the development of a text-to-speech system. |
| Approach: | They develop a Teochew Text-to-Speech system to respond to the needs of the diaspora . they also use Taiwanese Hokkien to assess the contribution of available resources . |
| Outcome: | The proposed system is based on a Teochew Text-to-Speech system . the system is built on Taiwanese Hokkien, the closest language with a significant body of resources . |
Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling (2024.lrec-main)
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| Challenge: | Existing models that generate complex questions do not explain reasoning process behind generated multi-hop questions. |
| Approach: | They propose an end-to-end question rewriting model that increases question complexity through sequential rewrite. |
| Outcome: | The proposed model generates complex questions that require multi-step reasoning over multiple documents. |
Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings (2024.lrec-main)
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| Challenge: | Currently, prompt-based models are gaining popularity due to their easier adaptability in low-resource settings. |
| Approach: | They analyze attribution scores extracted from prompt-based models w.r.t. plausibility and faithfulness and compare them with attribution score extracted from fine-tuned models and large language models. |
| Outcome: | The proposed model outperforms attention and Integrated Gradients in plausibility and faithfulness, while fine-tuning models are harder to explain in low-resource settings. |
Explicit over Implict: Explicit Diversity Conditions for Effective Question Answer Generation (2024.lrec-main)
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| Challenge: | Recent pretrained and large language model-based QAG methods suffer from redundant generation of QA pairs, affecting downstream QA systems. |
| Approach: | They propose to use explicit diversity conditions to generate diverse question-answer synthetic data by focusing on spatial aspects, question types, and entities. |
| Outcome: | The proposed diversity conditions significantly increase diversity in QA generation over existing diversity techniques. |
Exploring and Mitigating Shortcut Learning for Generative Large Language Models (2024.lrec-main)
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| Challenge: | Recent large language models (LLMs) have incredible instruction-following capabilities while maintaining strong task completion ability. |
| Approach: | They propose a framework to encourage LLMs to Forget Spurious correlations and Learn from In-context information. |
| Outcome: | The proposed framework can mitigate shortcut learning by forging spurious correlations and learning from in-context information. |
Exploring BERT-Based Classification Models for Detecting Phobia Subtypes: A Novel Tweet Dataset and Comparative Analysis (2024.lrec-main)
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| Challenge: | Phobias are characterized by an intense and irrational fear of specific objects, situations, or activities despite there being no real risk or only a minor threat involved. |
| Approach: | They propose to use a dataset of 811,569 English tweets from user timelines spanning 102 phobia subtypes over six months to classify users into 65 specific phobias. |
| Outcome: | The proposed dataset includes 47,614 self-diagnosed phobia users and a high f1 score for binary classification and multi-class classification. |
Exploring Geometric Representational Disparities between Multilingual and Bilingual Translation Models (2024.lrec-main)
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| Challenge: | Existing work shows that limited modeling capacity is a major contributor to reduced performance in multilingual models. |
| Approach: | They investigate the isotropy of multilingual model decoder representations using intrinsic dimensionality and IsoScore to measure how they utilize the dimensions in their underlying vector space. |
| Outcome: | The proposed model decoder representations are less isotropic and occupy fewer dimensions than bilingual models. |
Exploring Interpretability of Independent Components of Word Embeddings with Automated Word Intruder Test (2024.lrec-main)
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| Challenge: | Independent Component Analysis (ICA) is an algorithm for finding separate sources in a mixed signal. |
| Approach: | They propose to use ICA to analyze word embeddings to quantify interpretability . they propose to automate word intruder test to quantify the components . |
| Outcome: | The proposed algorithm can be used to find semantic features of words . it can be combined to find words that have features associated with the components . |
Exploring Neural Topic Modeling on a Classical Latin Corpus (2024.lrec-main)
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| Challenge: | Using topic modeling, it is possible to study Latin literature through methods and tools that support distant reading. |
| Approach: | They propose to use topic modeling to investigate thematic distribution of Latin corpus . they train, optimize and compare two neural models to evaluate which performs better . |
| Outcome: | The proposed model is compared with two neural models with a Classical Latin corpus and shows that it is coherent and interpretable. |
Exploring Pathological Speech Quality Assessment with ASR-Powered Wav2Vec2 in Data-Scarce Context (2024.lrec-main)
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| Challenge: | Current studies only gain good results on simple tasks such as binary classification due to data scarcity. |
| Approach: | They propose to use the pre-trained Wav2Vec2 architecture for both SSL, and ASR as feature extractor in speech assessment. |
| Outcome: | The proposed system achieves the best results on the HNC dataset using 95 training samples. |
Exploring the Emotional Dimension of French Online Toxic Content (2024.lrec-main)
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| Challenge: | Emotion annotations can be used to analyze content and can be applied to content analysis. |
| Approach: | They propose to use a corpus annotation scheme to annotate three online data sets composed of extremist, sexist and hateful messages respectively. |
| Outcome: | The proposed method can provide new insights for content analysis and stronger empirical background for automatic content detection. |
Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers across Diseases (2024.lrec-main)
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| Challenge: | Clinical trials are pivotal in medical research, and NLP can enhance their success with application in recruitment. |
| Approach: | They examine the generalizability of eligibility classification across clinical trials . they use an extensive cancer dataset to examine how well models can handle criteria . |
| Outcome: | The proposed model can handle criteria commonly found in non-cancer trials, but struggle with criteria disproportionately prevalent in cancer trials. |
Exploring the Impact of Human Evaluator Group on Chat-Oriented Dialogue Evaluation (2024.lrec-main)
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| Challenge: | Evaluator groups such as domain experts, university students, and crowdworkers have been used to assess and compare chat-oriented dialogue systems. |
| Approach: | They analyze the impact of evaluator groups on dialogue system evaluation by testing 4 state-of-the-art dialogue systems using 4 distinct evaluer groups. |
| Outcome: | The proposed evaluations show that the evaluator group impact is not seen for Pairwise, and that it is beneficial for certain metrics. |
Exploring the Potential of Large Language Models (LLMs) for Low-resource Languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali Language (2024.lrec-main)
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| Challenge: | Large Language Models excel in various tasks like Named Entity Recognition and Part-of-Speech tagging. |
| Approach: | They propose to use large language models to perform NLP tasks such as Named Entity Recognition and Part-of-Speech tagging in Nepali. |
| Outcome: | The proposed models perform better than other approaches for Nepali NER and POS tagging tasks. |
Exploring the Synergy of Dual-path Encoder and Alignment Module for Better Graph-to-Text Generation (2024.lrec-main)
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| Challenge: | KG-to-text generation model lacks explicit graph-text alignment strategy due to discrepancy between textual and structure information. |
| Approach: | They propose a synergetic knowledge graph-to-text model with a dual-path encoder, alignment module and guidance module to solve these problems. |
| Outcome: | The proposed model achieves competitive performance on three benchmark datasets. |
Exploring the Usability of Persuasion Techniques for Downstream Misinformation-related Classification Tasks (2024.lrec-main)
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| Challenge: | systematically explore the predictive power of features derived from Persuasion Techniques detected in texts for different tasks of interest for media analysis. |
| Approach: | They propose a set of meaningful features aimed at capturing persuasiveness of a text . they also assess the discriminatory power of these features in different text classification tasks . |
| Outcome: | The proposed features can be applied to detecting mis/disinformation, fake news, propaganda, partisan news and conspiracy theories. |
Extending AZee with Non-manual Gesture Rules for French Sign Language (2024.lrec-main)
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| Challenge: | Currently, Sign Languages (SLs) are under-resourced and are difficult to develop. |
| Approach: | They propose to extend AZee to formally represent Sign Language discourses, but also to animate them with a virtual signer. |
| Outcome: | The proposed model allows to formally represent Sign Language discourses, but also to animate them with a virtual signer. |
Extending the Discourse Analysis Tool Suite with Whiteboards for Visual Qualitative Analysis (2024.lrec-main)
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Tim Fischer, Florian Schneider, Fynn Petersen-Frey, Anja Silvia Mollah Haque, Isabel Eiser, Gertraud Koch, Chris Biemann
| Challenge: | Existing web-based platform for qualitative discourse analysis is limited to text, image, audio, video, and other multimodal data. |
| Approach: | They propose to extend existing web-based platform for digital qualitative discourse analysis with a new extension, Whiteboards, which offers a customizable view of the material and a wide range of actions that enable new ways of interacting with it. |
| Outcome: | The proposed extension facilitates reflection of the research process through sampling maps, creation of actor networks, and refining code taxonomies. |
Extracting Biomedical Entities from Noisy Audio Transcripts (2024.lrec-main)
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Nima Ebadi, Kellen Morgan, Adrian Tan, Billy Linares, Sheri Osborn, Emma Majors, Jeremy Davis, Anthony Rios
| Challenge: | Named Entity Recognition (NER) is particularly affected by noise, often termed the ASR-NLP gap. |
| Approach: | They propose a dataset to bridge the ASR-NLP gap in the biomedical domain by extracting adverse drug reactions and mentions of entities from the Brief Test of Adult Cognition by Telephone (BTACT) exam. |
| Outcome: | The proposed method can clean 2,000 clean and noisy recordings and eliminate errors using zero-shot and few-shot methods. |
Extracting Financial Events from Raw Texts via Matrix Chunking (2024.lrec-main)
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| Challenge: | Event Extraction (EE) is widely used in the Chinese financial field to provide valuable structured information. |
| Approach: | They propose a task which extracts financial events from raw texts and an efficient method called MACK. |
| Outcome: | The proposed method is fault-tolerant and can visualize interactions among text components. |
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods (2024.lrec-main)
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Yujuan Fu, Giridhar Kaushik Ramachandran, Nicholas J. Dobbins, Namu Park, Michael Leu, Abby R. Rosenberg, Kevin Lybarger, Fei Xia, Özlem Uzuner, Meliha Yetisgen
| Challenge: | Social determinants of health (SDoH) are often studied in the electronic health record (EHR) however, there are difficulties in documenting SDoH in a tabular format due to the lack of a comprehensive SDoh tool. |
| Approach: | They propose to annotate social history sections from 1,260 clinical notes from pediatric patients within the University of Washington (UW) hospital system. |
| Outcome: | The proposed corpus captures ten distinct health determinants including living and economic stability, prior trauma, education access, substance use history, and mental health with an overall annotator agreement of 81.9 F1. |
Eye-Tracking Features Masking Transformer Attention in Question-Answering Tasks (2024.lrec-main)
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| Challenge: | Eye movement features are considered to be direct signals reflecting human attention distribution with a low cost to obtain, inspiring researchers to augment language models with eye-tracking (ET) data. |
| Approach: | They select first fixation duration (FFD) and total reading time (TRT) as the cognitive signals to guide Transformer attention in question-answering tasks. |
| Outcome: | The proposed models improve but compromise stability when augmenting with ET data. |
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)
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| Challenge: | Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient. |
| Approach: | They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve. |
| Outcome: | The proposed approach improves on three SLT datasets conducted under the gloss-free setting. |
FaGANet: An Evidence-Based Fact-Checking Model with Integrated Encoder Leveraging Contextual Information (2024.lrec-main)
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| Challenge: | Existing evidence-based fact-checking efforts are time-consuming and challenging . however, relying on surface patterns of claims makes it difficult to identify subtle connections between claims and evidence. |
| Approach: | They propose a model that leverages sentence-level attention and graph attention network to enhance accuracy and fusing claims and evidence information for accurate identification of even well-disguised data. |
| Outcome: | The proposed model improves accuracy and state-of-the-art in the evidence-based fact-checking task. |
FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods for aspect-based sentiment analysis are limited and integrating with existing techniques is difficult. |
| Approach: | They propose a framework that utilizes in-context learning as a feature-aware mechanism that facilitates adaptive learning in multi-domain ABSA tasks. |
| Outcome: | The proposed framework achieves significant performance improvements in multiple domains compared to baselines, increasing F1 by 2.07% on average. |
FAIRification of LeiLanD (2024.lrec-main)
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| Challenge: | LeiLanD is a searchable catalogue of language datasets collected at LUCL and other institutes of Leiden University. |
| Approach: | They propose to use a standardised metadata format called CMDI to improve the findability of Leiden language datasets. |
| Outcome: | The proposed catalogue has enhanced the findability and accessibility of incredibly diverse datasets. |
FalAI: A Dataset for End-to-end Spoken Language Understanding in a Low-Resource Scenario (2024.lrec-main)
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Andres Pineiro-Martin, Carmen Garcia-Mateo, Laura Docio-Fernandez, Maria del Carmen Lopez-Perez, Jose Gandarela-Rodriguez
| Challenge: | End-to-end (E2E) Spoken Language Understanding systems extract structured information from speech signals using a single model. |
| Approach: | They propose to use a dataset to extract structured information from speech signals . they define splits for noisy audio, hesitant audio and audio where sentence has changed . |
| Outcome: | The proposed model exploits acoustic information and avoids cascading errors . falAI dataset is the largest public SLU dataset in Galician and first to be obtained in low-resource scenario. |
Fast Adaptation via Prompted Data: An Efficient Cross-Domain Fine-tuning Method for Large Language Models (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have been successful in a variety of natural language understanding tasks, but domain discrepancies between the downstream task and the pre-training corpora may have hindered LLMs to excel further in the vertical applications. |
| Approach: | They propose a Fast Adaptation method for LLMs via Prompted Data that integrates downstream text corpora, gold labels and external knowledge sources into a highly controllable prompt. |
| Outcome: | The proposed method bridges the gap between the downstream task and the pre-training corpora and integrates downstream text corpors, gold labels and external knowledge sources into a highly controllable prompt. |
FastSpell: The LangId Magic Spell (2024.lrec-main)
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| Challenge: | Language identification is a crucial component in the automated production of language resources. |
| Approach: | They propose a language identifier that combines fastText and Hunspell to give a second opinion before deciding which language to assign to a text. |
| Outcome: | The proposed language identifier is based on a pre-trained language identifier and a spell checker. |
FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction (2024.lrec-main)
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| Challenge: | Document-level Relation Extraction (DocRE) aims to identify relation labels between entities within a document. |
| Approach: | They propose to fuse constituency and dependency syntax into DocRE to exploit the rich syntax information in the document. |
| Outcome: | The proposed method is able to identify relation labels between entities within a document and is scalable. |
Feature Structure Matching for Multi-source Sentiment Analysis with Efficient Adaptive Tuning (2024.lrec-main)
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| Challenge: | Existing domain matching methods tend to pull all feature instances close, but they are expensive and expensive to update. |
| Approach: | They propose to extract multi-layer features from a large pre-trained model and propose a dynamic parameter fusion module to exploit them for efficient and adaptive tuning. |
| Outcome: | The proposed framework is more robust and generalizable in the multi-source scenario. |
Federated Document-Level Biomedical Relation Extraction with Localized Context Contrast (2024.lrec-main)
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| Challenge: | Existing studies on relation extraction focus on document-level training without sharing raw medical texts. |
| Approach: | They propose a federated framework for relation extraction that enables collaborative training without sharing raw medical texts. |
| Outcome: | The proposed framework extends document-level relation extraction to a federated environment. |
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models (2024.lrec-main)
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| Challenge: | Foundation Models (FMs) have demonstrated success in a wide range of applications, but their optimization often requires access to sensitive data. |
| Approach: | They propose a framework that combines FMs and Federated Learning to enable privacy-preserving and collaborative learning across multiple end-users. |
| Outcome: | The proposed framework combines benefits of FMs and Federated Learning (FL) it enables privacy-preserving and collaborative learning across multiple end-users. |
Few-Shot Learning for Cold-Start Recommendation (2024.lrec-main)
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| Challenge: | Existing methods for cold-start learning and recommendation are brittle to scenarios with few interactions. |
| Approach: | They propose a Few-shot learning method for Cold-Start recommendation that consists of three hierarchical structures that are local and global . |
| Outcome: | The proposed method improves on two public real-world datasets and is stable compared with the state-of-the-art. |
Few-shot Link Prediction on Hyper-relational Facts (2024.lrec-main)
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| Challenge: | Existing methods to predict missing elements in hyper-relational facts require high-quality data. |
| Approach: | They propose a task to predict a missing entity in a hyper-relational fact with limited support instances. |
| Outcome: | The proposed model outperforms existing models on three datasets. |
Few-Shot Multimodal Named Entity Recognition Based on Mutlimodal Causal Intervention Graph (2024.lrec-main)
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| Challenge: | Existing methods for multimodal named entity recognition are limited due to limited resources. |
| Approach: | They propose a Few-shot Multimodal Named Entity Recognition task to address these relation types by constructing a multimodal graph and a new multimodal causal intervention strategy. |
| Outcome: | The proposed model improves on two multimodal named entity recognition datasets. |
Few-shot Named Entity Recognition via Superposition Concept Discrimination (2024.lrec-main)
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| Challenge: | Few-shot named entity recognition (NER) aims to identify entities of target types with limited number of illustrative instances. |
| Approach: | They propose a superposition concept discriminator which solves the intrinsic generalization problem by an active learning paradigm. |
| Outcome: | The proposed model significantly improves few-shot named entity recognition (FS-NER) with minimal additional efforts. |
Few-Shot Relation Extraction with Hybrid Visual Evidence (2024.lrec-main)
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| Challenge: | Existing few-shot relation extraction methods focus on uni-modal information such as text only. Existing methods focus only on text, requiring only a few labeled instances for training. |
| Approach: | They propose a multi-modal few-shot relation extraction model that leverages both textual and visual semantic information to learn a multiple-modal representation jointly. |
| Outcome: | The proposed model leverages both textual and visual semantic information to learn a multi-modal representation jointly. |
Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing graph neural networks (GNNs) have shown promising performance on semantic dependency parsing (SDP) training a high-performing model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labele . |
| Approach: | They propose a syntax-guided graph contrastive learning framework to train GNNs with unlabeled data and fine-tune pre-trained GNN models with few-shot labeled SDP data. |
| Outcome: | The proposed framework achieves promising results when few-shot training samples are available. |
Few-shot Temporal Pruning Accelerates Diffusion Models for Text Generation (2024.lrec-main)
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| Challenge: | Existing acceleration methods for text generation ignore the importance of the distribution of sampling steps, resulting in slow sampling rates. |
| Approach: | They propose a technique to accelerate diffusion models for text generation without additional training by using a Bayesian optimization approach. |
| Outcome: | The proposed technique achieves 400x acceleration even with minimal sampling steps after down to less than 1 minute of optimization yielding a competitive performance even with minimum sampling steps. |
FFSTC: Fongbe to French Speech Translation Corpus (2024.lrec-main)
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| Challenge: | Fongbe to French Speech Translation Corpus is a comprehensive dataset compiled through various collection methods and the efforts of dedicated individuals. |
| Approach: | They introduce the Fongbe to French Speech Translation Corpus (FFSTC) which encompasses approximately 31 hours of collected Fongbbe language content. |
| Outcome: | The proposed corpus includes both transcriptions and voice recordings in Fongbe and French. |
FinCorpus-DE10k: A Corpus for the German Financial Domain (2024.lrec-main)
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| Challenge: | a predominantly German corpus of financial documents is available for the first time . financial text is characterized by a unique vocabulary with implications including sentiment analysis . |
| Approach: | They propose a predominantly German financial corpus comprising 12.5k PDF documents . they hope it will fill this gap and foster further research in the financial domain . |
| Outcome: | The proposed corpus is the first non-email German financial corpus available . it aims to provide insights into financial discourse in the German language and multilingually. |
Finding Educationally Supportive Contexts for Vocabulary Learning with Attention-Based Models (2024.lrec-main)
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| Challenge: | Identifying educationally supportive contexts for vocabulary learning is an important problem to solve for designing effective curricula for contextual word learning. |
| Approach: | They evaluate attention-based approaches to find supportive contexts for vocabulary learning scenarios using an existing benchmark dataset. |
| Outcome: | The proposed model outperforms a generic model and a custom model on a major dataset for educational context support prediction. |
Finding Spoken Identifications: Using GPT-4 Annotation for an Efficient and Fast Dataset Creation Pipeline (2024.lrec-main)
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Maliha Jahan, Helin Wang, Thomas Thebaud, Yinglun Sun, Giang Ha Le, Zsuzsanna Fagyal, Odette Scharenborg, Mark Hasegawa-Johnson, Laureano Moro Velazquez, Najim Dehak
| Challenge: | Existing datasets that are limited to a few dialects, ethnicities, and age groups are not annotated considering these factors. |
| Approach: | They propose a semi-automated dataset creation pipeline that leverages large language models to perform two complex annotation tasks using human annotations as ground truths. |
| Outcome: | The proposed pipeline reduces time required for the filtering and tagging tasks while losing no important information. |
Find-the-Common: A Benchmark for Explaining Visual Patterns from Images (2024.lrec-main)
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| Challenge: | Recent advances in Instruction-fine-tuned Vision and Language Models (IVLMs) have prompted some studies to analyze the reasoning capabilities of IVLMs. |
| Approach: | They introduce a vision and language task for Inductive Visual Reasoning that uses common attributes across visual scenes to find common answers. |
| Outcome: | The proposed model can archive with 48% accuracy on the FTC, compared with state-of-the-art models. |
Fine-grained Classification of Circumstantial Meanings within the Prague Dependency Treebank Annotation Scheme (2024.lrec-main)
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| Challenge: | a formally and semantically based fine-grained classification of circumstantial meanings is proposed for the Czech language . the methodology and principles used are language independent . |
| Approach: | They propose a formally and semantically based fine-grained classification of circumstantial meanings based on Prague Dependency Treebanks examples. |
| Outcome: | The proposed method is language independent and compares with English . it is carried out in the Czech language but not in any other annotation project . |
Fine-Grained Legal Argument-Pair Extraction via Coarse-Grained Pre-training (2024.lrec-main)
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| Challenge: | Current methods conceptualize LAE as a supervised sentence-pair classification problem and necessitate extensive manual annotations. |
| Approach: | They propose a model that focuses on fine-grained alignment of argument pairs building upon coarse-grain complaint-defense pairs. |
| Outcome: | The proposed model outperforms baseline models by 3.7 and 2.4 points on average. |
Fine-Tuning a Pre-Trained Wav2Vec2 Model for Automatic Speech Recognition- Experiments with De Zahrar Sproche (2024.lrec-main)
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| Challenge: | Developing semi-automatic methods of transcription and annotation based on small amounts of annotated data would free field linguists to focus on tasks that are linguistically and relationally significant during fieldwork. |
| Approach: | They propose to use a pre-trained model to tune a generic pre-trainer model to reduce the transcription workload of field linguists. |
| Outcome: | The proposed system reduces the transcription workload of field linguists by averaging a pre-trained model with a language-specific tuning. |
First Steps Towards the Integration of Resources on Historical Glossing Traditions in the History of Chinese: A Collection of Standardized Fǎnqiè Spellings from the Guǎngyùn (2024.lrec-main)
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| Challenge: | fnqiè spellings in the Gungyùn provide pronunciations for more than 20000 characters . historical glossing practices are a crucial part of the pronunciation of ancient Chinese characters - but no integrated resources have been developed on the topic . |
| Approach: | They propose to standardize digital versions of fnqiè spellings in the Gungyùn, one of the early rhyme books in the history of Chinese, providing pronunciations for more than 20000 characters. |
| Outcome: | The proposed resource can predict historical spellings with high precision and shed light on ancient glossing practices. |
Fisher Mask Nodes for Language Model Merging (2024.lrec-main)
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| Challenge: | Pre-trained models are ubiquitous in natural language processing, but individual fine-tuned models require significant overhead in multi-task scenarios. |
| Approach: | They propose a method for fine-tuning pre-trained models for Transformers using Fisher information. |
| Outcome: | The proposed method outperforms Fisher-weighted averaging in a fraction of the computational cost. |
FlattenQuant: Breaking through the Inference Compute-bound for Large Language Models with Per-tensor Quantization (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have demonstrated state-of-the-art accuracies across tasks, but their latency and GPU memory consumption limit their performance. |
| Approach: | They propose a method which flattens the tensor to achieve low bit per-tensori quantization with minimal accuracy loss. |
| Outcome: | The proposed method achieves low bit per-tensor quantization with minimal accuracy loss. |
Flexible Lexicalization in Rule-based Text Realization (2024.lrec-main)
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| Challenge: | GenDR is a text realizer that takes as input a graph-based semantic representation and outputs the corresponding syntactic dependency trees. |
| Approach: | They propose to use a dictionary that maps semantemes to corresponding lexical units in a given language to perform a task in lexiconalization. |
| Outcome: | The proposed module can build a rich semantic dictionary for French. |
FLOR: On the Effectiveness of Language Adaptation (2024.lrec-main)
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Severino Da Dalt, Joan Llop, Irene Baucells, Marc Pamies, Yishi Xu, Aitor Gonzalez-Agirre, Marta Villegas
| Challenge: | Large language models have amply proven their capabilities, but low- and mid-resource languages do not have access to the necessary means to train such models from scratch. |
| Approach: | They use a 26B tokens corpus to further pre-train BLOOM, giving rise to FLOR models. |
| Outcome: | The proposed model achieves consistent gains across Catalan and Spanish tasks. |
FoRC4CL: A Fine-grained Field of Research Classification and Annotated Dataset of NLP Articles (2024.lrec-main)
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| Challenge: | Existing systems for categorising scientific knowledge are lacking in many digital repositories. |
| Approach: | They propose to classify papers in the ACL Anthology using a hierarchical taxonomy of core CL/NLP topics and sub-topics. |
| Outcome: | The proposed corpus of 1,500 ACL Anthology publications is annotated with their main contributions using a hierarchical taxonomy of core CL/NLP topics and sub-topics. |
FORECAST2023: A Forecast and Reasoning Corpus of Argumentation Structures (2024.lrec-main)
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| Challenge: | Existing work on the role of reasoning in forecasting has focused on surface-level features such as linguistic markers, the use of comparison classes, and overall dialectical complexity. |
| Approach: | They propose to use a dataset of such prediction rationales to create a fully automated annotation system that can be used to enhance the argumentation. |
| Outcome: | The proposed dataset provides a uniquely fine-grained and close characterisation of the structure of argumentation with potential impact on forecasting domains from intelligence analysis to investment decision-making. |
FoTo: Targeted Visual Topic Modeling for Focused Analysis of Short Texts (2024.lrec-main)
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| Challenge: | Existing topic models perform a full corpus analysis that treats all topics equally, making them not on target. |
| Approach: | They propose a targeted topic model for semantic short-text embedding which aims to learn all topics and low-dimensional visual representations of documents while preserving relevant topics. |
| Outcome: | The proposed model learns all topics and low-dimensional visual representations while preserving relevant topics in the visualization space. |
FRACAS: a FRench Annotated Corpus of Attribution relations in newS (2024.lrec-main)
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| Challenge: | Quotation extraction is a useful task, but it is not widely studied in other languages. |
| Approach: | They propose to annotate a manually annotated corpus of 1,676 newswire texts in French for quotation extraction and source attribution. |
| Outcome: | The proposed system is compared to the most recent system for quotation extraction in the French language. |
Frame2: A FrameNet-based Multimodal Dataset for Tackling Text-image Interactions in Video (2024.lrec-main)
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Frederico Belcavello, Tiago Timponi Torrent, Ely E. Matos, Adriana S. Pagano, Maucha Gamonal, Natalia Sigiliano, Lívia Vicente Dutra, Helen de Andrade Abreu, Mairon Samagaio, Mariane Carvalho, Franciany Campos, Gabrielly Azalim, Bruna Mazzei, Mateus Fonseca de Oliveira, Ana Carolina Loçasso Luz, Lívia Pádua Ruiz, Júlia Bellei, Amanda Pestana, Josiane Costa, Iasmin Rabelo, Anna Beatriz Silva, Raquel Roza, Mariana Souza, Igor Oliveira
| Challenge: | et al., 2016) describe a multimodal dataset built from a Brazilian travel TV show . frameNet is composed of frames and their associated roles in a network of typed frame-to-frame relations. |
| Approach: | They present a multimodal dataset built from a Brazilian travel TV show annotated for FrameNet categories for both text and image communicative modes. |
| Outcome: | The proposed dataset includes 230 minutes of video annotated for FrameNet categories . the model can be applied to other communicative modes, i.e., images . |
Framed Multi30K: A Frame-Based Multimodal-Multilingual Dataset (2024.lrec-main)
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Marcelo Viridiano, Arthur Lorenzi, Tiago Timponi Torrent, Ely E. Matos, Adriana S. Pagano, Natália Sathler Sigiliano, Maucha Gamonal, Helen de Andrade Abreu, Lívia Vicente Dutra, Mairon Samagaio, Mariane Carvalho, Franciany Campos, Gabrielly Azalim, Bruna Mazzei, Mateus Fonseca de Oliveira, Ana Carolina Luz, Livia Padua Ruiz, Júlia Bellei, Amanda Pestana, Josiane Costa, Iasmin Rabelo, Anna Beatriz Silva, Raquel Roza, Mariana Souza Mota, Igor Oliveira, Márcio Henrique Pelegrino de Freitas
| Challenge: | Recent advances in image-captioning datasets combine image and language to solve a diverse range of tasks. |
| Approach: | They propose a Brazilian Portuguese multimodal-multilingual dataset that extends the Multi30K dataset with 158,915 original Brazilian Portuguese descriptions and 30,104 Brazilian Portuguese translations. |
| Outcome: | The proposed dataset adds 2,677,613 frame evocation labels to the 158,915 English descriptions and to the ones created for Brazilian Portuguese. |
FRASIMED: A Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection (2024.lrec-main)
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Jamil Zaghir, Mina Bjelogrlic, Jean-Philippe Goldman, Soukaïna Aananou, Christophe Gaudet-Blavignac, Christian Lovis
| Challenge: | Existing methods for generating annotations for large datasets are time-consuming and resource-intensive. |
| Approach: | They propose a method for generating translated versions of annotated datasets through crosslingual annotation projection. |
| Outcome: | The proposed method shows that it is efficient and high-quality in the resulting dataset. |
FReND: A French Resource of Negation Data (2024.lrec-main)
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| Challenge: | Negation data are limited by the language models of the BERT-generation, which are still underperforming on tasks and benchmarks featuring negation. |
| Approach: | FReND is a freely available corpus of French language in which negations are hand-annotated by their cues and scopes. |
| Outcome: | FReND is the largest dataset available for french negation analysis . it is a valuable resource for linguistic research and as training data for AI tasks such as negation detection. |
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction (2024.lrec-main)
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| Challenge: | Existing charge prediction methods have shown impressive performance, but they face significant challenges when dealing with confusing charges, such as Snatch and Robbery. |
| Approach: | They propose a novel approach which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge’s reasoning process. |
| Outcome: | The proposed approach maintains exceptional performance in imbalanced label distributions. |
From Laughter to Inequality: Annotated Dataset for Misogyny Detection in Tamil and Malayalam Memes (2024.lrec-main)
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Rahul Ponnusamy, Kathiravan Pannerselvam, Saranya R, Prasanna Kumar Kumaresan, Sajeetha Thavareesan, Bhuvaneswari S, Anshid K.a, Susminu S Kumar, Paul Buitelaar, Bharathi Raja Chakravarthi
| Challenge: | a new form of memes has emerged to combat misogyny and harmful stereotypes . authors present a dataset to analyze online misogamy in Tamil and Malayalam communities . |
| Approach: | They propose to create an annotated dataset with detailed annotation guidelines to analyze online misogyny within Tamil and Malayalam-speaking communities. |
| Outcome: | The proposed dataset reveals the world of gender bias and stereotypes in Tamil and Malayalam-speaking communities. |
From Linguistic Linked Data to Big Data (2024.lrec-main)
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Dimitar Trajanov, Elena Apostol, Radovan Garabik, Katerina Gkirtzou, Dagmar Gromann, Chaya Liebeskind, Cosimo Palma, Michael Rosner, Alexia Sampri, Gilles Sérasset, Blerina Spahiu, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene
| Challenge: | Language data on the LOD cloud has grown in number, size, and variety . Linked (Open) Data (LLOD) is a standardized way of representing and sharing linguistic datasets . |
| Approach: | They propose to combine LLOD and Big Data to improve interoperability of linguistic datasets . they propose to use a machine-readable format to represent and share linguistic data . |
| Outcome: | This paper examines the use cases of Linked (Open) Data and Big Data in language data. |
From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization (2024.lrec-main)
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| Challenge: | Existing models and datasets for training summarization models are limited for less resourceful languages like Hungarian . |
| Approach: | They propose to use a Hungarian corpus for training abstractive and extractive summarization models by cleaning, preprocessing and deduplication. |
| Outcome: | The proposed model trains abstractive and extractive summarization models using the dataset . it will be made publicly available, encouraging replication, further research, and real-world applications across various domains. |
From Technology to Market. Bilingual Corpus on the Evaluation of Technology Opportunity Discovery (2024.lrec-main)
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| Challenge: | a large variety of TOD approaches have been proposed to explore emerging technologies and diversify existing products and services. |
| Approach: | They propose to use a technology-market corpus in English and Japanese to construct a market space using a BERT model. |
| Outcome: | The proposed method is based on a fine-tuned BERT model for linking technology to the market. |
From Text to Historical Ecological Knowledge: The Construction and Application of the Shan Jing Knowledge Base (2024.lrec-main)
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| Challenge: | Traditional Ecological Knowledge (TEK) is a shared cultural heritage and crucial instrument to tackle environmental challenges. |
| Approach: | They propose to build a language resource based on Shanhai Jing (the classic of mountains and seas) written 2000 years ago and uses a stylized narrative and juxtaposition of knowledge from multiple domains to build the knowledge base. |
| Outcome: | The proposed knowledge base contains 1432 systematically classified entities and 3294 relationships. |
From Text to Source: Results in Detecting Large Language Model-Generated Content (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) generate human-like text, but have ethical and misuse concerns. |
| Approach: | They evaluate whether a classifier trained to distinguish between source and target LLMs can detect text from an LLM without further training. |
| Outcome: | The proposed method detects text from target LLMs without further training. |
FUSE - FrUstration and Surprise Expressions: A Subtle Emotional Multimodal Language Corpus (2024.lrec-main)
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| Challenge: | elicitation of frustration and surprise are understudied for emotion modeling in language, but are difficult to characterize. |
| Approach: | They propose a multimodal corpus for expressive task-based spoken language and dialogue focused on frustration and surprise, which are understudied for emotion modeling in language. |
| Outcome: | The proposed corpus provides both individual and dyadic multimodally grounded language. |
Fusion-in-T5: Unifying Variant Signals for Simple and Effective Document Ranking with Attention Fusion (2024.lrec-main)
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| Challenge: | Current document ranking pipelines involve multiple ranking layers to integrate different information step-by-step. |
| Approach: | They propose a novel re-ranker Fusion-in-T5 which integrates text matching information, ranking features, and global document information into one single unified model via templated-based input and global attention. |
| Outcome: | The proposed model significantly improves ranking performance over complex cascade pipelines. |
GAATME: A Genetic Algorithm for Adversarial Translation Metrics Evaluation (2024.lrec-main)
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| Challenge: | a genetic algorithm generates adversarial translations for arbitrary metrics . produced translations score well in an arbitrary MT evaluation metric, despite serious, deliberately introduced errors. |
| Approach: | They propose a method for decoding translation candidates from a machine translation model via a genetic algorithm to generate adversarial translations to test and challenge MT evaluation metrics. |
| Outcome: | The proposed method scores very well in an arbitrary MT evaluation metric despite serious, deliberately introduced errors. |
GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence. |
| Approach: | They propose a GCNet that explicitly leverages global semantic information to guide context encoding. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets. |
GECSum: Generative Evaluation-Driven Sequence Level Contrastive Learning for Abstractive Summarization (2024.lrec-main)
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| Challenge: | Abstractive summarization is a technique in natural language processing that involves generating a summary of a source document by creating new sentences and phrases. |
| Approach: | They propose a sequence-level contrastive learning framework that leverages the semantic understanding capabilities of the abstractive model itself to evaluate summary in reference-based settings. |
| Outcome: | The proposed framework outperforms the state-of-the-art in four summarization datasets. |
Gendered Grammar or Ingrained Bias? Exploring Gender Bias in Icelandic Language Models (2024.lrec-main)
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| Challenge: | Large language models are trained on vast datasets and exhibit increased output quality in proportion to the amount of data that is used to train them. |
| Approach: | They explore whether language models mirror gender distributions within professions or exhibit biases tied to their grammatical genders. |
| Outcome: | The proposed model may reflect and amplify gender bias, racism, religious prejudice, and queerphobia in training data that may not always be recent. |
Generating Clarification Questions for Disambiguating Contracts (2024.lrec-main)
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| Challenge: | Contractual clauses are obligatory and can detail downstream implementation activities . however, contract ambiguities can be difficult to comprehend and can lead to errors . |
| Approach: | They propose a legal NLP task that generates clarification questions for contracts . they propose generating questions that identify contract ambiguities on a document level . |
| Outcome: | The proposed task generates clarification questions for contracts that detect ambiguities on a document level and can generate an F2 score of 0.87. |
Generating Contextual Images for Long-Form Text (2024.lrec-main)
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| Challenge: | Recent advances in Text-to-Image models require short prompts that describe both the content and style of the target image. |
| Approach: | They propose to use Large Language Models (LLMs) and Text-to-Image Models to synthesize relevant visual imagery from generic long-form text. |
| Outcome: | The proposed models can generate high-quality images from short prompts that describe both the content and style of the target image. |
Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification (2024.lrec-main)
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| Challenge: | Existing studies have not studied the performance of intent classifiers against hard-negative out-of-scope utterances. |
| Approach: | They propose to generate hard-negative OOS data using ChatGPT and evaluate them against three benchmark intent classifiers. |
| Outcome: | The proposed method improves classifiers' robustness against hard-negative out-of-scope utterances and general OOS data. |
Generating Multiple-choice Questions for Medical Question Answering with Distractors and Cue-masking (2024.lrec-main)
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| Challenge: | Medical multiple-choice question answering (MCQA) requires high accuracy to be useful in practice. |
| Approach: | They propose to focus masked language modeling on disease name prediction when using medical encyclopedic paragraphs as input. |
| Outcome: | The proposed model outperforms the masked language model on disease name prediction and masks the cues to the answers. |
Generative Multimodal Entity Linking (2024.lrec-main)
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| Challenge: | Existing Entity Linking methods focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters. |
| Approach: | They propose a framework for multimodal entity linking based on Large Language Models (LLMs) that trains a feature mapper to enable cross-modal interactions. |
| Outcome: | The proposed framework achieves state-of-the-art on two well-established datasets with a performance gain of 7.7% on WikiDiverse and 8.8% on Wikileaks. |
GENTRAC: A Tool for Tracing Trauma in Genocide and Mass Atrocity Court Transcripts (2024.lrec-main)
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| Challenge: | GENTRAC analyzes witness statements of genocide and mass atrocity trials using a sophisticated parsing algorithm and a powerful tool for detecting trauma. |
| Approach: | They propose to use a web-based tool to analyze potentially traumatic content in witness statements of genocide and mass atrocity trials. |
| Outcome: | The tool visualizes the density of such content throughout a trial day and provides statistics on the overall amount of traumatic content and speaker distribution. |
Geographically-Informed Language Identification (2024.lrec-main)
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| Challenge: | a paper develops a method to identify languages based on geographic origin of text . the model is based in regions where languages are widely spoken and may occur anywhere . |
| Approach: | They propose to incorporate geographic information into a language identification model to ensure coverage of linguae francae regardless of location. |
| Outcome: | The proposed model includes 31 widely-spoken international languages . the model improves on social media data and improves performance on 916 languages compared to baseline models . |
GerDISDETECT: A German Multilabel Dataset for Disinformation Detection (2024.lrec-main)
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| Challenge: | Disinformation datasets are sparse and expensive to train . annotated datasets often have only binary or multiclass labels . |
| Approach: | They propose to use a textual dataset to detect disinformation in German . the dataset contains 39 multilabel classes with 5 top-level categories . |
| Outcome: | The proposed dataset provides comprehensive insights into disinformation in German using a taxonomy guided annotation scheme. |
German Also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant progress on a wide range of natural language processing tasks, but they still suffer from hallucinating information in their output. |
| Approach: | They propose to use an annotated dataset to detect hallucinations in german news summarization and open-source it to foster further research on hallucinosity detection in german. |
| Outcome: | The proposed model can detect hallucinations in the output and evaluate the faithfulness of the summaries. |
German Parliamentary Corpus (GerParCor) Reloaded (2024.lrec-main)
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| Challenge: | In 2022, the largest German-speaking corpus of parliamentary protocols from three different centuries has been published - GerParCor. |
| Approach: | They propose to update the largest German-speaking corpus of parliamentary protocols from three different centuries, on a national and federal level, from Germany, Austria, Switzerland and Liechtenstein, and to make them available in XMI format. |
| Outcome: | The updated corpus includes all new parliamentary protocols and adds and preprocesses further parliamentary protocol not covered in the previous version. |
German SRL: Corpus Construction and Model Training (2024.lrec-main)
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| Challenge: | Existing semantic role annotation resources are lacking for German. |
| Approach: | They propose a translation-based approach to train German semantic role models using semantic annotations and alignment models. |
| Outcome: | The proposed method achieves competitive evaluation scores, but avoids limitations of previous approaches. |
GERMS-AT: A Sexism/Misogyny Dataset of Forum Comments from an Austrian Online Newspaper (2024.lrec-main)
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| Challenge: | sexism/misogyny dataset extracted from comments of online forum of newspaper . corpus of 8 000 comments annotated with 5 levels of sexist/mistoginist . |
| Approach: | They present a sexism/misogyny dataset extracted from comments of an online forum of an Austrian newspaper. |
| Outcome: | The results show that the corpus of comments is sexist/misogynistic and has 5 levels of sexism/mistoginess. |
GIL-GALaD: Gender Inclusive Language - German Auto-Assembled Large Database (2024.lrec-main)
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| Challenge: | grammatically gendered languages such as German pose unique challenges in generating gender-inclusive language for corrective model training or fine-tuning. |
| Approach: | a corpus of German gender-inclusive language is assembled to help improve model training . grammatically gendered languages such as german pose unique challenges . authors describe most common strategies for gender- inclusive language in german . |
| Outcome: | a corpus of German gender-inclusive language is assembled and will be included in the release. |
GLAMR: Augmenting AMR with GL-VerbNet Event Structure (2024.lrec-main)
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Jingxuan Tu, Timothy Obiso, Bingyang Ye, Kyeongmin Rim, Keer Xu, Liulu Yue, Susan Windisch Brown, Martha Palmer, James Pustejovsky
| Challenge: | Abstract Meaning Representation (AMR) is a general-purpose semantic encoding for language. |
| Approach: | They propose an AMR interpretation of Generative Lexicon semantic components using a verb-net-encoded verb-node graph. |
| Outcome: | The proposed extension is compatible with current AMR specification and can be automated. |
Global and Local Hierarchical Prompt Tuning Framework for Multi-level Implicit Discourse Relation Recognition (2024.lrec-main)
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| Challenge: | Recent methods to recognize hierarchical discourse relations without explicit connectives are inefficient and ignore the utilization of the output probability distribution information of the verbalizer. |
| Approach: | They propose a global and local hierarchical prompt tuning framework which leverages top-up propagated probability as the global hierarchy to inject it into multi-level verbalizer. |
| Outcome: | The proposed framework achieves competitive results on two benchmacks. |
GlotScript: A Resource and Tool for Low Resource Writing System Identification (2024.lrec-main)
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| Challenge: | GlotScript is an open resource and tool for low resource writing system identification. |
| Approach: | They propose to use GlotScript to automatically identify writing systems for low resource languages . they demonstrate that Glotscript can help cleaning multilingual corpora . |
| Outcome: | The proposed tool can help clean multilingual corpora and provide insights on coverage of low resource scripts and languages by each language model. |
GMEG-EXP: A Dataset of Human- and LLM-Generated Explanations of Grammatical and Fluency Edits (2024.lrec-main)
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| Challenge: | Recent work has explored the ability of large language models (LLMs) to generate explanations of existing labeled data. |
| Approach: | They propose a dataset to examine the ability of large language models to explain revisions in sentences by comparing human- and LLM-generated explanations of grammatical and fluency edits to a human evaluation criteria. |
| Outcome: | The proposed explanations address grammatical and fluency edits and are compared with a dataset built from the GMEG (Grammarly Multi-domain Evaluation for GEC) dataset. |
GOLEM: GOld Standard for Learning and Evaluation of Motifs (2024.lrec-main)
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| Challenge: | Motifs are distinctive, recurring, widely used idiom-like words or phrases, often originating from folklore, whose meaning are anchored in a narrative. |
| Approach: | They present a dataset annotated for motific information in English . it contains 26,078 motif candidates across 34 motif types from three cultural or national groups: Jewish, Irish, and Puerto Rican. |
| Outcome: | The first dataset annotated for motific information identifies 26,078 motif candidates across 34 motif types from three cultural or national groups: Jewish, Irish, and Puerto Rican. |
Good or Bad News? Exploring GPT-4 for Sentiment Analysis for Faroese on a Public News Corpora (2024.lrec-main)
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| Challenge: | Existing studies on sentiment analysis in low-resource languages have focused on major languages and emotionally laden text genres like social media and reviews. |
| Approach: | They propose to use GPT-4 for sentiment analysis on Faroese news texts using a multi-class approach with 225 sentences analysed in 170 articles. |
| Outcome: | The proposed model performs remarkably well on 225 sentences and 170 articles compared to human annotators . |
Gos 2: A New Reference Corpus of Spoken Slovenian (2024.lrec-main)
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| Challenge: | a new corpus of spoken Slovenian has been added to the Gos reference corpus . the corpus is now more than double the original size of 300 hours, 2.4 million words . |
| Approach: | They propose to add speech recordings and transcriptions from two related initiatives, the Gos VideoLectures corpus of public academic speech, and the Artur speech recognition database. |
| Outcome: | The new corpus is double the original size and contains 2.4 million words . it includes speech recordings and transcriptions from two related initiatives . |
GPT-3.5 for Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Recent work shows that GPT-3.5 struggles with several error types, including punctuation mistakes, tense errors, syntactic dependencies between words, and lexical compatibility at the sentence level. |
| Approach: | They evaluate GPT-3.5 for grammatical error correction in multiple languages . they use it to re-rank correction hypotheses generated by other GEC models . |
| Outcome: | The proposed model performs well in English and Russian, but struggles with errors in other languages. |
GPTEval: A Survey on Assessments of ChatGPT and GPT-4 (2024.lrec-main)
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| Challenge: | emergence of ChatGPT has generated speculation about its potential to disrupt social and economic systems. |
| Approach: | They analyze prior assessments of ChatGPT and GPT-4 to analyze their language and reasoning abilities, scientific knowledge, ethical considerations and existing evaluation methods. |
| Outcome: | The proposed model performs satisfactorily in science knowledge and can answer open questions. |
GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection? (2024.lrec-main)
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| Challenge: | HateCheck test cases are generic and have simplistic sentence structures that do not match the real-world data. |
| Approach: | They propose a framework to generate more diverse and realistic functional tests from scratch by instructing large language models. |
| Outcome: | The proposed framework generates more diverse and realistic functional tests from scratch by instructing large language models (LLMs). |
GPT-SW3: An Autoregressive Language Model for the Scandinavian Languages (2024.lrec-main)
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Ariel Ekgren, Amaru Cuba Gyllensten, Felix Stollenwerk, Joey Öhman, Tim Isbister, Evangelia Gogoulou, Fredrik Carlsson, Judit Casademont, Magnus Sahlgren
| Challenge: | a growing interest in building and applying large language models for languages other than English is fueling interest in developing LLMs for smaller languages. |
| Approach: | They describe the development process for the first native large generative language model for the North Germanic languages, GPT-SW3. |
| Outcome: | The proposed model is based on the generative language model for the North Germanic languages . it is a first-generation model with a high-quality data set and a low cost of implementation . |
Gradient Consistency-based Parameter Allocation for Multilingual Neural Machine Translation (2024.lrec-main)
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| Challenge: | Multilingual neural machine translation models are often prone to parameter interference . a common problem is that the model compromises with the language diversity to find a solution . |
| Approach: | They propose a method that allocates parameters based on consistency between the gradients of the individual language and the average gradient. |
| Outcome: | The proposed method reduces parameter interference and improves translation quality. |
Gramble: A Tabular Programming Language for Collaborative Linguistic Modeling (2024.lrec-main)
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| Challenge: | a new programming language for linguistic parsing and generation is available for free . a cross-platform interpreter is available on the web via google sheets . |
| Approach: | a new Gramble programming language is being developed for linguistic parsing and generation . the language is a domain-specific programming language that supports live group programming . a cross-platform interpreter is available for Windows, MacOS, and UNIX . |
| Outcome: | a new programming language for linguistic parsing and generation is released under the MIT license . the language is based on handwritten declarative code rather than machine learning . |
Grammatical Error Correction for Code-Switched Sentences by Learners of English (2024.lrec-main)
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| Challenge: | Existing grammar error correction systems have been trained on monolingual data and not developed for CSW text. |
| Approach: | They propose a method of generating synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora and investigate different methods of selecting these spans based on CSW ratio, switch-point factor and linguistic constraints. |
| Outcome: | The proposed model achieves an average increase of 1.57 F0.5 across 3 CSW test sets (English-Chinese, English-Korean and English-Japanese) without affecting the model’s performance on a monolingual dataset. |
Granular Change Accuracy: A More Accurate Performance Metric for Dialogue State Tracking (2024.lrec-main)
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| Challenge: | Current metrics for evaluating Dialogue State Tracking (DST) systems exhibit three primary limitations: i) erroneously presume a uniform distribution of slots throughout the dialog; ii) neglect to assign partial scores for individual turns; c) frequently overestimate or underestimate performance by repeatedly counting the models’ successful or failed predictions. |
| Approach: | They propose a new metric: Granular Change Accuracy (GCA) which evaluates the predicted changes in dialogue state over the entire dialogue history. |
| Outcome: | The proposed metric reduces biases arising from distribution uniformity and the positioning of errors across turns, resulting in a more precise evaluation. |
GreekBART: The First Pretrained Greek Sequence-to-Sequence Model (2024.lrec-main)
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Iakovos Evdaimon, Hadi Abdine, Christos Xypolopoulos, Stamatis Outsios, Michalis Vazirgiannis, Giorgos Stamou
| Challenge: | Transfer learning has revolutionized the fields of Computer Vision and Natural Language Processing. |
| Approach: | They introduce a new language model, GreekBART, that is based on a BART-base architecture. |
| Outcome: | The proposed model outperforms BERT, GPT and other transformer-based models on discriminative tasks. |
GRIT: A Dataset of Group Reference Recognition in Italian (2024.lrec-main)
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| Challenge: | a task of automatically recognizing group references has not yet gained much attention within NLP. |
| Approach: | They propose a large-scale dataset for automatic group reference recognition in italian . they verify the validity of the task using a fine-tuned BERT model . |
| Outcome: | The proposed dataset proves that it can be applied to political text analysis and social media analysis. |
Grounded Multimodal Procedural Entity Recognition for Procedural Documents: A New Dataset and Baseline (2024.lrec-main)
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| Challenge: | Existing methods to extract procedural knowledge from documents focus on text-only settings, which is insufficient for entity disambiguation. |
| Approach: | They propose a model to detect the entity and the corresponding bounding box groundings in images. |
| Outcome: | The proposed model detects the entity and the corresponding bounding box groundings in image (i.e., visual entities) it is based on a dataset of a WikiHow 1 and EHow 2 document and the results are compared with existing models. |
Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language (2024.lrec-main)
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| Challenge: | Existing methods to extract relationships are limited to English and require annotating datasets in order to be expensive and time-consuming. |
| Approach: | They apply guided distant supervision to create a large biographical relationship extraction dataset for German using 80,000 instances for nine relationship types. |
| Outcome: | The proposed dataset is the largest biographical German relationship extraction dataset. |
HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models (2024.lrec-main)
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Guijin Son, Hanwool Lee, Suwan Kim, Huiseo Kim, Jae cheol Lee, Je Won Yeom, Jihyu Jung, Jung woo Kim, Songseong Kim
| Challenge: | Existing evaluation tools rely on translations of English datasets or translation-specific benchmarks such as WMT 21 to assess large language models. |
| Approach: | They propose a dataset curated to challenge models lacking Korean cultural and contextual depth. |
| Outcome: | The HAE-RAE Bench challenges models lacking Korean cultural and contextual depth by highlighting their aptitude for recalling Korean-specific knowledge and cultural contexts. |
Halwasa: Quantify and Analyze Hallucinations in Large Language Models: Arabic as a Case Study (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) generate text that is factually incorrect, nonsensical, or misleading. |
| Approach: | They create a large Arabic dataset that contains 10K of LLM generated sentences and annotate it for factuality and correctness. |
| Outcome: | The proposed dataset analyzes 10K of generated sentences and finds 25% of them are factually incorrect. |
HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text (2024.lrec-main)
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| Challenge: | a framework/annotation schema is being developed to assess the offline harm potential of social media texts. |
| Approach: | They propose to annotate social media texts with their potential for triggering offline harm . they propose to use mood and modality as relevant categories to mark the speaker's intention, intended goal and their own evaluation of whether what they are saying is 'necessary' and 'possible' |
| Outcome: | The proposed framework can be used to annotate social media texts with their potential for triggering offline harm. |
Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing (2024.lrec-main)
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| Challenge: | Existing methods for graph processing rely on assumptions about data relations that are inadequate when handling large and complex graph data. |
| Approach: | They propose a large language model enhanced by an uncertainty-aware module to provide a confidence score on the generated graph data. |
| Outcome: | The proposed approach surpasses state-of-the-art algorithms by a substantial margin on ten datasets. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking (2024.lrec-main)
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| Challenge: | determining the trustworthiness of online medical content is challenging in the digital age . fact-checking is an approach to assess the veracity of factual claims . a new dataset is presented to help advance automated fact- checking . |
| Approach: | They propose a dataset that assesses the veracity of factual claims using evidence from credible sources. |
| Outcome: | The proposed dataset can be used for automated fact-checking tasks. |
Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents (2024.lrec-main)
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| Challenge: | Consistency within a document is a crucial feature indicative of its quality . low-quality documents often lack internal consistency or contain content unrelated to headlines . |
| Approach: | They propose a hierarchical graph convolutional network that detects internal inconsistencies within a document and incongruences between the title and body. |
| Outcome: | The proposed model outperforms existing models on the inconsistency dataset and on the publicly available incongruent-related dataset. |
Hierarchical Selection of Important Context for Generative Event Causality Identification with Optimal Transports (2024.lrec-main)
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| Challenge: | Existing methods for Event Causality Identification (ECI) rely on external toolkits or human annotation to obtain training signals. |
| Approach: | They propose a generative framework that leverages Optimal Transport to automatically select the most important sentences and words from full documents. |
| Outcome: | The proposed framework can predict causal relation between two events in text without external tools. |
Hierarchical Topic Modeling via Contrastive Learning and Hyperbolic Embedding (2024.lrec-main)
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| Challenge: | Existing hierarchical topic models are based on Euclidean space, which cannot retain the hierarchically semantic information in the corpus, leading to irrational structure of the generated topics. |
| Approach: | They propose a novel hierarchical topic model that uses contrastive learning to capture information from documents. |
| Outcome: | The proposed model performs on topic coherence and topic diversity, and on the rationality of the topic hierarchy. |
High-order Joint Constituency and Dependency Parsing (2024.lrec-main)
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| Challenge: | Syntactic parsing aims to reveal how sentences are syntactically structured. |
| Approach: | They propose to produce compatible constituency and dependency trees simultaneously for input sentences . they adopt a much more efficient decoding algorithm and explore joint modeling at training phase . |
| Outcome: | The proposed model significantly improves matching ratio of whole trees compared to separate models . the proposed model adopts a much more efficient decoding algorithm . |
High-Order Semantic Alignment for Unsupervised Fine-Grained Image-Text Retrieval (2024.lrec-main)
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| Challenge: | Existing studies focus on learning global or local correspondence, but lack fine-grained local-global alignment. |
| Approach: | They propose a High Order Semantic Alignment (HOSA) model that can provide complementary and comprehensive semantic clues to infer correlation scores. |
| Outcome: | The proposed model outperforms state-of-the-art models in retrieving the most relevant results. |
HoLM: Analyzing the Linguistic Unexpectedness in Homeric Poetry (2024.lrec-main)
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| Challenge: | Existing work on the authorship of the Homeric poems has only been done at the level of lengthier excerpts, but not individual verses, at which most suspected interpolations occur. |
| Approach: | They present a corpus of Homeric verses with a score quantifying linguistic unexpectedness based on Perplexity. |
| Outcome: | The proposed corpus of Homeric verses is complemented with a score quantifying linguistic unexpectedness based on Perplexity. |
How Diplomats Dispute: The UN Security Council Conflict Corpus (2024.lrec-main)
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| Challenge: | Until now, there has been little work on how to formalize conflicts in a diplomatic setting. |
| Approach: | They present a corpus of 87 UNSC speeches that are annotated for conflicts and demonstrate the difficulty when dealing with diplomatic language. |
| Outcome: | The proposed method demonstrates that diplomatic language is complex and often implicit along various dimensions. |
How Do Hyenas Deal with Human Speech? Speech Recognition and Translation with ConfHyena (2024.lrec-main)
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| Challenge: | Currently, attention-based models face computational hurdles in processing long sequences due to its quadratic complexity. |
| Approach: | They propose a conformer whose encoder self-attentions are replaced with Hyena for speech processing . they propose 'confhyena' model that reduces training time by 27% at minimal cost . |
| Outcome: | The proposed model reduces training time by 27% at the cost of minimal quality degradation. |
How Far Is Too Far? Studying the Effects of Domain Discrepancy on Masked Language Models (2024.lrec-main)
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| Challenge: | Pre-trained masked language models perform strongly on a wide variety of NLP tasks. |
| Approach: | They propose a mechanism to quantify the difference in domains between the pre-trained model and the task and partition it using a cloze task. |
| Outcome: | The proposed model performs better on openly available e-commerce datasets than the original model on scientific and biomedical datasets. |
How Gender Interacts with Political Values: A Case Study on Czech BERT Models (2024.lrec-main)
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| Challenge: | Neural language models are trained on large text corpora that contain value-burdened content and often capture undesirable biases, which the models reflect. |
| Approach: | They propose a method to measure the model's perceived political values by comparing Czech with a representative value survey. |
| Outcome: | The proposed method does not assign statement probability following value-driven reasoning and there is no systematic difference between feminine and masculine sentences. |
How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)
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| Challenge: | Out-of-distribution (OOD) detection is crucial for ensuring AI safety . large language models (LLMs) are becoming more prevalent due to their scale, pre-training objectives, and paradigms used for inference. |
| Approach: | They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning. |
| Outcome: | The proposed method outperforms other OOD detectors in zero-grad and fine-tuning scenarios. |
How Important Is Tokenization in French Medical Masked Language Models? (2024.lrec-main)
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| Challenge: | Word tokenization into subword units has become the prevailing standard in the field of natural language processing (NLP) over recent years . the precise factors contributing to its success remain unclear . |
| Approach: | They propose a tokenization strategy that integrates morpheme-enriched word segmentation into existing tokenization methods. |
| Outcome: | The proposed tokenization strategy outperforms character and word tokenization but the precise factors contributing to its success remain unclear. |
How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study (2024.lrec-main)
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| Challenge: | Existing studies have focused on enhancing the factualness of large language models using context knowledge. |
| Approach: | They propose to use ChatGPT to construct probing datasets that provide diverse and coherent evidence corresponding to various facts. |
| Outcome: | The proposed model can encode knowledge across different layers, and it is compared with existing models. |
How Much Do Robots Understand Rudeness? Challenges in Human-Robot Interaction (2024.lrec-main)
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| Challenge: | This paper examines the pressing need to understand and manage inappropriate language within the evolving human-robot interaction landscape. |
| Approach: | They propose to use data cleaning methods to identify inappropriate language in real-time interactions and evaluate natural language models for their proficiency in discerning rudeness. |
| Outcome: | The proposed methods identify and mitigate inappropriate language in real-time interactions and evaluate natural language models for their proficiency in discerning rudeness. |
How Robust Are the QA Models for Hybrid Scientific Tabular Data? A Study Using Customized Dataset (2024.lrec-main)
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| Challenge: | Existing tabular QA models are lacking in understanding their robustness on scientific information. |
| Approach: | They propose a dataset to assess the robustness of tabular QA models on scientific hybrid tabular data. |
| Outcome: | The proposed model performs well on scientific tables and text, while the best score is 0.462. |
How Speculative Can Speculative Decoding Be? (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have a largely increased latency due to their ability to autoregressively model . speculative decoding is a technique that trades generation quality for speed . |
| Approach: | They propose to use a draft model to draft tokens autoregressively and then verify them in parallel. |
| Outcome: | The proposed model could draft tokens autoregressively and then verify them in parallel . the proposed model trades quality for speed and could fail in verification stage . |
How Susceptible Are LLMs to Logical Fallacies? (2024.lrec-main)
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| Challenge: | Recent studies have focused on LLMs' reasoning abilities, but their rational thinking capacity is not as robust as that of other NLP downstream tasks. |
| Approach: | They propose a diagnostic benchmark to assess the robustness of Large Language Models against logical fallacies by comparing their performance against a scenario where the persuader employs logical fallsacie. |
| Outcome: | The proposed benchmark compares the performance of LLMs in debates on controversial topics against logical fallacies. |
How to Do Politics with Words: Investigating Speech Acts in Parliamentary Debates (2024.lrec-main)
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| Challenge: | a new perspective on framing through the lens of speech acts investigates how politicians make use of different pragmatic speech act functions in political debates. |
| Approach: | They propose a new framework for framing through the lens of speech acts and an annotation scheme for political debates. |
| Outcome: | The proposed framework can predict speech acts with an avg. F1 of around 82.0% . the proposed framework is based on a dataset of German parliamentary debates . |
How to Encode Domain Information in Relation Classification (2024.lrec-main)
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Elisa Bassignana, Viggo Unmack Gascou, Frida Nøhr Laustsen, Gustav Kristensen, Marie Haahr Petersen, Rob van der Goot, Barbara Plank
| Challenge: | Existing deep learning models require a lot of training data to obtain high performance. |
| Approach: | They propose a multi-domain training setup for Relation Classification (RC) they compare different ways to enrich input instances with domain information . |
| Outcome: | The proposed model improves > 2 Macro-F1 against the baseline setup. |
How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have (2024.lrec-main)
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| Challenge: | Existing datasets for abusive language detection are expensive and lack of knowledge about the target is a challenge. |
| Approach: | They propose to build models cheaply for a new target label set and/or language, using only a few training examples of the target domain. |
| Outcome: | The proposed model improves monolingually and across languages using existing datasets and only a few-shots of the target domain. |
How to Understand “Support”? An Implicit-enhanced Causal Inference Approach for Weakly-supervised Phrase Grounding (2024.lrec-main)
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| Challenge: | Existing studies on Weakly-supervised Phrase Grounding (WPG) largely ignore the implicit phrase-region matching relations, rendering it arduous to explore the semantic nature of phrases. |
| Approach: | They propose an Implicit-Enhanced Causal Inference approach to address the challenges of modeling the implicit relations and highlighting them beyond the explicit. |
| Outcome: | The proposed approach outperforms the state-of-the-art baselines on an implicit-enhanced dataset. |
How Well Can BERT Learn the Grammar of an Agglutinative and Flexible-Order Language? The Case of Basque. (2024.lrec-main)
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| Challenge: | Neural Language Models (NLMs) have demonstrated effectiveness in acquiring skills related to human language use. |
| Approach: | They hypothesize that languages with complex grammar present substantial challenges during the pre-training phase . they constructed a test set that measures grammatical knowledge of BERT models trained under various pre-training configurations using corpus size, model size, number of epochs, and lemmatization. |
| Outcome: | The proposed model is based on a student-based minimal pairs test set with a grammatically correct and an incorrect sentence. |
HS-GC: Holistic Semantic Embedding and Global Contrast for Effective Text Clustering (2024.lrec-main)
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| Challenge: | In this paper, we introduce Holistic Semantic Embedding and Global Contrast (HS-GC) to learn the instance- and cluster-level representations. |
| Approach: | They propose a novel loss function that exploits different layers of semantic information in a deep neural network to provide a more holistic semantic text representation. |
| Outcome: | The proposed model outperforms the state-of-the-art model on five text datasets and improves clustering accuracy of 5.9% and 3.2% on the StackOverflow and TREC datasets. |
HuLU: Hungarian Language Understanding Benchmark Kit (2024.lrec-main)
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Noémi Ligeti-Nagy, Gergő Ferenczi, Enikő Héja, László János Laki, Noémi Vadász, Zijian Győző Yang, Tamás Váradi
| Challenge: | The Hungarian Language Understanding (HuLU) benchmark is a comprehensive assessment framework designed to evaluate the performance of neural language models on Hungary language tasks. |
| Approach: | They propose to use a framework to evaluate the performance of neural language models on Hungarian language tasks. |
| Outcome: | The framework evaluates models against Hungarian language tasks using a web service and a leaderboard. |
Human and System Perspectives on the Expression of Irony: An Analysis of Likelihood Labels and Rationales (2024.lrec-main)
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| Challenge: | a new study examines the recognition of irony by humans and automatic systems . a fine-grained annotation scheme allows for improved modeling of ironity in automatic systems. |
| Approach: | They propose a fine-grained annotation scheme that allows for better recognition of irony by humans and automatic systems. |
| Outcome: | The proposed model improves on tweets annotated with high confidence and agreement . it also performs better on high-confidence and highagreement samples compared to automated systems . |
HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization (2024.lrec-main)
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| Challenge: | Existing benchmarks focus on translating English prompts to multilingual codes or have been constrained to very limited natural languages (NLs). |
| Approach: | They propose a benchmark to evaluate multilingual LLMs using multiple natural languages. |
| Outcome: | The proposed benchmarks focus on translating English prompts to multilingual code or have been constrained to very limited natural languages (NLs). |
Human in the Loop: How to Effectively Create Coherent Topics by Manually Labeling Only a Few Documents per Class (2024.lrec-main)
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| Challenge: | Few-shot methods for accurate modeling under sparse label-settings are still challenging in document classification. |
| Approach: | They propose to combine supervised few-shot learning with a topic extraction method to generate coherent topics in large text corpora. |
| Outcome: | The proposed method outperforms unsupervised topic modeling methods in document classification. |
Humanistic Buddhism Corpus: A Challenging Domain-Specific Dataset of English Translations for Classical and Modern Chinese (2024.lrec-main)
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| Challenge: | Compared to existing machine translation datasets, HBC presents unique challenges . classical and modern Chinese texts are often translated in distant languages . |
| Approach: | They propose a dataset containing 80,000 Chinese-English parallel phrases extracted and translated from publications in the domain of Buddhism. |
| Outcome: | The Humanistic Buddhism Corpus (HBC) contains 80,000 parallel Chinese-English phrases extracted and translated from publications in the domain of Buddhism. |
Humanitarian Corpora for English, French and Spanish (2024.lrec-main)
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| Challenge: | et al., a leading database of humanitarian documents, compiled with ReliefWeb reports . documents selected with language identification and noise reduction techniques . authors present corpora of English, French and Spanish humanitarian documents . |
| Approach: | They present three corpora of English, French and Spanish humanitarian documents compiled with ReliefWeb reports . documents were tokenized, lemmatized, tagged by part of speech, and enriched with metadata . authors propose a project to develop a humanitarian dictionary with a focus on conceptual variation . |
| Outcome: | The corpora were compiled to satisfy the research needs of the Humanitarian Encyclopedia project with a focus on conceptual variation. |
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack (2024.lrec-main)
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| Challenge: | Despite the development of large language models, there are still significant challenges in detecting whether text is generated by a machine. |
| Approach: | They propose a framework for a broader class of adversarial attacks to perform minor perturbations in machine-generated content to evade detection. |
| Outcome: | The proposed framework can be compromised in as little as 10 seconds, and improves over iterative adversarial learning. |
Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection (2024.lrec-main)
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| Challenge: | In this paper, we examine the role of conversational context in abusive language detection . prior studies have ignored the contextual nature of abusive language, ignoring this aspect . toxicity, hate speech, harmful stereotypes are among the forms of harmful language . |
| Approach: | They propose to use conversational context to analyze abusive language detection using two methods . they use "abusive language" as an umbrella term to refer to various forms of harmful language . |
| Outcome: | The proposed approach is based on two datasets in English and a new dataset of French tweets annotated for hate speech and stereotypes. |
Human vs. Machine Perceptions on Immigration Stereotypes (2024.lrec-main)
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| Challenge: | a growing number of natural language processing models leave aside the language itself . a recent paradigm in the computational linguistics community is training models on specific perspectives of a segment of the population or an individual. |
| Approach: | They propose to use BERT-based classification models to detect stereotypes related to immigrants . they compare models with predictions from GPT-4 and annotated tweets from Spanish Twitter . |
| Outcome: | The proposed models are compared with predictions from the dataset of Spanish Twitter posts containing stereotypes . the models are confident in their predictions and more accurate for implicit stereotypes, the authors show . |
Hybrid of Spans and Table-Filling for Aspect-Level Sentiment Triplet Extraction (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is an emerging task in sentiment analysis research. |
| Approach: | They propose a model which combines span with table-filling to extract triplets from words . they use syntactic and contextual features to generate word-pair tables and convert them to span tables . |
| Outcome: | The proposed model achieves competitive results on a dataset with a large dataset. |
Hyperbolic Graph Neural Network for Temporal Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing knowledge graph models are inefficient at capturing complex temporal dynamics and hierarchical relations within TKGs. |
| Approach: | They propose to use hyperbolic geometry to effectively model temporal knowledge graphs . they use the hyperbolical gated Graph Neural Network and the hyperbipolar convolutional neural network . |
| Outcome: | The proposed model achieves state-of-the-art performance on four benchmark datasets . it is compared with previous models and is expected to be useful in real-world applications . |
Hyperbolic Representations for Prompt Learning (2024.lrec-main)
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| Challenge: | Existing techniques to train only continuous prompts while freezing the language model have been developed. |
| Approach: | They propose to use hyperbolic space to model hierarchical relationships between prompts and inputs . they use a Poincaré disk to capture the hierarchic relationship between prompt and input . |
| Outcome: | The proposed approach reduces training time and storage for downstream tasks by reducing training costs. |
Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems (2024.lrec-main)
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| Challenge: | Presently, graph-based recommendations are limited by session dependencies and data sparsity in real-world scenarios. |
| Approach: | They propose a method which uses multi-collaborative self-supervised learning in hypergraph neural networks to model item transitions and to mitigate the challenges of data sparsity. |
| Outcome: | The proposed method outperforms existing methods in a number of domains and consistently outperformed existing methods. |
HyperMR: Hyperbolic Hypergraph Multi-hop Reasoning for Knowledge-based Visual Question Answering (2024.lrec-main)
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| Challenge: | Existing studies on knowledge-based visual question answering (KBVQA) describe the semantic distance using the actual Euclidean distance between two nodes, which leads to distortion in modeling knowledge graphs with hierarchical and scale-free structure. |
| Approach: | They propose to use the actual Euclidean distance between two nodes to solve a problem of hierarchical and free-scale knowledge graphs. |
| Outcome: | Extensive experiments on the KVQA, PQ and PQL datasets demonstrate the effectiveness of HyperMR for strong-hierarchy knowledge graphs. |
HYPERTTS: Parameter Efficient Adaptation in Text to Speech Using Hypernetworks (2024.lrec-main)
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| Challenge: | Neural text-to-speech (TTS) systems limited to predefined speaker styles or specific sets of speaker IDs. |
| Approach: | They propose a network that can adapt adapter parameters to new speakers . they compare two domain adaptation settings and find it to be very efficient . |
| Outcome: | The proposed Adapters improve speech synthesis performance on two domains and compare them with baselines. |
HYRR: Hybrid Infused Reranking for Passage Retrieval (2024.lrec-main)
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| Challenge: | Existing passage retrieval systems typically adopt a two-stage retrieve-then-rerank pipeline. |
| Approach: | They propose a framework for training robust reranking models using hybrid retrievers . they propose HYRR framework that allows users to select training data using hybrids . |
| Outcome: | The proposed framework is robust to different first-stage retrieval settings. |
IAD: In-Context Learning Ability Decoupler of Large Language Models in Meta-Training (2024.lrec-main)
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| Challenge: | In-context Learning (ICL) is a paradigm in which LLMs acquire task-specific knowledge by processing input-output pairs provided as prompts. |
| Approach: | They propose an In-context learning Ability Decoupler to separate ICL ability from general ability of LLMs in meta-training phase . they first identify parameters suitable for ICL by transference-driven gradient importance and propose a new max-margin loss to emphasize the separation of the two abilities. |
| Outcome: | The proposed model separates the ICL ability from the general ability of LLMs in the meta-training phase, where the I-related parameters are tuned to adapt for ICL tasks. |
IDC: Boost Text-to-image Retrieval via Indirect and Direct Connections (2024.lrec-main)
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| Challenge: | Dual Encoders (DE) and Cross Attention (CA) frameworks for image and text retrieval are more accurate but slower. |
| Approach: | They propose a dual encoders-based approach to map image and text inputs into a coordinated representation space and calculate their similarity directly. |
| Outcome: | Extensive experiments on the MSCOCO and Flickr30K datasets validate the effectiveness of the proposed methods. |
IDEATE: Detecting AI-Generated Text Using Internal and External Factual Structures (2024.lrec-main)
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| Challenge: | Existing methods to detect AI-generated text rely on internal evidences, but external evidences are not considered. |
| Approach: | They propose a hierarchical graph network that utilizes internal and external factual structures to detect AI-generated text. |
| Outcome: | The proposed network outperforms current state-of-the-art methods on four datasets. |
IDEM: The IDioms with EMotions Dataset for Emotion Recognition (2024.lrec-main)
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Alexander Prochnow, Johannes E. Bendler, Caroline Lange, Foivos Ioannis Tzavellos, Bas Marco Göritzer, Marijn ten Thij, Riza Batista-Navarro
| Challenge: | idiomatic expressions are used in everyday language and typically convey affect, i.e., emotion. |
| Approach: | They present a dataset of idiom-containing sentences that were generated and labelled with any one of 36 emotion types using a generative language model. |
| Outcome: | The proposed method achieves an agreement rate of 62% on the IDioms with EMotions dataset, with human validation by two independent annotators. |
Identifying and Aligning Medical Claims Made on Social Media with Medical Evidence (2024.lrec-main)
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| Challenge: | Evidence-based medicine is the practice of making medical decisions that adhere to the latest, and best known evidence available. |
| Approach: | They propose a system that can generate synthetic medical claims to aid each of these tasks and a dataset that demonstrates an improvement in all comparable metrics. |
| Outcome: | The proposed system improves on core tasks and shows that it is more flexible and holistic. |
Identifying Fine-grained Depression Signs in Social Media Posts (2024.lrec-main)
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| Challenge: | Currently, most studies focus on a binary classification setup or on pre-established resources. |
| Approach: | They evaluated machine learning techniques to model 21 depression signs in social media posts from Brazilian undergraduate students. |
| Outcome: | The proposed methods struggle to classify the majority of depression signs on social media posts, compared with the majority on the social media sites. |
Identifying Source Language Expressions for Pre-editing in Machine Translation (2024.lrec-main)
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| Challenge: | MT-mediated communication can benefit from pre-editing source language texts to ensure accurate transmission of intended meaning in the target language. |
| Approach: | They hypothesize that such expressions tend to be distinctive features of texts originally written in the source language rather than translations generated from the target language into the source languages. |
| Outcome: | The proposed method identified characteristic expressions of the native language despite the noise and inherent nuances of the task. |
Ideological Knowledge Representation: Framing Climate Change in EcoLexicon (2024.lrec-main)
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| Challenge: | a method to extract ideological knowledge from corpora is proposed to represent environmental concepts and terms in terminological resources . political discourse is not considered specialized language, but politicians use scientific terms to soften the message . |
| Approach: | They propose to use terminological knowledge bases to represent political discourse on environmental concepts and terms. |
| Outcome: | The proposed method shows how climate change discourse changes across ideological spectrum . it uses spanish and english parliamentary debates to extract ideological knowledge from corpora. |
ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation (2024.lrec-main)
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| Challenge: | Existing approaches for local citation recommendation map or translate a query to citation-worthy research papers. |
| Approach: | They propose a local citation recommendation task that uses latent evidence spans to recommend papers . proposed system retrieves ranked lists of evidence span and recommended paper pairs . |
| Outcome: | The proposed system retrieves ranked lists of evidence span and recommended paper pairs based on evidence from the existing literature. |
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler (2024.lrec-main)
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| Challenge: | State-of-the-art intent classification and slot filling methods rely on data-intensive deep learning models . large language models exhibit remarkable zero-shot performance across various natural language tasks. |
| Approach: | They propose an approach framing IC and SF as language generation tasks for instruction-LLMs with a more efficient SF-prompting method. |
| Outcome: | The proposed approach outperforms state-of-the-art IC+SF method and in-context learning methods with GPT3.5 (175B). |
Image Matters: A New Dataset and Empirical Study for Multimodal Hyperbole Detection (2024.lrec-main)
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| Challenge: | linguistic detection of hyperbole is an important part of understanding human expression . studies on hyperbolic expressions focus on text modality, but social media can be used to detect it . |
| Approach: | They propose to use a multimodal detection dataset to study hyperbole detection . they treat text and image as two modalities and evaluate pre-trained encoders . |
| Outcome: | The proposed dataset is constructed from five different keywords and shows its performance. |
Impact of Task Adapting on Transformer Models for Targeted Sentiment Analysis in Croatian Headlines (2024.lrec-main)
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| Challenge: | BERT models are often taken off-the-shelf and fine-tuned on a downstream task. |
| Approach: | They propose an extra stage of self-supervised task-adaptive pre-training to perform a task on a number of Croatian-supporting Transformer models. |
| Outcome: | The proposed approach improves performance across multilingual models but not in Croatian-dominant models. |
Impoverished Language Technology: The Lack of (Social) Class in NLP (2024.lrec-main)
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| Challenge: | Existing work on socio-demographic factors has focused on how much a person's socioeconomic status affects their language production and perception. |
| Approach: | They propose to include socio-economic class in future natural language processing (NLP) research aimed at understanding relationships between socio-demographic factors and language production and perception. |
| Outcome: | The proposed definition of class can be operationalised by NLP researchers and argue for including socio-economic class in future language technologies. |
Improved Neural Protoform Reconstruction via Reflex Prediction (2024.lrec-main)
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| Challenge: | comparative method allows linguists to infer protoforms from their reflexes based on sound change . authors argue that this approach ignores one of the most important aspects of the comparative approach . |
| Approach: | They propose a comparative method that allows linguists to infer protoforms from their reflexes . they propose to use a system where candidate protoform from a reconstruction model are reranked by a reflex prediction model. |
| Outcome: | The comparative method surpasses state-of-the-art methods on Chinese and Romance datasets. |
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification (2024.lrec-main)
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| Challenge: | Current methods for intent classification often rely on assumptions about data distributions and outliers are unpredictable . |
| Approach: | They propose a dual encoder for threshold-based re-classification that generates user utterance embeddings and incorporates out-of-scope phrases from open-domain datasets. |
| Outcome: | The proposed framework outperforms benchmarks on the CLINC-150, Stackoverflow, and Banking77 datasets and achieves an increase of up to 13% and 5% in F1 score for known and unknown intents. |
Improving Bengali and Hindi Large Language Models (2024.lrec-main)
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| Challenge: | Bengali and Hindi are low-resource languages, and the state-of-the-art tokenization methods fail to separate roots from affixes. |
| Approach: | They used BERT and Wordpiece tokenizers to train a wordpiece tokenization system for Bengali and Hindi to model fine-grained character-level information. |
| Outcome: | The proposed tokenizers outperform the state-of-the-art and Wordpiece tokenizer for modeling Bengali and Hindi. |
Improving Chinese Named Entity Recognition with Multi-grained Words and Part-of-Speech Tags via Joint Modeling (2024.lrec-main)
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| Challenge: | Named entity recognition (CNER) is a fundamental task in natural language processing (NLP). |
| Approach: | They propose a tree parsing approach for jointly modeling Chinese named entity recognition (CNER) with multi-grained word segmentation (MWS) and POS tagging tasks. |
| Outcome: | The proposed approach achieves better or comparable performance with current methods. |
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users (2024.lrec-main)
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Yejin Kim, Scott Rome, Kevin Foley, Mayur Nankani, Rimon Melamed, Javier Morales, Abhay K. Yadav, Maria Peifer, Sardar Hamidian, H. Howie Huang
| Challenge: | Current approaches focus on improving ranking performance at the cost of escalating complexity and complicating the task. |
| Approach: | They propose a hybrid multi-task learning approach that trains on user-item and item-i item interactions. |
| Outcome: | The proposed approach improves accuracy, relevance, and diversity of user recommendations even for cold-start users. |
Improving Continual Few-shot Relation Extraction through Relational Knowledge Distillation and Prototype Augmentation (2024.lrec-main)
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| Challenge: | Existing approaches to Continual Relation Extraction (CRE) are limited in handling the rapid emergence of new relations in real-world scenarios. |
| Approach: | They propose a framework that integrates prototype-based data augmentation and relational knowledge distillation to solve the problem of Continual Few-shot Relation Extraction (CFRE). |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the FewRel and TACRED datasets. |
Improving Copy-oriented Text Generation via EDU Copy Mechanism (2024.lrec-main)
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| Challenge: | Existing extractive models generate texts through word-by-word decoding, causing factual inconsistencies and slow inference. |
| Approach: | They propose a framework that integrates the behavior of copying EDUs into generative models. |
| Outcome: | The proposed framework reduces the number of generated tokens significantly. |
Improving Cross-lingual Transfer with Contrastive Negative Learning and Self-training (2024.lrec-main)
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| Challenge: | Recent studies improve cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. |
| Approach: | They propose to use negative pairs to align the multilingual model and self-train the model to converge on the obtained clean pseudo-labels. |
| Outcome: | The proposed method improves upon the baseline models and can serve as a beneficial complement to the alignment-based methods. |
Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)
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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu
| Challenge: | Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures. |
| Approach: | They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree . |
| Outcome: | The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness. |
Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency (2024.lrec-main)
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| Challenge: | Abstractive summarization models (LLMs) have demonstrated impressive performance in various tasks, but they are still suffering from factual inconsistency problem called hallucination. |
| Approach: | They propose to improve the faithfulness of large language models by impelling them to process the entire article more fairly and faithfully. |
| Outcome: | The proposed strategy improves the faithfulness of large language models in summarization while maintaining their fluency and informativeness. |
Improving Grammatical Error Correction by Correction Acceptability Discrimination (2024.lrec-main)
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| Challenge: | Existing Grammatical Error Correction (GEC) methods overlook the assessment of sentence-level syntax and semantics in the corrected sentence. |
| Approach: | They propose a correction acceptance discrimination task to assess sentence-level syntax and semantics in corrected sentences and a pipeline method to remove invalid corrections. |
| Outcome: | The proposed method improves F0.5 score by 1.01% over 13 GEC systems in the BEA-2019 test set. |
Improving Implicit Discourse Relation Recognition with Semantics Confrontation (2024.lrec-main)
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| Challenge: | Existing methods for implicit discourse relation recognition (IDRR) are unsatisfactory for the task. |
| Approach: | They propose a method that trains PLMs through two semantics enhancers to implicitly differentiate logical and general semantics. |
| Outcome: | The proposed method exceeds baseline by 3.81% F1 score on PDTB 2.0 dataset . it infers discourse logical relations without explicit connectives, but performance remains unsatisfactory . |
Improving Language Model Reasoning with Self-motivated Learning (2024.lrec-main)
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| Challenge: | Large-scale high-quality training data is important for improving the performance of models. |
| Approach: | They propose a framework that motivates the model to automatically generate rationales on existing datasets and improves the performance of reasoning through reinforcement learning. |
| Outcome: | The proposed model outperforms InstructGPT on multiple reasoning datasets and outperformed InstructGPT on other datasets. |
Improving Low-Resource Keyphrase Generation through Unsupervised Title Phrase Generation (2024.lrec-main)
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| Challenge: | Existing methods for unsupervised keyphrase generation use phrases extracted from document title instead of phrase bank. |
| Approach: | They propose a method for generating pseudo labels from a document title . they use phrases mined from the document title to generate absent keyphrases . |
| Outcome: | The proposed method outperforms existing methods on human-annotated datasets even with fewer labeled data. |
Improving Multi-view Document Clustering: Leveraging Multi-structure Processor and Hybrid Ensemble Clustering Module (2024.lrec-main)
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| Challenge: | Experimental results show that DMsECN outperforms existing models for document clustering . |
| Approach: | They propose a multi-view document clustering model with a processor and hybrid module . they demonstrate that DMsECN outperforms existing models by creating a consensus structure from multiple clustering structures. |
| Outcome: | The proposed model outperforms existing models on four multi-view document clustering datasets. |
Improving Personalized Sentiment Representation with Knowledge-enhanced and Parameter-efficient Layer Normalization (2024.lrec-main)
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| Challenge: | Existing studies on personalized sentiment classification consider document reviews as overall text unit and incorporate backgrounds (i.e., user and product information) Existing methods for personalized sentiment modeling have quadratic costs that increase with text length and heterogeneous mixes of background information and textual information. |
| Approach: | They propose a knowledge-enhanced and parameter-efficient layer normalization model that leverages pretrained checkpoints and background information into transformer structures. |
| Outcome: | The proposed model can be used to improve pretrained language models in document reviews and incorporate background information with parameter-efficient fine-tuning and knowledge injecting. |
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction (2024.lrec-main)
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| Challenge: | Existing large language models can extract triples from simple sentences with few-shot learning or fine-tuning, but they often miss out when extracting from complex sentences. |
| Approach: | They propose an evaluation-filtering framework that integrates large language models with small models for relational triple extraction tasks. |
| Outcome: | The proposed framework integrates large language models with small models for relational triple extraction tasks. |
Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training (2024.lrec-main)
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| Challenge: | Graph neural networks excel at anomaly detection, but exhibit vulnerability to attacks . novel mechanism for graph adversarial training designed to bolster anomaly detectors . |
| Approach: | They propose a mechanism for graph adversarial training to bolster anomaly detection systems against potential poisoning attacks. |
| Outcome: | The proposed method bolsters GNN-based anomaly detection systems against poisoning attacks. |
Improving Role-Oriented Dialogue Summarization with Interaction-Aware Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing methods for encoding dialogues do not capture interaction information between roles, thus ignore interaction-related key information. |
| Approach: | They propose a contrastive learning based interaction-aware model for the role-oriented dialogue summarization namely CIAM and use it to train the decoder to learn role-level interaction. |
| Outcome: | The proposed model captures interaction information between different roles and produces informative summaries on two public datasets. |
Improving Text Readability through Segmentation into Rheses (2024.lrec-main)
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| Challenge: | a new study examines the segmentation of sentences into rheses to improve readability for dyslexics . short lines of text can be beneficial for dyslexia sufferers as it limits attention span . however, random line splits can be confusing than helpful . |
| Approach: | They propose to segment sentences into rhythmic and semantic units to improve comprehension . they also use a bilingual dataset to evaluate the efficiency of their approach . |
| Outcome: | The proposed approach achieves an F1 score of 90.0% in English and 91.3% in French . the proposed approach also demonstrates the potential of leveraging prosodic elements . |
Improving the Robustness of Large Language Models via Consistency Alignment (2024.lrec-main)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Large language models have shown tremendous success in following user instructions and generating helpful responses, but their robustness is still far from optimal. |
| Approach: | They propose a two-stage training framework that helps a model generalize on following instructions via similar instruction augmentations. |
| Outcome: | The proposed training framework improves diversity and aligns the model with human expectations by differentiating subtle differences in similar responses. |
Improving Unsupervised Neural Machine Translation via Training Data Self-Correction (2024.lrec-main)
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| Challenge: | Unsupervised neural machine translation models can generate mistakes during training . however, the quality of pseudo-parallel sentences cannot be guaranteed . |
| Approach: | They propose a method to improve the quality of pseudo-parallel sentences . they use token-level translations to correct mis-translated tokens . |
| Outcome: | Empirical results show that the proposed method outperforms baselines on widely used datasets. |
Improving Vietnamese-English Medical Machine Translation (2024.lrec-main)
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| Challenge: | Existing high-quality Vietnamese-English parallel datasets are inadequate for translation training. |
| Approach: | They introduce a high-quality Vietnamese-English parallel dataset for medical translation . they compare Google Translate, ChatGPT, and pre-trained bilingual/multilingual models . |
| Outcome: | The proposed dataset is compared with translation models from Google Translate and ChatGPT. |
InaGVAD : A Challenging French TV and Radio Corpus Annotated for Speech Activity Detection and Speaker Gender Segmentation (2024.lrec-main)
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| Challenge: | InaGVAD is an audio corpus collected from 10 French radio and 18 TV channels categorized into 4 groups: generalist radio, music radio, news TV, and generalist TV. |
| Approach: | They propose to use an audio corpus from 10 French radio and 18 TV channels to represent the acoustic diversity of French audiovisual programs. |
| Outcome: | The proposed system is trained on a single hour of data and achieved competitive results. |
In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing approaches to solve few-shot aspect-based sentiment analysis (ABSA) are suboptimal for this task because of in-context examples . |
| Approach: | They propose to retrieve in-context examples for few-shot aspect-based sentiment analysis . they construct positive and negative pairs from three perspectives and train the retriever . |
| Outcome: | The proposed retrieval framework outperforms baselines on four ABSA datasets. |
Incorporating Lexical and Syntactic Knowledge for Unsupervised Cross-Lingual Transfer (2024.lrec-main)
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| Challenge: | Unsupervised cross-lingual transfer is a process of transferring knowledge between languages without explicit supervision. |
| Approach: | They propose a framework that combines lexical and syntactic knowledge to enhance learning . they use a code-switching technique to implicitly teach lexica and a syntaktic-based graph attention network to help encode syntakic structure. |
| Outcome: | The proposed framework outperforms baselines of zero-shot cross-lingual transfer with 1.0 3.7 points on text classification, named entity recognition, and semantic parsing tasks. |
Incorporating Word-level Phonemic Decoding into Readability Assessment (2024.lrec-main)
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| Challenge: | a recent study suggests that automatic readability assessment is not able to provide interpretability for teachers and educators. |
| Approach: | They propose to incorporate phonetic and orthographic features into automatic readability assessment by handcrafted feature sets. |
| Outcome: | a new feature set shows comparable performance to larger feature sets on grade-level classification in english . authors say the model improves on multiple readability datasets but lacks interpretability . |
IndicFinNLP: Financial Natural Language Processing for Indian Languages (2024.lrec-main)
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| Challenge: | IndicFinNLP is a collection of 9 datasets relating to FinNLP for three Indian languages. |
| Approach: | They propose to use financial NLP to detect exaggerated numerals in financial texts written in Hindi, Bengali, and Telugu. |
| Outcome: | The proposed framework detects exaggerated numerals in financial texts written in Hindi, Bengali, and Telugu. |
Indic-TEDST: Datasets and Baselines for Low-Resource Speech to Text Translation (2024.lrec-main)
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| Challenge: | Speech-to-text Translation (ST) tasks are performed by human translators with proficiency in both the source and target languages. |
| Approach: | a new study compares the performance of SOTA ST models on low-resource languages . the authors propose to use a dataset to compare the models on high-resourced languages based on the results of their research . |
| Outcome: | a new study shows that only a few models have performed well on low-resource languages . the results indicate the need for specialized models for low- and high-resourced languages based on the dataset . |
IndirectQA: Understanding Indirect Answers to Implicit Polar Questions in French and Spanish (2024.lrec-main)
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| Challenge: | polar questions are common in spoken dialogues and expect exactly one of two answers (yes/no) but conversational systems struggle to interpret them. |
| Approach: | They propose to use subtitle data to interpret indirect answers in french and spanish . they use subtitles to broaden polar questions to include also implicit polar ones . |
| Outcome: | The proposed corpus of indirect answers shows that the task is challenging and challenging . the baseline accuracy scores drop from 61.43 on english to 44.06 for french and Spanish . |
Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis (2024.lrec-main)
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| Challenge: | Existing methods for inductive knowledge graph completion are underperforming . implausible entities are not ranked and only the most informative path is taken into account . |
| Approach: | They propose to use a rule-based approach to find plausible triples missing from a given KG. |
| Outcome: | The proposed models outperform state-of-the-art methods on inductive knowledge graph completion. |
InferBR: A Natural Language Inference Dataset in Portuguese (2024.lrec-main)
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| Challenge: | Portuguese has few NLI-annotated datasets created through automatic translation followed by manual checking. |
| Approach: | They propose to generate premises and hypotheses using a semiautomatic process to generate sentences and manually check the annotations. |
| Outcome: | The proposed dataset is better at recognizing entailment classes in other Portuguese datasets than the reverse. |
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks (2024.lrec-main)
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| Challenge: | InfFeed uses influence functions to compute the influential instances for a target instance. |
| Approach: | They propose an apparatus that uses influence functions to compute the influential instances for a target instance. |
| Outcome: | The proposed model outperforms the state-of-the-art baselines by 4% for hate speech classification, 3.5% for stance classification, and 3% for irony and 2% for sarcasm detection. |
InfoEnh: Towards Multimodal Sentiment Analysis via Information Bottleneck Filter and Optimal Transport Alignment (2024.lrec-main)
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| Challenge: | Existing methods for multi-modal sentiment analysis have been developed to overcome these challenges. |
| Approach: | They propose a method that utilizes a masking technique as the bottleneck for information filtering and integrates all modalities into a common feature space via domain adaptation. |
| Outcome: | Extensive experiments on two benchmark MSA datasets show the proposed method performs better than baselines. |
Information Extraction with Differentiable Beam Search on Graph RNNs (2024.lrec-main)
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| Challenge: | Existing approaches to information extraction suffer from exposure bias due to discrepancy between training and decoding. |
| Approach: | They propose to cast graph generation as auto-regressive sequence labeling and make it aware of decoding procedure by using differentiable beam search. |
| Outcome: | The proposed model outperforms its non-decoding-aware version on ACE05 and ConLL04 datasets. |
INMT-Lite: Accelerating Low-Resource Language Data Collection via Offline Interactive Neural Machine Translation (2024.lrec-main)
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Harshita Diddee, Anurag Shukla, Tanuja Ganu, Vivek Seshadri, Sandipan Dandapat, Monojit Choudhury, Kalika Bali
| Challenge: | Interactive Neural Machine Translation (INMT) systems can be used to promote data collection in several under-resourced languages, but are often not adapted to the deployment constraints native language speakers operate in. |
| Approach: | They propose to use interactive neural machine translation systems to promote data collection in several under-resourced languages by integrating three different modes of Internet-independent deployment and four assistive interfaces suitable for data-sparse languages. |
| Outcome: | The proposed model improves the data generation experience of community members along multiple axes without compromising on the quality of the generated translations. |
Integrating Headedness Information into an Auto-generated Multilingual CCGbank for Improved Semantic Interpretation (2024.lrec-main)
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| Challenge: | Combinatory Categorial Grammar is a grammar formalism that provides a transparent interface between syntax and semantics. |
| Approach: | They propose an algorithm that adds semantic representations to existing CCG derivations by combining them with predefined combinatory rules. |
| Outcome: | The proposed method produces bare CCG derivations without any accompanying semantic representations and limits its general applicability. |
Integrating Representation Subspace Mapping with Unimodal Auxiliary Loss for Attention-based Multimodal Emotion Recognition (2024.lrec-main)
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Xulong Du, Xingnan Zhang, Dandan Wang, Yingying Xu, Zhiyuan Wu, Shiqing Zhang, Xiaoming Zhao, Jun Yu, Liangliang Lou
| Challenge: | Existing methods to identify emotions rely on a large modality gap in their representations . |
| Approach: | They propose a representation subspace mapping module that maps each modality into two distinct subspaces and a cross-modality attention module that leverages auxiliary loss to remove the noise unrelated to emotion classification. |
| Outcome: | The proposed approach achieves superior performance to state-of-the-art MER methods on the IEMOCAP and MSP-Improv datasets. |
Intent-Aware and Hate-Mitigating Counterspeech Generation via Dual-Discriminator Guided LLMs (2024.lrec-main)
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| Challenge: | Hate speech is an aggressive expression that incites hatred towards specific groups based on their group identity. |
| Approach: | They propose an LLMs-based framework for counterspeech generation that uses intent-aware discriminators to decode intents of LLM models. |
| Outcome: | The proposed framework matches intents with hate mitigation intents and performs well. |
Intention and Face in Dialog (2024.lrec-main)
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| Challenge: | a theory of politeness focuses on how intentions mediate the planning of turns which impose upon face. |
| Approach: | They propose to train a model which classifies intention and politeness using existing corpus and a new model which incorporates annotations. |
| Outcome: | The proposed model improves face act detection for minority classes and points to a close relationship between aspects of face and intent. |
InteRead: An Eye Tracking Dataset of Interrupted Reading (2024.lrec-main)
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| Challenge: | Eye movements during reading can provide insights into cognitive processes and language comprehension, but the scarcity of reading data with interruptions hampers advances in the development of intelligent learning technologies. |
| Approach: | They propose a dataset of eye movements during reading that includes eye movements and word frequency effects. |
| Outcome: | The proposed dataset shows that interruptions, word length and word frequency effects significantly impact eye movements during reading. |
Interpretable Assessment of Speech Intelligibility Using Deep Learning: A Case Study on Speech Disorders Due to Head and Neck Cancers (2024.lrec-main)
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Sondes Abderrazek, Corinne Fredouille, Alain Ghio, Muriel Lalain, Christine Meunier, Mathieu Balaguer, Virginie Woisard
| Challenge: | Using deep learning, speech disorders can be evaluated by perceptual measures, but they are subject to subjectivity and lack of reproducibility. |
| Approach: | They propose to use deep-learning to explain hidden representations in a deep- learning speech model to provide a deeper understanding of the final intelligibility assessment of patients with Head and Neck Cancers. |
| Outcome: | The proposed approach predicts speech intelligibility and severity of patients with Head and Neck Cancers while giving relevant interpretations of the final assessment at the phonemes and phonetic feature levels. |
Interpretable Short Video Rumor Detection Based on Modality Tampering (2024.lrec-main)
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| Challenge: | Existing methods to detect rumors from the perspective of modality tampering are labor-intensive and time-consuming. |
| Approach: | They propose a short video rumor detection framework that integrates modality tampering detection and inter-modal matching into a model to detect modality-tampers and interpretability mechanisms to make the results more reasonable. |
| Outcome: | The proposed model improves on the short video rumor dataset by 4.6%-12% compared with other models and can explain whether the short clip is a rumour or not through the perspective of modality tampering. |
Interpreting Themes from Educational Stories (2024.lrec-main)
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| Challenge: | Recent advances in machine reading comprehension (MRC) have centered on literal comprehension, referring to the surface-level understanding of content. |
| Approach: | They propose a dataset specifically designed for interpretive comprehension of educational narratives, providing corresponding well-edited theme texts. |
| Outcome: | The proposed dataset spans genres and cultural origins and includes human-annotated theme keywords with varying levels of granularity. |
Intrinsic Subgraph Generation for Interpretable Graph Based Visual Question Answering (2024.lrec-main)
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| Challenge: | Visual Question Answering (VQA) is acknowledged as a challenging multi-modal task for Machine Learning (ML). |
| Approach: | They propose an interpretable approach for graph-based Visual Question Answering . their model is designed to intrinsically produce a subgraph during the question-answering process as its explanation . |
| Outcome: | The proposed model outperforms existing explainable methods on a graph-based VQA dataset. |
Introducing a Parsed Corpus of Historical High German (2024.lrec-main)
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| Challenge: | outlines the development of the Indiana Parsed Corpus of (Historical) High German . outlines selection of texts, decisions on part-of-speech tags and other labels . |
| Approach: | They propose to build a parsed German corpus that spans Germanic from 1050 to 1950 . they propose to use Penn-style treebanks to capture syntactic relationships between words . |
| Outcome: | The proposed corpus spans Germanic languages from 1050 to 1950 and illustrative annotation issues unique to the language. |
Introducing CQuAE : A New French Contextualised Question-Answering Corpus for the Education Domain (2024.lrec-main)
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| Challenge: | a new question answering corpus in french is designed to educational domain . we propose more complex questions and can justify the answers on validated material . |
| Approach: | They propose a question answering corpus in French designed to educational domain . they propose to propose more complex questions and justify answers on validated material . |
| Outcome: | The proposed question answering corpus is designed to be useful in educational domain . it proposes more complex questions and can justify answers on validated material . the proposed corpus could be used in the education domain, but it's not yet ready for use . |
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study (2024.lrec-main)
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| Challenge: | Existing models for stance detection are not robust enough to be used in a viewpoint-diverse news recommender because the news constantly has new discussion topics. |
| Approach: | They propose to use two stance task definitions (Pro/Con versus Same Side Stance) and two LLM architectures (bi-encoding versus cross-encode) to test model performance. |
| Outcome: | The proposed models outperform the same side-stance definition and other models on stance across different topics. |
IR2: Information Regularization for Information Retrieval (2024.lrec-main)
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| Challenge: | Effective information retrieval (IR) in settings with limited training data remains a challenging task. |
| Approach: | They propose a technique for reducing overfitting during synthetic data generation . they use DORIS-MAE, ArguAna, and WhatsThatBook as examples . |
| Outcome: | The proposed technique outperforms previous methods and reduces cost by 50% on three recent IR tasks characterized by complex queries. |
I Remember You!: SUI Corpus for Remembering and Utilizing Users’ Information in Chat-oriented Dialogue Systems (2024.lrec-main)
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| Challenge: | Existing methods for remembering and utilizing information on users in system utterances do not always fit the context of the dialogue. |
| Approach: | They propose to use user information to fill in utterance templates but the utterrances do not always fit the context. |
| Outcome: | The proposed system can remember and utilize user information on users in dialogues while keeping appropriateness for the context. |
ÌròyìnSpeech: A Multi-purpose Yorùbá Speech Corpus (2024.lrec-main)
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| Challenge: | rynSpeech corpus is a dataset that can be used for both Text-to-Speecher (TTS) and Automatic Speech Recognition (ASR) speakers of many African languages have no access to voice-enabled applications in their native languages. |
| Approach: | They propose a dataset to collect Yorùbá speech data that can be used for both TTS and ASR tasks. |
| Outcome: | The proposed dataset can generate a good quality model with as little as 5 hours of speech . the results are consistent with previous studies on the Yorùbá language . |
Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization (2024.lrec-main)
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| Challenge: | Existing methods to improve language models require manual ranking and annotators. |
| Approach: | They propose a self-supervised text ranking approach for applying Proximal-Policy-Optimization to fine-tune language models while eliminating the need for human annotators. |
| Outcome: | The proposed method significantly outperforms baselines regarding BLEU, GLEU, and METEOR scores on three tasks and is consistent with humans. |
Is Gender Reference Gender-specific? Studies in a Polar Domain (2024.lrec-main)
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| Challenge: | a german-language newspaper corpus contains a large number of newspaper texts with gender tags . a gender-specific way of gender reference is investigated by using a polar load-based classifier . |
| Approach: | They investigate how gender authorship influences positive and negative gender reference . they use a german valence lexicon, a German polar load lexicone and a verb-based analysis of the polar role a noun plays . |
| Outcome: | The proposed method mainly uses a German valence lexicon, a german polar load lexical, and a polar lexicogramma. |
Is It Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models (2024.lrec-main)
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| Challenge: | Text simplification and elaboration tasks are limited to only relatively altering the readability of texts to cater to a diverse audience. |
| Approach: | They propose to generate 8 versions of a text at different readability levels using ChatGPT and Llama-2 and introduce a two-step process to generate paraphrases. |
| Outcome: | The proposed task requires the generation of 8 versions at various target readability levels for each input text. |
Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LLM on Automatic Paper Reviewing Tasks (2024.lrec-main)
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| Challenge: | Existing datasets and methods targeting review-related tasks have not thoroughly inspected model's review ability. |
| Approach: | They propose to evaluate GPT-3.5 and GPT-4 on two types of tasks under different settings: the score prediction task and the review generation task. |
| Outcome: | The proposed model can give passable decisions (> 60% accuracy) on single options, but it always makes mistakes. |
Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation (2024.lrec-main)
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| Challenge: | Existing approaches to modularity are limited to the case of pre-trained modules in a pre-training language model. |
| Approach: | They propose a method that allows the transfer of pre-trained PEFT modules between incompatible PLMs without any change in the inference complexity. |
| Outcome: | The proposed method allows the transfer of modules between incompatible PLMs without any change in the inference complexity. |
ISO 24617-12: A New Standard for Semantic Annotation (2024.lrec-main)
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| Challenge: | ISO 24617-12 is a proposed new standard 1 for the annotation of quantification phenomena in natural language. |
| Approach: | This paper proposes an annotation scheme for quantification phenomena in natural language as part of the ISO Semantic Annotation Framework (ISO 24617) it combines ideas from the theory of generalised quantifiers, from neo-Davidsonian event semantics, and from Discourse Representation Theory. |
| Outcome: | The proposed standard 1 is an annotation scheme for quantification phenomena in natural language. |
IsraParlTweet: The Israeli Parliamentary and Twitter Resource (2024.lrec-main)
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| Challenge: | IsraParlTweet is a linked corpus of parliamentary discussions from the Knesset between 1992-2023 and Twitter posts made by Members of the Kneset between 2008-2023. |
| Approach: | They propose a linked corpus of parliamentary discussions from the Knesset between 1992-2023 and Twitter posts made by Members of the Kneset between 2008-2023. |
| Outcome: | IsraParlTweet can be used to conduct quantitative and qualitative analyses and provide valuable insights into political discourse in Israel. |
Is Spoken Hungarian Low-resource?: A Quantitative Survey of Hungarian Speech Data Sets (2024.lrec-main)
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Peter Mihajlik, Katalin Mády, Anna Kohári, Fruzsina Sára Fruzsina, Gábor Kiss, Tekla Etelka Gráczi, A. Seza Doğruöz
| Challenge: | Existing data sets in Hungarian are limited in quality and quality . however, it is difficult to train a modern automatic speech recognition system with thousands of hours of transcribed speech. |
| Approach: | They propose to analyze available speech data sets in Hungarian in five categories . they estimate that the available data sets are 2800 hours across 7500 speakers . |
| Outcome: | The available data sets in spoken Hungarian are compared to other languages and are estimated to be 2800 hours in size . however, their distribution and alignment to real-life tasks are far from optimal indicating the need for larger-scale natural language speech data sets. |
Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks (2024.lrec-main)
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| Challenge: | a recent study focused on intrinsic evaluation, which assesses the quality of summaries, e.g. coherence, fluency, and informativeness, but it focused on task-based extrinsic evaluation to determine the usefulness of summarizations. |
| Approach: | They incorporate three downstream tasks to measure the usefulness of summaries . they find that fine-tuned models produce more useful summary across all three tasks . |
| Outcome: | The proposed model produces more useful summaries across all three tasks compared to zero-shot models . human evaluation provides more reliable performance assessment compared with automatic methods . |
IT2ACL Learning Easy-to-Hard Instructions via 2-Phase Automated Curriculum Learning for Large Language Models (2024.lrec-main)
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| Challenge: | Existing studies have focused on the pre-training phase of large language models, but this study focuses on the learning phase of pre-trained LLMs. |
| Approach: | They propose a 2-phase automated curriculum learning guided instruction tuning framework that learns easy-to-hard instructions in a self-adjusting dynamic manner. |
| Outcome: | The proposed framework unlocks latent ability in pre-trained large language models and achieving superior performance across diverse tasks. |
IT5: Text-to-text Pretraining for Italian Language Understanding and Generation (2024.lrec-main)
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| Challenge: | Xue et al., 2022) use the text-to-text paradigm to train multilingual models. |
| Approach: | They introduce the first family of encoder-decoder transformer models pretrain specifically on Italian and introduce the ItaGen benchmark to evaluate the models' performance. |
| Outcome: | The proposed model outperforms models with multilingual baselines and the original model on English data. |
Italian Word Embeddings for the Medical Domain (2024.lrec-main)
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| Challenge: | Neural word embeddings have proven valuable in the development of medical applications, but for the Italian language, there are no publicly available corpora, embedds, or evaluation resources tailored to this domain. |
| Approach: | They propose to use a corpus of medical texts to generate neural word embeddings in Italian using Metathesaurus concept graphs. |
| Outcome: | The results show that the new embeddings correlate well with human judgments regarding similarity and relatedness of medical concepts. |
It’s Not under the Lamppost: Expanding the Reach of Conversational AI (2024.lrec-main)
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| Challenge: | Focused probes into the capabilities of language-based assistants easily reveal significant areas of brittleness that demonstrate large gaps in their coverage. |
| Approach: | They propose a process for collecting specific kinds of data to uncover these gaps and an annotation scheme for system responses. |
| Outcome: | The proposed system includes both Conventional and GenAI systems, including ChatGPT and Bard/Gemini. |
JaParaPat: A Large-Scale Japanese-English Parallel Patent Application Corpus (2024.lrec-main)
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| Challenge: | a recent study has demonstrated that patent translation accuracy improves as the amount of training data or the number of model parameters increases. |
| Approach: | They construct a bilingual corpus of Japanese-English patent application data from 2000 to 2021 . they extracted 1.4M Japanese- English document pairs and extracted 350M sentence pairs . |
| Outcome: | The proposed method improves translation accuracy by 20 bleu points . it is the first publicly available large-scale Japanese-English patent corpus . |
Jargon: A Suite of Language Models and Evaluation Tasks for French Specialized Domains (2024.lrec-main)
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Vincent Segonne, Aidan Mannion, Laura Cristina Alonzo Canul, Alexandre Daniel Audibert, Xingyu Liu, Cécile Macaire, Adrien Pupier, Yongxin Zhou, Mathilde Aguiar, Felix E. Herron, Magali Norré, Massih R Amini, Pierrette Bouillon, Iris Eshkol-Taravella, Emmanuelle Esperança-Rodier, Thomas François, Lorraine Goeuriot, Jérôme Goulian, Mathieu Lafourcade, Benjamin Lecouteux, François Portet, Fabien Ringeval, Vincent Vandeghinste, Maximin Coavoux, Marco Dinarelli, Didier Schwab
| Challenge: | Pretrained language models are the de facto backbone of most state-of-the-art NLP systems. |
| Approach: | They propose a family of domain-specific pretrained PLMs for French focusing on three important domains: transcribed speech, medicine, and law. |
| Outcome: | The proposed models perform better on transcribed speech, medicine, and law domains than state-of-the-art models on a diverse set of tasks and datasets. |
JCoLA: Japanese Corpus of Linguistic Acceptability (2024.lrec-main)
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| Challenge: | Neural language models have exhibited outstanding performance in downstream tasks, yet there is limited understanding regarding the extent of their internalization of syntactic knowledge. |
| Approach: | They introduce a dataset that analyzes sentences annotated with binary acceptability judgments from linguistic textbooks and handbooks and splits them into in-domain and out-of-domain data. |
| Outcome: | The proposed datasets show that models can surpass human performance for in-domain data while no models can exceed human performance on out-of-domain datasets. |
J-CRe3: A Japanese Conversation Dataset for Real-world Reference Resolution (2024.lrec-main)
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Nobuhiro Ueda, Hideko Habe, Akishige Yuguchi, Seiya Kawano, Yasutomo Kawanishi, Sadao Kurohashi, Koichiro Yoshino
| Challenge: | Existing studies have ground referential expressions in language to real-world objects for cooperative action generation. |
| Approach: | They propose a Japanese Conversation dataset for real-world reference resolution that ground referential expressions to visual information observed in egocentric views. |
| Outcome: | The proposed dataset contains egocentric video and dialogue audio of real-world conversations between two people acting as a master and assistant robot at home. |
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models (2024.lrec-main)
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| Challenge: | Document question answering is a task of question answering on given documents such as reports, slides, pamphlets, and websites. |
| Approach: | They propose a large-scale document-based QA dataset that requires both visual and textual information to answer questions. |
| Outcome: | The proposed dataset incorporates multiple categories of questions and unanswerable questions from the document for realistic question-answering applications. |
JEMHopQA: Dataset for Japanese Explainable Multi-Hop Question Answering (2024.lrec-main)
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| Challenge: | a dataset for explainable QA in Japanese is available for many languages, but not in other languages. |
| Approach: | They present a multi-hop QA dataset based on Japanese Wikipedia . it includes question-answer pairs and supporting evidence in the form of derivation triples . they show that the dataset is sufficiently challenging for state-of-the-art LLMs based upon this dataset . |
| Outcome: | The proposed dataset is based on Japanese Wikipedia and can be used to evaluate QA tasks. |
JFLD: A Japanese Benchmark for Deductive Reasoning Based on Formal Logic (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have proficiently solved a broad range of tasks with their rich knowledge but struggle with logical reasoning. |
| Approach: | They propose a deductive reasoning benchmark for Japanese that assesses logical reasoning abilities isolated from knowledge and various reasoning rules. |
| Outcome: | The proposed benchmarks assess whether LLMs can generate logical steps to (dis)prove a given hypothesis based on a set of facts. |
JLBert: Japanese Light BERT for Cross-Domain Short Text Classification (2024.lrec-main)
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| Challenge: | Short Texts face the problem of being short, equivocal, and non-standard. |
| Approach: | They propose a Japanese BERT model with cross-domain functionality and comparable accuracy to State of the Art models. |
| Outcome: | The proposed model outperforms state-of-the-art models on three short text datasets by 1.5% across various domains. |
JL-Hate: An Annotated Dataset for Joint Learning of Hate Speech and Target Detection (2024.lrec-main)
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| Challenge: | Existing data resources for the detection of hate speech focus on text sequence classification, but the target of hateful content is lacking. |
| Approach: | They propose a tweet dataset for the task of joint learning of hate speech detection and target detection called JL-Hate. |
| Outcome: | The proposed dataset performs similar tasks to the existing datasets in sequence and token classification tasks. |
JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset (2024.lrec-main)
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| Challenge: | Existing datasets for task-oriented dialogue systems in English are limited compared to Japanese. |
| Approach: | They evaluated the dialogue state tracking and response generation capabilities of Japanese language datasets using multi-domain task-oriented dialogues. |
| Outcome: | The proposed dataset provides a benchmark that is on par with MultiWOZ2.2 and the latest large language model (LLM)-based methods. |
Joint Annotation of Morphology and Syntax in Dependency Treebanks (2024.lrec-main)
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| Challenge: | Syntactic treebanks have been in development since the 1970s . they are now available for a vast array of languages from across the globe . |
| Approach: | They propose new formats to annotate syntactic and morphological relations in a dependency treebank using distributional criteria for the choice of the head of any combination. |
| Outcome: | The proposed formats are compatible with the UD schema for syntactic treebanks. |
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialogue Policy Learning (2024.lrec-main)
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Wai-Chung Kwan, Huimin Wang, Hongru Wang, Zezhong Wang, Bin Liang, Xian Wu, Yefeng Zheng, Kam-Fai Wong
| Challenge: | Dialogue policy learning (DPL) aims to determine an abstract representation (also known as action) to guide what the response should be. |
| Approach: | They propose a joint Transformer-based model that generates a token-grained policy that allows more dynamic dialogue action generation without the need for predefined action candidates. |
| Outcome: | The proposed model outperforms existing models showing improvements of 9% and 13% in success rate and 34% and 37% in diversity of dialogue actions across two benchmark dialogue modeling tasks. |
JRC-Names-Retrieval: A Standardized Benchmark for Name Search (2024.lrec-main)
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| Challenge: | a lack of research on the ability to search through databases of personal and organization name is hindering this area . specialized indexing methods which understand the structure of names are essential to efficient performance. |
| Approach: | They propose a neural solution which provides a 12% performance gain over baselines . they propose specialized indexing methods which understand the structure of names . |
| Outcome: | The proposed solution shows up to 12% performance gain over baselines . the proposed solution is compared against a similar dataset from a different dataset . |
J-SNACS: Adposition and Case Supersenses for Japanese Joshi (2024.lrec-main)
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| Challenge: | adpositions are used to mark a variety of semantic relations in languages such as English and Korean. |
| Approach: | They propose a Japanese extension of the SNACS framework for annotating adpositions in corpora from several languages. |
| Outcome: | The proposed framework captures similarities not seen in multilingual embedding space. |
Jump to Conclusions: Short-Cutting Transformers with Linear Transformations (2024.lrec-main)
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| Challenge: | Transformer-based language models create hidden representations of inputs at every layer, but only use final-layer representations for prediction. |
| Approach: | They propose a method for casting hidden representations as final representations, bypassing transformer computation in-between. |
| Outcome: | The proposed method produces more accurate predictions from hidden layers across various model scales, architectures, and data distributions. |
KazEmoTTS: A Dataset for Kazakh Emotional Text-to-Speech Synthesis (2024.lrec-main)
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| Challenge: | Using KazEmoTTS, synthesized speech still faces significant difficulties in expressing paralinguistic features such as emotions. |
| Approach: | They created a KazEmoTTS dataset with 54,760 audio-text pairs and a TTS model trained on the KazEmpoTTs dataset. |
| Outcome: | The proposed dataset yields an MCD score of 6.02 to 7.67 and a MOS of 3.51 to 3.57. |
KazParC: Kazakh Parallel Corpus for Machine Translation (2024.lrec-main)
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| Challenge: | Statistical machine translation gained ground over rule-based machine translation in the late 1990s thanks to its ability to learn from large bilingual corpora. |
| Approach: | They propose to develop a parallel corpus for machine translation across Kazakh, English, Russian, and Turkish. |
| Outcome: | The proposed model outperforms Google Translate and Yandex Translate in terms of performance and evaluation metrics. |
KazQAD: Kazakh Open-Domain Question Answering Dataset (2024.lrec-main)
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| Challenge: | KazQAD contains just under 6,000 unique questions with extracted short answers and nearly 12,000 passage-level relevance judgements. |
| Approach: | They introduce a Kazakh open-domain question answering dataset that can be used in reading comprehension and full ODQA settings. |
| Outcome: | The proposed dataset can be used in reading comprehension and full ODQA settings, as well as for information retrieval experiments. |
KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes (2024.lrec-main)
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| Challenge: | Currently, sentiment analysis is a widely employed text classification task that involves extracting the sentiment expressed by individuals towards a variety of entities. |
| Approach: | They propose to use KazSAnDRA to automate Kazakh sentiment analysis by developing and evaluating four machine learning models for polarity and score classification. |
| Outcome: | The proposed dataset is the first and largest publicly available dataset of its kind. |
KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Knowledge graph completion (KGC) is a critical task to predict missing facts among entities. |
| Approach: | They propose a knowledge-constrained generative re-ranking method based on generative large language models for KGC that can predict missing facts among entities. |
| Outcome: | The proposed method achieves state-of-the-art performance on four datasets and 9.0% and 11.1% compared to the previous methods. |
KCL: Few-shot Named Entity Recognition with Knowledge Graph and Contrastive Learning (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) is a key subtask in natural language processing but is limited to a few labeled samples. |
| Approach: | They propose a few-shot method that harnesses the power of Knowledge Graph and Contrastive Learning to improve the prototypical semantic space learning. |
| Outcome: | The proposed method improves the prototypical semantic space learning by using knowledge graphs and contrastive learning to learn the label semantic representation. |
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning (2024.lrec-main)
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| Challenge: | General pre-trained language models (PLMs) leverage relation triples from knowledge graphs (KGs) and integrate external data sources into language models via self-supervised learning. |
| Approach: | They propose to learn Knowledge-Enhanced language representations with Hierarchical Reinforcement Learning (KEHRL) to detect positions for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
| Outcome: | The proposed model can detect essential positions in texts for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
KET-QA: A Dataset for Knowledge Enhanced Table Question Answering (2024.lrec-main)
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| Challenge: | Existing datasets that ignore the challenge of missing knowledge in TableQA are limited in their use. |
| Approach: | They propose to use a knowledge base as the external knowledge source for TableQA and construct a dataset with fine-grained gold evidence annotation. |
| Outcome: | The proposed model achieves remarkable performance improvements on three different settings, but still lags behind the human-level performance. |
Keyphrase Generation: Lessons from a Reproducibility Study (2024.lrec-main)
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| Challenge: | Reproducibility studies are used to verify the validity of a scientific method, but what else can we learn from such experiments? |
| Approach: | They use Keyphrase Generation to examine reproducibility under different conditions . they draw conclusions on state of the art in KPG and provide guidelines for researchers . |
| Outcome: | The proposed models are compared under the same or varied conditions and provide guidelines for reporting results in a more comprehensive manner. |
KGConv, a Conversational Corpus Grounded in Wikidata (2024.lrec-main)
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| Challenge: | a large corpus of 71k English conversations contains on average 8.6 questions . Unlike open domain and task-oriented dialogues, information seeking conversations are driven by the desire to acquire or evaluate knowledge. |
| Approach: | They propose a large corpus of 71k English conversations where each question-answer pair is grounded in a Wikidata fact. |
| Outcome: | The proposed dataset can be used for knowledge-based, conversational question generation . it can also be used to generate single-turn questions from Wikidata triples, question rewriting, question answering from conversation or knowledge graphs and quiz generation. |
Khan Academy Corpus: A Multilingual Corpus of Khan Academy Lectures (2024.lrec-main)
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| Challenge: | a dataset of 10122 hours in 87394 recordings is presented in a new journal . 43% of recordings have human-written subtitles, covering a total of 137 languages. |
| Approach: | They present a Khan Academy corpus with 10122 hours in 87394 recordings . 43% of recordings have human-written subtitles, and 137 languages are included . |
| Outcome: | The dataset can be used to train multilingual speech recognition and translation models. |
Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic Information (2024.lrec-main)
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| Challenge: | Existing datasets for automatic speech recognition (ASR) in the endangered Kichwa language have been limited. |
| Approach: | They present Killkan, the first dataset for automatic speech recognition (ASR) in the Kichwa language, an indigenous language of Ecuador. |
| Outcome: | The proposed dataset shows that it can be used to build an automatic speech recognition system for the endangered language with reliable quality despite its small size. |
KIT-19: A Comprehensive Korean Instruction Toolkit on 19 Tasks for Fine-Tuning Korean Large Language Models (2024.lrec-main)
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| Challenge: | Instruction tuning on large language models is an essential process for models to function well and achieve high performance in the specific tasks. |
| Approach: | They propose to use KIT-19 as an instruction dataset for the development of LLM in Korean to demonstrate its effectiveness. |
| Outcome: | The proposed model outperforms existing Korean LLMs. |
Know-Adapter: Towards Knowledge-Aware Parameter-Efficient Transfer Learning for Few-shot Named Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition (NER) is a fundamental task in natural language processing. |
| Approach: | They propose a knowledgeable adapter to incorporate structure and semantic knowledge of knowledge graphs into PLMs for few-shot NER. |
| Outcome: | The proposed adapter improves the quality of retrieved information by adding explicit knowledge from external sources to PEFTs. |
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection (2024.lrec-main)
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| Challenge: | Recent studies have used word embedding and deep learning to automate ADE detection from text, but they did not incorporate explicit medical knowledge about drugs and adverse reactions or the corresponding feature learning. |
| Approach: | They propose to integrate medical knowledge into ADE detection from text . they use contextualized embeddings from pretrained language models and convolutional graph neural networks to learn features differently for different types of nodes in the graph. |
| Outcome: | The proposed model outperforms existing models on four public datasets and shows that it is based on medical knowledge and embeddings from pretrained language models and neural networks. |
Knowledge-aware Attention Network for Medication Effectiveness Prediction (2024.lrec-main)
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| Challenge: | Existing effectiveness prediction methods focus on one specific medicine, one specific disease, or one specific lab test, making it hard to extend to general medicines and diseases in hospital/ICU scenarios. |
| Approach: | They propose to use knowledge enhanced module to incorporate external knowledge about medications and a medical feature learning module to determine the interaction between diagnosis and medications. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a public dataset showing that it significantly outperformed existing models. |
Knowledge Enhanced Pre-training for Cross-lingual Dense Retrieval (2024.lrec-main)
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| Challenge: | Existing mPLMs neglect the importance of knowledge in cross-lingual dense retrieval. |
| Approach: | They propose a novel mPLM that leverages knowledge to learn language-agnostic semantic representations from a multilingual knowledge base and an annotation of Wiki. |
| Outcome: | The proposed model achieves strong multilingual and cross-lingual retrieval performance with significant improvements over existing mPLMs. |
Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic (2024.lrec-main)
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| Challenge: | Existing methods to determine semantic relation between two arguments in dialogues are limited due to the low information density of text. |
| Approach: | They propose a Knowledge-Enhanced Prompt-Tuning method to enhance DRE model by exploiting trigger and label semantics. |
| Outcome: | The proposed method achieves state-of-the-art in F1 and F1c scores on a DialogRE dataset. |
Knowledge GeoGebra: Leveraging Geometry of Relation Embeddings in Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Knowledge graph embedding models are limited to the algebra and geometry of the entity embeddable space, the algebra of the relation embeddible space, and the interaction between relation and entity embeds. |
| Approach: | They propose a method that leverages the geometry of relation embeddings and generalizes it with the concept of a butterfly curve, consecutively. |
| Outcome: | The proposed model outperforms existing models on the WN18RR, FB15K-237 and YouTube benchmarks. |
Knowledge Graphs for Real-World Rumour Verification (2024.lrec-main)
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| Challenge: | Recent advances in automated rumour verification have limited results in real-world scenarios. |
| Approach: | They propose to use Twitter responses to construct knowledge graphs based on the PHEME dataset to identify discrepancies between the evidence retrieved and PHE ME’s labels. |
| Outcome: | The proposed model outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset. |
Knowledge-Guided Cross-Topic Visual Question Generation (2024.lrec-main)
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| Challenge: | Existing methods for visual question generation use answers or question types as constraints to generate questions. |
| Approach: | They propose a knowledge-guided cross-topic visual question generation task to generate unseen topics in cross-section scenarios. |
| Outcome: | The proposed model outperforms baselines and can generate unseen topic-related questions in cross-topic scenarios. |
Knowledge Triplets Derivation from Scientific Publications via Dual-Graph Resonance (2024.lrec-main)
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| Challenge: | Existing relation extraction methods aim to extract explicit triplet knowledge from documents, but they can hardly perceive unobserved factual relations. |
| Approach: | They propose a novel Extraction-Contextualization-Derivation strategy to generate a document-specific dynamic graph from a shared static knowledge graph. |
| Outcome: | The proposed method can generate richer explicit and implicit relations under the guidance of static and dynamic knowledge topologies. |
KnowVrDU: A Unified Knowledge-aware Prompt-Tuning Framework for Visually-rich Document Understanding (2024.lrec-main)
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Yunqi Zhang, Yubo Chen, Jingzhe Zhu, Jinyu Xu, Shuai Yang, Zhaoliang Wu, Liang Huang, Yongfeng Huang, Shuai Chen
| Challenge: | Existing methods for integrating layout and image features into pre-training language models are not suitable for few-shot settings. |
| Approach: | They propose to reformulate VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method. |
| Outcome: | The proposed framework can be used in few-shot settings and reduces data requirements. |
KoCoSa: Korean Context-aware Sarcasm Detection Dataset (2024.lrec-main)
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| Challenge: | Sarcasm is a form of verbal irony where someone says the opposite of what they mean . misunderstanding this sarcasm may lead to fatal errors in dialogue systems . |
| Approach: | They propose a dataset for the Korean dialogue sarcasm detection task that uses 12.8K daily Korean dialogues and the labels on the last response. |
| Outcome: | The proposed system outperforms strong baselines like large language models in the Korean sarcasm detection task. |
KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark (2024.lrec-main)
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| Challenge: | KoDialogBench is a benchmark designed to assess language models’ conversational capabilities in low-resource languages such as Korean. |
| Approach: | They propose a benchmark to assess language models’ conversational capabilities in Korean by collecting native Korean dialogues from public sources and translating them into diverse test datasets. |
| Outcome: | The proposed benchmark measures the conversational capabilities of language models in Korean, and shows that they can improve on previous training techniques. |
KoFREN: Comprehensive Korean Word Frequency Norms Derived from Large Scale Free Speech Corpora (2024.lrec-main)
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| Challenge: | Word frequency norms in Korean are based on large-scale spontaneous speech corpora, but are not available in minor languages. |
| Approach: | They employ a machine learning-powered POS tagger to create Korean word frequency norms from large-scale spontaneous speech corpora that include a balanced representation of gender and age. |
| Outcome: | The proposed Korean word frequency norms correlate with external studies’ lexical decision time (LDT) and AoA measures. |
Konidioms Corpus: A Dataset of Idioms in Konkani Language (2024.lrec-main)
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| Challenge: | Konkani is a low-resource language spoken by 2.5 million speakers . idiomatic sense processing is challenging due to the nature of idioms . |
| Approach: | They propose to use crowdsourced idiomatic sentence identification to build a corpus for idioms in the Konkani language. |
| Outcome: | The proposed corpus consists of 6520 sentences written in the Konkani language. |
Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition (2024.lrec-main)
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Sungjoo Byun, Jiseung Hong, Sumin Park, Dongjun Jang, Jean Seo, Minseok Kim, Chaeyoung Oh, Hyopil Shin
| Challenge: | Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP) yet, there is no open-source medical NER dataset specifically for Korean. |
| Approach: | They used ChatGPT to construct an open-source Korean NER dataset . they found 20% increase in medical NER performance compared to general Korean ner datasets. |
| Outcome: | The KBMC dataset shows an impressive 20% increase in medical NER performance compared to models trained on general Korean NER datasets. |
Korean Disaster Safety Information Sign Language Translation Benchmark Dataset (2024.lrec-main)
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Wooyoung Kim, TaeYong Kim, Byeongjin Kim, Myeong Jin MJ Lee, Gitaek Lee, Kirok Kim, Jisoo Cha, Wooju Kim
| Challenge: | Sign language is a crucial means of communication for deaf communities. |
| Approach: | They propose to refine Korean sign language translation datasets and release them . they show baseline performance varies depending on tokenization method applied to gloss sequences . |
| Outcome: | The proposed dataset outperforms baseline and spoken language tokenization methods. |
Kosmic: Korean Text Similarity Metric Reflecting Honorific Distinctions (2024.lrec-main)
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| Challenge: | Existing methods for text similarity measurement focus on the semantic dimension, neglecting the unique linguistic attributes found in languages like Korean. |
| Approach: | They propose a Korean text-similarity metric that encompasses the semantic and tonal facets of a given text pair. |
| Outcome: | The proposed method outperforms existing methods in Korean and other languages . it identifies which methods preserve semantics and tone while preserving similarity . |
KPatch: Knowledge Patch to Pre-trained Language Model for Zero-Shot Stance Detection on Social Media (2024.lrec-main)
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Shuohao Lin, Wei Chen, Yunpeng Gao, Zhishu Jiang, Mengqi Liao, Zhiyu Zhang, Shuyuan Zhao, Huaiyu Wan
| Challenge: | Existing knowledge injection methods fail to understand the semantics of tweets . |
| Approach: | They propose a method to flexibly inject knowledge into a pre-trained language model and adaptively expand tweets context. |
| Outcome: | The proposed method is based on two training stages to flexibly inject knowledge into the pre-trained language model and adaptively expand tweets context. |
K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling (2024.lrec-main)
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| Challenge: | lyric translation studies have focused on Western genres and languages, with no previous study centering on K-pop despite its popularity. |
| Approach: | They propose a singable lyric translation dataset that aligns Korean and English lyrics line-by-line and section-by section. |
| Outcome: | The proposed dataset reveals unique characteristics of K-pop lyric translation, distinguishing it from other extensively studied genres, and constructs a neural lyrical translation model. |
Lˆ2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification (2024.lrec-main)
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| Challenge: | Existing linear GCNs perform neural network operations in Euclidean space, which do not capture tree-like hierarchical structure of graphs. |
| Approach: | They propose a Lorentzian linear GCN framework that maps features into hyperbolic space and performs a feature transformation to capture the underlying tree-like structure of data. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy on standard citation networks datasets and 81.3% on PubMed datasets. |
Labeling Comic Mischief Content in Online Videos with a Multimodal Hierarchical-Cross-Attention Model (2024.lrec-main)
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| Challenge: | Existing systems for detecting questionable content in online media are limited by age, life experiences, socio-cultural values, and cognitive skills. |
| Approach: | They propose a multimodal system for comic mischief detection using video, text, and audio. |
| Outcome: | The proposed system improves existing models and baselines for comic mischief detection and its type classification. |
Labeling Results of Topic Models: Word Sense Disambiguation as Key Method for Automatic Topic Labeling with GermaNet (2024.lrec-main)
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| Challenge: | Existing methods for topic modeling are not suitable for document classification, but they can be used to generate training data from large corpus. |
| Approach: | They propose to use topic modeling and automatic topic labeling to understand large corpora of text. |
| Outcome: | The proposed method is more accurate than existing methods. |
Landmark-Guided Cross-Speaker Lip Reading with Mutual Information Regularization (2024.lrec-main)
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| Challenge: | Lip reading is a process of interpreting silent speech from visual lip movements . but lip reading in cross-speaker scenarios poses a challenging problem due to inter-speech variability . |
| Approach: | They propose to exploit lip landmark-guided visual clues instead of mouth-cropped images as input features. |
| Outcome: | Experimental results show that the proposed approach reduces speaker-specific appearance characteristics in cross-speaker scenarios. |
Language and Speech Technology for Central Kurdish Varieties (2024.lrec-main)
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| Challenge: | a recent study focused on the Kurdish language, a less-resourced Indo-European language spoken by over 30 million speakers. |
| Approach: | They propose to develop resources for language and speech technology for Kurdish . they report the performance of machine translation, automatic speech recognition and language identification . |
| Outcome: | The proposed model is based on transcribing movies and TV series as an alternative to fieldwork. |
Language Models and Semantic Relations: A Dual Relationship (2024.lrec-main)
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| Challenge: | Existing studies on language models for the extraction of semantic relations have focused on injecting semantic knowledge into these models to enhance them. |
| Approach: | They propose to extract lexical semantic relations from a BERT model and inject them into it using unsupervised methods based on semantic similarity at word and sentence levels. |
| Outcome: | The proposed method allows to enrich a BERT model without using any external semantic resource. |
Language Models for Text Classification: Is In-Context Learning Enough? (2024.lrec-main)
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| Challenge: | Existing research on text classification models with prompts is limited in scale and lacks understanding of how these methods compare to more established methods. |
| Approach: | They compare the performance of large and smaller language models with prompts to achieve state-of-the-art performance in many NLP tasks. |
| Outcome: | The proposed models outperform the more standard approaches in binary, multiclass, and multilabel tasks in a large scale evaluation of 16 text classification datasets. |
Language Pivoting from Parallel Corpora for Word Sense Disambiguation of Historical Languages: A Case Study on Latin (2024.lrec-main)
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| Challenge: | Word Sense Disambiguation (WSD) is an important task in NLP . most of the work on this task has been done on contemporary English or other modern languages, leaving challenges posed by low-resource languages and diachronic change open. |
| Approach: | They propose to use existing bilingual corpora instead of native English datasets to generate a Latin WSD model. |
| Outcome: | The proposed approach achieves state-of-the-art on a standard benchmark for Latin WSD. |
Language Technologies as If People Mattered: Centering Communities in Language Technology Development (2024.lrec-main)
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| Challenge: | Developing and deploying language technologies "as if people mattered" requires a reflexive and receptive approach, argues a new position paper . |
| Approach: | They argue that researchers should address linguistic and algorithmic injustice together with language communities to build strong interdisciplinary teams. |
| Outcome: | The authors argue that researchers should address social and linguistic injustice together with language communities to solve the challenges raised by language technologies. |
Language Variety Identification with True Labels (2024.lrec-main)
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Marcos Zampieri, Kai North, Tommi Jauhiainen, Mariano Felice, Neha Kumari, Nishant Nair, Yash Mahesh Bangera
| Challenge: | Language identification datasets are compiled with the assumption that the gold label of each instance is determined by where texts are retrieved from. |
| Approach: | They present a human-annotated multilingual dataset for language variety identification . they use a model to train multiple models to discriminate between different languages . |
| Outcome: | The proposed dataset provides a reliable benchmark toward robust and fairer language variety identification systems. |
LANID: LLM-assisted New Intent Discovery (2024.lrec-main)
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| Challenge: | Data annotation is expensive in Task-Oriented Dialogue systems. |
| Approach: | They propose a framework that leverages Large Language Models' zero-shot capability to enhance the performance of a smaller text encoder on the NID task. |
| Outcome: | The proposed framework surpasses all strong baselines in both unsupervised and semi-supervised settings. |
Large Language Models Are Echo Chambers (2024.lrec-main)
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Jan Nehring, Aleksandra Gabryszak, Pascal Jürgens, Aljoscha Burchardt, Stefan Schaffer, Matthias Spielkamp, Birgit Stark
| Challenge: | Modern large language models and chatbots are subject to criticism in many aspects. |
| Approach: | They show that large language models and chatbots are echo chambers . they annotate inputs and show that all chatbot agree . |
| Outcome: | The proposed models show that they tend to agree with the opinions of their users. |
Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency (2024.lrec-main)
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Toyin D. Aguda, Suchetha Siddagangappa, Elena Kochkina, Simerjot Kaur, Dongsheng Wang, Charese Smiley
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performance in data annotation tasks on general domain datasets, but their effectiveness on domain specific datasets remains under-explored. |
| Approach: | They compare the annotations produced by three LLMs against expert annotators and crowdworkers. |
| Outcome: | The proposed models outperform expert crowdworkers and crowd-sourced annotators on domain specific datasets. |
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
| Outcome: | The proposed approach can be simplified to generate recommendations from the entire pool of items. |
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling (2024.lrec-main)
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| Challenge: | Topic modelling has found extensive use in automatically detecting significant topics within a corpus of documents, but there are certain drawbacks. |
| Approach: | They propose a framework that prompts large language models to generate topics from a given set of documents and establish evaluation protocols to assess the clustering efficacy of LLMs. |
| Outcome: | The proposed model generates relevant topic titles and adheres to human guidelines to refine and merge topics. |
Latent vs Explicit Knowledge Representation: How ChatGPT Answers Questions about Low-Frequency Entities (2024.lrec-main)
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| Challenge: | In this paper, we compare two different approaches to the free-form Question Answering task. |
| Approach: | They propose to use a new benchmark to test knowledge representations on a dynamic benchmark. |
| Outcome: | The proposed benchmark is particularly challenging and the best model answers only on 50% of the questions. |
LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles (2024.lrec-main)
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| Challenge: | Existing evaluation benchmarks, such as MMLU, C-Eval, and GSM8K, evaluate models by posing a variety of problems, including problems about mathematics, science, law, and general knowledge. |
| Approach: | They propose a benchmark which assesses the model’s lateral thinking within an interactive framework. |
| Outcome: | The proposed evaluation benchmark assesses the model’s lateral thinking within an interactive framework. |
LA-UCL: LLM-Augmented Unsupervised Contrastive Learning Framework for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | Experimental results show that our model exceeds the baseline models due to the lack of cognitive ability. |
| Approach: | They propose a LLM-Augmented Unsupervised Contrastive Learning Framework which introduces a cognition-enabled Large Language Model (LLM) for efficient data augmentation and presents corresponding contrastive learning strategies. |
| Outcome: | The proposed model exceeds baseline models on six datasets. |
Layer-wise Regularized Dropout for Neural Language Models (2024.lrec-main)
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| Challenge: | Existing methods to regularize dropout are consistency training and dropout is a problem in many pre-trained neural language models. |
| Approach: | They propose a layer-wise regularized dropout technique which regularizes dropout at the output layer using consistency training. |
| Outcome: | The proposed model can be regarded as a "self-distillation" framework, in which each sub-model generated by dropout is the other's "teacher" model and "student" model. |
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding (2024.lrec-main)
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| Challenge: | Existing methods to enhance document comprehension require fine-tuning for each task and dataset, and are expensive to train and operate. |
| Approach: | They propose a more flexible document analysis method that integrates visual-rich document understanding with large-scale language models (LLMs) by leveraging existing research in document image understanding and LLMs’ superior language understanding capabilities, the proposed model performs an understanding of document images in a single model. |
| Outcome: | The proposed model improves on the baseline model in document image understanding tasks. |
LCGbank: A Corpus of Syntactic Analyses Based on Proof Nets (2024.lrec-main)
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| Challenge: | Recent studies have focused on statistical syntactic parsing with proof nets . however, there has been a paucity of corpora in formalisms for which proof net is applicable . |
| Approach: | They propose a corpus of syntactic analyses based on Lambek categorial grammar . they leverage the relationship between LCG and CCG to address this problem . |
| Outcome: | The proposed method exploits the relationship between LCG and CCG to build an English-language corpus of syntactic analyses based on proof nets . the results suggest that the proposed method is weakly context-free equivalent and NP-complete . |
LeadEmpathy: An Expert Annotated German Dataset of Empathy in Written Leadership Communication (2024.lrec-main)
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| Challenge: | Empathetic leadership communication is associated with a wide range of positive individual and organizational outcomes. |
| Approach: | They propose an expert-annotated german dataset for modeling empathy in written leadership communication that uses a theory-based coding scheme to model cognitive and affective empathy in asynchronous communication. |
| Outcome: | The proposed model can be applied to produce high-quality, multidimensional empathy ratings in current and future applications. |
Learning Bidirectional Morphological Inflection like Humans (2024.lrec-main)
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| Challenge: | Recent research has focused on whether neural models can acquire morphological inflection like humans. |
| Approach: | They propose to use a recurrent neural network with attention and the transformer to train a symbolic model under a human-like learning environment to evaluate their models. |
| Outcome: | The proposed models did not accurately inflect verbs in the same manner as humans in terms of morphological inflection direction. |
Learning from Wrong Predictions in Low-Resource Neural Machine Translation (2024.lrec-main)
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| Challenge: | Existing approaches to Neural Machine Translation use additional linguistic sources and software tools but these are often not available in less favoured languages. |
| Approach: | They propose a pre-training strategy that leverages the relationships and similarities that exist between unaligned sentences to increase the dataset size of endangered and low-resource languages. |
| Outcome: | The proposed approach increases the dataset size of endangered and low-resource languages by the square of the initial quantity, matching the typical size of high-resourced datasets such as WMT14 En-Fr. |
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) however, in the context of Aspect-based Sentiment Analysis, only specific dimensions are pertinent. |
| Approach: | They propose a Gradient-based explanation framework that leverages an information bottleneck to refine word embeddings into a concise intrinsic dimension, maintaining essential features and omitting unrelated information. |
| Outcome: | The proposed framework improves both the models’ performance and explanations’ clarity by identifying sentiment-aware features. |
Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain (2024.lrec-main)
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| Challenge: | Argument mining is a complex process that requires a large amount of resources and time. |
| Approach: | They propose to analyze arguments in three different languages and domains to understand their robustness to natural language variations. |
| Outcome: | The proposed systems are more robust to natural language variations than existing arguments mining systems. |
Lemmatisation of Medieval Greek: Against the Limits of Transformer’s Capabilities? (2024.lrec-main)
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| Challenge: | Existing lemmatisation algorithms display an accuracy drop of around 30pp when tested on unedited, Byzantine Greek epigrams. |
| Approach: | They propose to use transformer-based embeddings and a dictionary look-up to lemmatise unedited, Byzantine Greek epigrams. |
| Outcome: | The proposed method outperforms existing methods and provides detailed error analysis revealing why unedited, Byzantine Greek is so challenging for lemmatisation. |
Leros: Learning Explicit Reasoning on Synthesized Data for Commonsense Question Answering (2024.lrec-main)
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Chenhao Wang, Pengfei Cao, Jiachun Li, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, Jun Zhao
| Challenge: | Recent work shows large language models can generate useful rationales for commonsense question answering (CQA) however, the cost of deployment and further tuning is relatively expensive for the large models. |
| Approach: | They propose a framework that leverages both knowledge graphs and large language models to synthesize rationale-augmented CQA data. |
| Outcome: | The proposed model can generate useful rationales on unseen CQA benchmarks. |
Lessons from Deploying the First Bilingual Peruvian Sign Language - Spanish Online Dictionary (2024.lrec-main)
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Joe Huamani-Malca, Miguel Rodriguez Mondoñedo, Francisco Cerna-Herrera, Gissella Bejarano, Carlos Vásquez Roque, Cesar Augusto Ramos Cantu, Sabina Oporto Pérez
| Challenge: | Bilingual dictionaries are key tools for learners when expanding their vocabulary or translating terms. |
| Approach: | They propose a bilingual Peruvian Sign Language (LSP) - Spanish Online Dictionary with two features that allow users to search for Spanish words in a database. |
| Outcome: | The proposed model is based on a database of videos whose glosses are related to the input text or Spanish word and a sign in front of the camera. |
Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)
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| Challenge: | Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
| Approach: | They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner. |
| Outcome: | Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
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| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
Leveraging Domain Corpora for Enhanced Terminology: The Case of Estonian-English Remote Sensing Termbase (2024.lrec-main)
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| Challenge: | Termbase is a domain corpora and terminological database for remote sensing in Estonia. |
| Approach: | They propose to develop an Estonian-English Remote Sensing Termbase from scratch . they use the Estonian Remote Sensenting Corpus 2022 as the primary data source . |
| Outcome: | The Estonian Remote Sensing Corpus 2022 served as the primary data source for the termbase. |
Leveraging Information Redundancy of Real-World Data through Distant Supervision (2024.lrec-main)
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| Challenge: | Existing methods for annotation of health care notes are promising but they are limited due to privacy regulations. |
| Approach: | They propose a text labeling method that leverages the redundancy of temporal information in a data lake to create a large programmatically annotated corpus and train transformer models using distant supervision. |
| Outcome: | The proposed method reduces expert annotation time, a scarce and expensive resource. |
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction (2024.lrec-main)
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| Challenge: | Open Information Extraction (OIE) is a structure prediction task in NLP that aims to extract structured n-ary tuples from free text. |
| Approach: | They propose to leverage linguistic features with a Seq2Seq PLM for OIE to improve performance. |
| Outcome: | The proposed methods give any neural OIE architecture the key performance boost from both PLMs and linguistic features in one go. |
Leveraging Pre-existing Resources for Data-Efficient Counter-Narrative Generation in Korean (2024.lrec-main)
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| Challenge: | Existing datasets and methods for detecting hate speech are limited by resource-intensive nature and only focus on the primary language. |
| Approach: | They propose a Korean Hate Speech Counter Punch (KHSCP) method that generates fact-based responses to hate speech in the Korean language and propose to use existing resources to overcome data scarcity. |
| Outcome: | The proposed method can overcome data scarcity in low-resource environments by leveraging existing resources. |
Leveraging Social Context for Humor Recognition and Sense of Humor Evaluation in Social Media with a New Chinese Humor Corpus - HumorWB (2024.lrec-main)
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| Challenge: | Existing humor computing research focuses on content while neglecting interaction relationships in social media. |
| Approach: | They propose a dataset which introduces social context information from social media . they propose 'humor recognition' task and 'horror evaluation task' |
| Outcome: | The proposed model incorporates social context information from social media . it shows that it is efficient and can be used to evaluate humor in real life . |
Leveraging Syntactic Dependencies in Disambiguation: The Case of African American English (2024.lrec-main)
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| Challenge: | African American English (AAE) is a low-resource language facing the challenge of inadequate annotated data for training natural language processing models. |
| Approach: | They propose a syntactically informed classifier for automatic disambiguation of AAE's habitual be. |
| Outcome: | The proposed classifier improves automatic disambiguation of habitual and non-habitual meanings of "be" integrating syntactic information improves disambiguations of habituality by 65 F1 points over baseline models and as much as 74 points. |
Leveraging the Interplay between Syntactic and Acoustic Cues for Optimizing Korean TTS Pause Formation (2024.lrec-main)
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| Challenge: | despite recent advances in speech synthesis, the focus of research has been on high-resource languages like English. |
| Approach: | They propose a framework that incorporates modeling of syntactic and acoustic cues associated with pausing patterns. |
| Outcome: | The proposed framework generates natural speech even for longer and intricate out-of-domain sentences, despite training on short audio clips. |
LexAbSumm: Aspect-based Summarization of Legal Decisions (2024.lrec-main)
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| Challenge: | LexAbSumm is a dataset designed for aspect-based summarization of legal documents . it is based on a set of ECtHR fact sheets, and is available for download. |
| Approach: | They propose a dataset designed for aspect-based summarization of legal case decisions . they evaluate abstractive summarizing models tailored for longer documents . |
| Outcome: | The proposed dataset is designed for aspect-based summarization of legal cases . it reveals a challenge in conditioning models to produce aspect-specific summaries . |
LexComSpaL2: A Lexical Complexity Corpus for Spanish as a Foreign Language (2024.lrec-main)
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| Challenge: | 58,240 annotations are available for learners of Spanish as a foreign/second language (L2). |
| Approach: | They propose a corpus which can be employed to train personalised word-level difficulty classifiers for learners of Spanish as a foreign/second language (L2). |
| Outcome: | The proposed model can train personalised word-level difficulty classifiers for learners of Spanish as a foreign/second language (L2) using a customised version of the 5-point lexical complexity prediction scale. |
LexDrafter: Terminology Drafting for Legislative Documents Using Retrieval Augmented Generation (2024.lrec-main)
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| Challenge: | With the increase in legislative documents, the number of new terms and their definitions is increasing as well. |
| Approach: | They propose a framework that helps in drafting Definitions articles for legislative documents using retrieval augmented generation and existing term definitions present in different legislative documents. |
| Outcome: | The proposed framework can be used to draft Definitions articles for legislative documents using retrieval augmented generation and existing term definitions present in different legislative documents. |
LexiVault: A Repository for Psycholinguistic Lexicons of Lesser-studied Languages (2024.lrec-main)
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| Challenge: | LexiVault is an open-source web tool with annotated lexicons and rich retrieval capabilities primarily developed for, but not restricted to, the support of psycholinguistic research . |
| Approach: | They propose to use LexiVault to design stimuli for low-resource languages with annotated lexicons and rich retrieval capabilities. |
| Outcome: | The LexiVault tool is designed to be user friendly and accommodate incremental growth of new and existing low-resource language lexicons in the system while abstracting programming complexity to foster more interest from the psycholinguistics community in exploring low-rsource languages. |
LFED: A Literary Fiction Evaluation Dataset for Large Language Models (2024.lrec-main)
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| Challenge: | LFED is a literary fiction evaluation dataset for large language models that evaluate the capability of LLMs on the long fiction comprehension and reasoning. |
| Approach: | They propose a Literary Fiction Evaluation Dataset to evaluate LLMs' comprehension and reasoning on long fictions. |
| Outcome: | The proposed dataset evaluates the capability of large language models on the long fiction comprehension and reasoning. |
LHMKE: A Large-scale Holistic Multi-subject Knowledge Evaluation Benchmark for Chinese Large Language Models (2024.lrec-main)
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| Challenge: | Existing benchmarks for comprehensively evaluating Chinese Large Language Models are insufficient. |
| Approach: | They propose a Large-scale, Holistic, and Multi-subject Knowledge Evaluation benchmark to evaluate Chinese Large Language Models. |
| Outcome: | The proposed benchmark measures the knowledge acquisition capabilities of Chinese Large Language Models across 75 subjects from primary school to professional certification exams. |
LI4: Label-Infused Iterative Information Interacting Based Fact Verification in Question-answering Dialogue (2024.lrec-main)
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| Challenge: | Existing studies on fact verification have failed to fully exploit question structures and ignoring relevant label information during the verification process. |
| Approach: | They propose a new approach for question-answering dialogue based fact verification using label-infused iterative information interacting. |
| Outcome: | The proposed approach achieves remarkable performance on HEALTHVER, FAVIQ, and COLLOQUIAL. |
LightVLP: A Lightweight Vision-Language Pre-training via Gated Interactive Masked AutoEncoders (2024.lrec-main)
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| Challenge: | Existing vision-language pre-training models use multi-modal encoders to encode image and text, causing noisy training corpora. |
| Approach: | They propose a vision-language pre-training framework with two autoencoders for efficient training . they propose masked tokens and a gated interaction mechanism to cope with noise . |
| Outcome: | The proposed model achieves 2.2% R@1 gains on COCO Text Retrieval and 1.1% on refCOCO+ on six datasets. |
Limitations of Human Identification of Automatically Generated Text (2024.lrec-main)
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Nadège Alavoine, Maximin Coavoux, Emmanuelle Esperança-Rodier, Romane Gallienne, Carlos Gonzalez Gallardo, Jérôme Goulian, Jose G. Moreno, Aurélie Névéol, Didier Schwab, Vincent Segonne, Johanna Simoens
| Challenge: | Neural text generation tools such as ChatGPT are gaining popularity . human annotations are considered gold standard labels for multiple tasks . |
| Approach: | They propose a new corpus in French and English for recognising automatically generated texts . they propose 'incontext' setup which makes explicit the interaction between two parties . |
| Outcome: | The proposed model generates fluent text, which requires much closer reading than the current model. |
Linear Cross-document Event Coreference Resolution with X-AMR (2024.lrec-main)
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Shafiuddin Rehan Ahmed, George Arthur Baker, Evi Judge, Michael Reagan, Kristin Wright-Bettner, Martha Palmer, James H. Martin
| Challenge: | Event Coreference Resolution (ECR) is expensive both for automated systems and manual annotations. |
| Approach: | They propose a graphical representation of events anchored around individual mentions using a cross-document version of Abstract Meaning Representation. |
| Outcome: | The proposed model is anchored around individual mentions using a cross-document version of Abstract Meaning Representation. |
LinguaMeta: Unified Metadata for Thousands of Languages (2024.lrec-main)
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| Challenge: | LinguaMeta is a unified repository of language metadata for thousands of languages. |
| Approach: | They introduce LinguaMeta, a unified resource for language metadata for thousands of languages. |
| Outcome: | The proposed resource is intended for use by researchers and organizations who aim to extend technology to thousands of languages. |
Linguistic Knowledge Can Enhance Encoder-Decoder Models (If You Let It) (2024.lrec-main)
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| Challenge: | a recent study has shown that pre-trained NLMs can capture syntax- and semantic-sensitive phenomena. |
| Approach: | They investigate whether fine-tuning pre-trained models with linguistic knowledge improves their performance in a target task. |
| Outcome: | The proposed enhancements improve models' performance in a target task, the authors show . the study includes models in Italian and English, and multilingual models in English and Italian . |
Linguistic Nudges and Verbal Interaction with Robots, Smart-Speakers, and Humans (2024.lrec-main)
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| Challenge: | nudges are a choice architecture that alters people's behavior without forbidding any options or significantly changing their economic incentives. |
| Approach: | They describe a data collection methodology and emotion annotation of dyadic interactions between a human, a Pepper robot, . a Google Home smart-speaker, and other humans. |
| Outcome: | The collected 16-hour audio recordings show that humans change their opinions on more questions with a human than with nudges, even against mainstream ideas. |
Linguistic Rule Induction Improves Adversarial and OOD Robustness in Large Language Models (2024.lrec-main)
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| Challenge: | Existing large language models (LLMs) do not perform satisfactorily in OOD and adversarial robustness evaluations. |
| Approach: | They propose to use linguistic rule induction to fine-tune large language models with linguistic rules to achieve better adversarial and OOD robustness. |
| Outcome: | The proposed model achieves comparable or better results with GPT-3.5 and GPT-4 on various adversarial and OOD robustness evaluations. |
Linguistic Survey of India and Polyglotta Africana: Two Retrostandardized Digital Editions of Large Historical Collections of Multilingual Wordlists (2024.lrec-main)
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| Challenge: | Linguistic Survey of India and Polyglotta Africana are two of the largest historical collections of multilingual wordlists. |
| Approach: | They present a retro-standardized edition of the Linguistic Survey of India and the Polyglotta Africana, which are two of the largest historical collections of multilingual wordlists. |
| Outcome: | The LSI and PA are the largest historical collections of multilingual wordlists . but no editions in which the original data is presented in standardized form have been produced so far . |
Linking Adaptive Structure Induction and Neuron Filtering: A Spectral Perspective for Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | incorporating structure information can improve the performance of aspect-based sentiment analysis. |
| Approach: | They propose a method to conduct neuron-level manipulations on word representations in the frequency domain. |
| Outcome: | The proposed method can achieve or come close to state-of-the-art in ABSA. |
Linking Judgement Text to Court Hearing Videos: UK Supreme Court as a Case Study (2024.lrec-main)
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| Challenge: | Typically, transcripts of legal hearings are lengthy, making it time-consuming for legal professionals to analyse crucial arguments. |
| Approach: | They propose to use judgement-hearing pairs to link sections of written judgements with relevant moments in Supreme Court hearing videos to improve access to justice. |
| Outcome: | The proposed tool connects sections of written judgements with relevant moments in Supreme Court hearing videos, streamlining access to critical information. |
Linking Named Entities in Diderot’s Encyclopédie to Wikidata (2024.lrec-main)
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| Challenge: | Encyclopédie was published between 1751 and 1772 and aimed to collect the knowledge of its time. |
| Approach: | They describe the annotation of more than 9,100 Encyclopédie entries with Wikidata identifiers . they extract all geographic entries and annotate 8,300 entries having a geographic content only. |
| Outcome: | The annotation process and application examples are presented in this paper. |
Little Red Riding Hood Goes around the Globe: Crosslingual Story Planning and Generation with Large Language Models (2024.lrec-main)
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| Challenge: | Existing work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English. |
| Approach: | They propose a task of crosslingual story generation with planning to leverage the creative and reasoning capabilities of large pretrained language models to generate stories in multiple languages. |
| Outcome: | The proposed task combines planning and planning in a monolingual setting and demonstrates that plans which structure stories into three acts lead to more coherent and interesting narratives while allowing to explicitly control their content and structure. |
LlamaCare: An Instruction Fine-Tuned Large Language Model for Clinical NLP (2024.lrec-main)
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| Challenge: | Large language models have shown remarkable abilities in generating natural texts . applying LLMs to clinical domain still poses significant challenges . |
| Approach: | They propose a method of instruction fine-tuning for adapting large language models to clinical domains . they generate instructions, inputs, and outputs covering a wide spectrum of clinical services . |
| Outcome: | The proposed method outperforms baseline LLMs on clinical tasks . it requires domain adaptation, task-specific learning, and reliability . |
Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness (2024.lrec-main)
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| Challenge: | Recent advances in Natural Language Processing (NLP) have seen Large-scale Language Models excel at producing high-quality text for various purposes. |
| Approach: | They propose a language model that enriches semantic content of text using Llama2 . their method enhances emotive expressiveness on a dataset . |
| Outcome: | The proposed model matches the naturalness of the original VITS and incorporates BERT (BERT-VITS) on the LJSpeech dataset, highlighting its potential to generate emotive speech. |
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward (2024.lrec-main)
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| Challenge: | Large language models have demonstrated remarkable performance in various NLP tasks, but are typically computationally expensive and difficult to be deployed in resource-constrained environments. |
| Approach: | They propose a knowledge distillation method based on a reward function induced from large language models. |
| Outcome: | The proposed method outperforms traditional methods on multiple datasets and tasks. |
LLMSegm: Surface-level Morphological Segmentation Using Large Language Model (2024.lrec-main)
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| Challenge: | Existing approaches to morphological segmentation split word into its morphemes . LLMSegm is applicable in low-data settings and low-resourced languages . |
| Approach: | They propose a novel approach to surface-level morphological segmentation leveraging large language models. |
| Outcome: | The proposed method is applicable in low-data settings and low-resource languages. |
LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Recent work using model ensemble methods based on voting can effectively mitigate over-correction and improve the precision of the GEC system. |
| Approach: | They propose a rewriting model that can directly modify the over-correction of GEC system outputs without a model ensemble. |
| Outcome: | The proposed model can mitigate over-correction and improve accuracy of Chinese grammatical error correction tasks without a model ensemble. |
Locally Differentially Private In-Context Learning (2024.lrec-main)
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| Challenge: | Large pretrained language models (LLMs) have shown surprising In-Context Learning ability. |
| Approach: | They propose a locally differentially private framework of in-context learning for LLMs that can be augmented with a private database for some specific task. |
| Outcome: | The proposed framework can predict labels without additional parameter modifications without input-label pairs . |
LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data (2024.lrec-main)
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| Challenge: | Prior research on Twitter has provided positive evidence of its utility in developing supplementary health surveillance systems. |
| Approach: | They propose a framework to surveil public health, focusing on mental health outcomes by using tweets from 765 neighborhoods in the USA. |
| Outcome: | The proposed framework achieves the highest F1-score and accuracy over the previous framework, and extrapolates CDC’s estimates to proxy unreported neighborhoods. |
Loflòc: A Morphological Lexicon for Occitan using Universal Dependencies (2024.lrec-main)
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| Challenge: | Loflc is the first publicly available lexicon for Occitan. |
| Approach: | They propose to use an open inflected lexicon for Occitan to provide a morphological resource for low-resource languages. |
| Outcome: | The proposed lexicon covers Occitan in four major dialects and is a key resource for low-resource languages. |
Logging Keystrokes in Writing by English Learners (2024.lrec-main)
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Georgios Velentzas, Andrew Caines, Rita Borgo, Erin Pacquetet, Clive Hamilton, Taylor Arnold, Diane Nicholls, Paula Buttery, Thomas Gaillat, Nicolas Ballier, Helen Yannakoudakis
| Challenge: | Essay writing is a skill commonly taught and practised in schools. |
| Approach: | They collect and analyse data representing the essay writing process from start to finish by recording every keystroke from multiple writers participating in the study. |
| Outcome: | The data collected from 1,006 writers is compared against a standard dataset of texts, keystroke logs and metadata for public release. |
Logic Rules as Explanations for Legal Case Retrieval (2024.lrec-main)
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| Challenge: | Recent efforts to learn explainable legal case retrieval models fail to provide faithful and interpretable explanations for legal cases. |
| Approach: | They propose a framework that uses logic rules to explain legal case retrieval results . they extend benchmarks of LeCaRD and ELAM with manually annotated logic rules . |
| Outcome: | The proposed framework is able to provide faithful explanations for legal case retrieval. |
LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) reach hundreds of billions of parameters and require resources for training and inference stages. |
| Approach: | They propose a low-rank adapter to reduce the number of trainable parameters in a model and reduce memory requirements. |
| Outcome: | The proposed approach reduces memory and compute requirements while preserving performance. |
LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation (2024.lrec-main)
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| Challenge: | Existing metrics for text summarisation have restrictive token limits, limiting their effectiveness. |
| Approach: | They propose a human-annotated data set for evaluating automatic factuality metrics . they propose 'longDocFACTScore' framework which can be extended to any length document . |
| Outcome: | The proposed framework outperforms state-of-the-art metrics in evaluating long document summarisation data sets. |
Longform Multimodal Lay Summarization of Scientific Papers: Towards Automatically Generating Science Blogs from Research Articles (2024.lrec-main)
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| Challenge: | Science blogs and lay-speak are critical to communicating scientific information to the general public and policymakers. |
| Approach: | They propose to use presentation transcripts and slides to generate a scientific blog from a research article in layperson's terms. |
| Outcome: | The proposed approach can generate a blog text and select the most relevant figures to explain a research article in layperson’s terms, essentially a science blog. |
Look before You Leap: Dual Logical Verification for Knowledge-based Visual Question Generation (2024.lrec-main)
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| Challenge: | Existing methods for visual question generation focus on leveraging the semantics of inputs to propose questions, ignoring the logical coherence between generated questions and images. |
| Approach: | They propose a logical verification method that checks logical structure between Q, images, answers and acquired outside knowledge by incorporating logical coherence between Q and Q twice in the whole procedure. |
| Outcome: | The proposed method can generate diverse and insightful knowledge-based visual questions on two common datasets. |
LoSST-AD: A Longitudinal Corpus for Tracking Alzheimer’s Disease Related Changes in Spontaneous Speech (2024.lrec-main)
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| Challenge: | Language-based biomarkers have shown promising results in differentiating those with Alzheimer’s disease (AD) diagnosis from healthy individuals, but the earliest changes in language are thought to start years or even decades before the diagnosis. |
| Approach: | They propose to use transcripts of public interviews with 20 famous figures to track language change over several decades to validate their corpus. |
| Outcome: | The proposed corpus can provide a valuable starting point for the development of early detection tools and enhance our understanding of how AD affects language over time. |
Low-Rank Prune-And-Factorize for Language Model Compression (2024.lrec-main)
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| Challenge: | Existing methods to reduce parameter redundancy in pre-processed language models fail to retain satisfactory performance under moderate to high compression rates. |
| Approach: | They propose to use network pruning to extract low-rank sparsity pattern desirable to matrix factorization. |
| Outcome: | The proposed method has a superior compression-performance trade-off compared to existing methods. |
M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets (2024.lrec-main)
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| Challenge: | Existing studies on sentiment analysis of tweets focus on the English language . however, there is still a challenge of processing lower-resourced languages . |
| Approach: | They transform tweet sentiment dataset into a multimodal format through a straightforward curation process. |
| Outcome: | The proposed approach performs exceptionally well in unimodal and multimodal configurations. |
M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval (2024.lrec-main)
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| Challenge: | Recent research shows that contrastive learning can lead to suboptimal retrieval performance. |
| Approach: | They propose an advanced recursive Multi-hop dense sentence retrieval system built upon a novel Multi-task Mixed-objective approach for dense text representation learning. |
| Outcome: | The proposed approach yields state-of-the-art performance on a large-scale open-domain fact verification benchmark dataset, FEVER. |
m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt (2024.lrec-main)
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Jian Yang, Hongcheng Guo, Yuwei Yin, Jiaqi Bai, Bing Wang, Jiaheng Liu, Xinnian Liang, LinZheng Chai, Liqun Yang, Zhoujun Li
| Challenge: | Existing multimodal neural machine translation models focus on bilingual translation, but experimental results show that they outperform the text-only baselines and multilingual multimodal methods by a large margin. |
| Approach: | They propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual Neural Machine Translation (m3P) this framework aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation. |
| Outcome: | The proposed framework outperforms previous text-only baselines and multilingual multimodal methods by a large margin. |
M3TCM: Multi-modal Multi-task Context Model for Utterance Classification in Motivational Interviews (2024.lrec-main)
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| Challenge: | Motivational interviews have two distinct roles, namely client and therapist . previous approaches did not fully incorporate all of these characteristics into utterance classification . |
| Approach: | They propose a multi-modal, multi-task context model for utterance classification that integrates text and speech as well as conversation context. |
| Outcome: | The proposed model outperforms the state-of-the-art in utterance classification on the AnnoMI dataset with a relative improvement of 20% for the client- and by 15% for therapist utterrance classification. |
MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis (2024.lrec-main)
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| Challenge: | Sociolinguists and psychologists have been studying these variations in the lexicons and the language from the 50's . code-mixing is a popular method for understanding people's emotions and attitudes towards various subjects, but low-resourced languages often have a mix of scripts and languages. |
| Approach: | They introduce a new sentiment data, MaCMS, for Magahi-Hindi-English code-mixed language, where Magai is a less-resourced minority language. |
| Outcome: | The proposed dataset is the first Magahi-Hindi-English code-mixed dataset for sentiment analysis tasks. |
MAGIC: Multi-Argument Generation with Self-Refinement for Domain Generalization in Automatic Fact-Checking (2024.lrec-main)
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| Challenge: | Existing methods for fact-checking are limited in retrieving evidence from documents . retrieved evidence derived from different sources strains generalization capabilities of classifiers . |
| Approach: | They propose a framework for cross-domain fact-checking using multi-argument generation . they propose to reconstruct concise evidence from large amounts of evidence retrieved from different sources . |
| Outcome: | The proposed framework is effective in identifying the veracity of out-of-domain claims . it can be used to extract evidence from documents and verify claims across domains . |
MAGPIE: Multi-Task Analysis of Media-Bias Generalization with Pre-Trained Identification of Expressions (2024.lrec-main)
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Tomáš Horych, Martin Paul Wessel, Jan Philip Wahle, Terry Ruas, Jerome Waßmuth, André Greiner-Petter, Akiko Aizawa, Bela Gipp, Timo Spinde
| Challenge: | Existing approaches to media bias detection lack generalizability, resulting in limited generalizarability. |
| Approach: | They propose a large-scale multi-task pre-training approach specifically tailored for media bias detection that can be used to train 59 bias-related tasks. |
| Outcome: | The proposed approach outperforms existing methods on the BABE dataset with a relative improvement of 3.3% F1-score. |
MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank (2024.lrec-main)
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| Challenge: | Despite the success of the Universal Dependencies (UD) project, there is still a lack of diversity within high-resource languages and their closely related non-standard languages and dialects. |
| Approach: | They propose to annotate Bavarian with part-of-speech and syntactic dependency information manually in UD and to highlight morphosyntactical differences between the closely related languages. |
| Outcome: | The proposed treebank covers multiple genres including wiki, fiction, grammar examples, social, non-fiction and Bavarian. |
MaintIE: A Fine-Grained Annotation Schema and Benchmark for Information Extraction from Maintenance Short Texts (2024.lrec-main)
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| Challenge: | Maintenance short texts (MSTs) provide crucial insights into the state and maintenance activities of machines, infrastructure, and other engineered assets. |
| Approach: | They propose a multi-level fine-grained annotation scheme for entity recognition and relation extraction that includes 5 top-level classes and 6 relations tailored to MSTs. |
| Outcome: | The proposed scheme provides high-quality, fine-grained annotations and a coarse-grain corpus of 7,000 texts. |
Majority Rules Guided Aspect-Category Based Sentiment Analysis via Label Prior Knowledge (2024.lrec-main)
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| Challenge: | Aspect-Category based sentiment analysis is a fine-grained task to identify the sentiment polarities of pre-defined categories in text. |
| Approach: | They propose a MAjority Rules Guided for understanding the semantic difference between text and people. |
| Outcome: | The proposed model outperforms the state-of-the-art models on four benchmark datasets by 2.43% to 67.68% in terms of F1-score and by 1.16% to 10.22% in terms accuracy. |
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have shown increasing power on NLP tasks. however, tuning these models for downstream tasks usually requires exorbitant costs. |
| Approach: | They propose a black-box tuning technique that optimizes task-specific prompts without accessing gradients and hidden representations. |
| Outcome: | The proposed method improves performance under few-shot learning scenarios. |
Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors (2024.lrec-main)
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| Challenge: | Existing methods to learn new relations with limited labeled data are prone to catastrophic forgetting and overfitting. |
| Approach: | They propose a framework that uses prompts to acquire more generalized knowledge . they propose CFRE to continuously learn new relations while retaining knowledge of old ones . |
| Outcome: | The proposed method outperforms state-of-the-art methods by a large margin and significantly mitigates catastrophic forgetting and overfitting in low-resource scenarios. |
Making Sentence Embeddings Robust to User-Generated Content (2024.lrec-main)
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| Challenge: | NLP models struggle on user-generated content (UGC) due to high lexical variance and deviating from the standard texts. |
| Approach: | They propose a sentence embedding model that embeds non-standard sentences and their standard counterparts close to each other in the embeddable space. |
| Outcome: | The proposed model outperforms LASER on key typos and social media abbreviations while outperforming LASER in other tasks. |
Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction (2024.lrec-main)
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| Challenge: | Standard English and Malaysian English exhibit significant differences in morphosyntactic variations . existing datasets are not sufficient to enhance NLP tasks in Malaysian english . |
| Approach: | They propose to use a Malaysian English news article dataset to refine NER models for Malaysian english. |
| Outcome: | The proposed dataset can improve the performance of NER on Malaysian English. |
mALBERT: Is a Compact Multilingual BERT Model Still Worth It? (2024.lrec-main)
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| Challenge: | Existing studies on the ethical and ecological impact of pre-trained language models raise questions about the temporal, financial, and environmental aspects of such models. |
| Approach: | They propose to focus on smaller models, such as compact models like ALBERT, which are more ecologically virtuous than these PLMs. |
| Outcome: | The proposed model is compared with classical multilingual models and is ethically virtuous. |
ManNER & ManPOS: Pioneering NLP for Endangered Manchu Language (2024.lrec-main)
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| Challenge: | a new study examines the impact of natural language processing (NLP) on the endangered Manchu language. |
| Approach: | They propose to use BiLSTM-CRF, BERT, and mBERT to train transformer-based models on Manchu for NER and POS tagging tasks. |
| Outcome: | The proposed models achieved over 90% F1 score in both NER and POS tasks. |
Mapping the Past: Geographically Linking an Early 20th Century Swedish Encyclopedia with Wikidata (2024.lrec-main)
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| Challenge: | The Nordisk Familjebok encyclopedia is one of the most extensive encyclopepedias in the world. |
| Approach: | They propose to extract location entries from the Nordisk Familjebok encyclopedia from the early 20th century, focusing on the second edition called Uggleupplagan. |
| Outcome: | The extracted locations were found to be located within Sweden, Germany, and the United Kingdom. |
Mapping Work Task Descriptions from German Job Ads on the O*NET Work Activities Ontology (2024.lrec-main)
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| Challenge: | a new method for mapping job tasks to labor market ontologies is proposed . a top configuration of the method achieved a notable performance improvement . |
| Approach: | They use ontological data with Multiple Negatives Ranking loss to extract job tasks from job postings . they integrate labeled job advertisement data into training to improve their mapping . |
| Outcome: | The proposed method improves on the German job ads and their ontology . it can be used to map job tasks to established labor market ontologies or taxonomies . |
MARASTA: A Multi-dialectal Arabic Cross-domain Stance Corpus (2024.lrec-main)
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Anis Charfi, Mabrouka Ben-Sghaier, Andria Samy Raouf Atalla, Raghda Akasheh, Sara Al-Emadi, Wajdi Zaghouani
| Challenge: | Approximately half of the sentences are in Modern Standard Arabic (MSA) for each region, and the other half is in the region’s respective dialect. |
| Approach: | They propose a cross-domain and multi-dialectal stance corpus for Arabic that includes four regions in the Arab World and covers the main Arabic dialect groups. |
| Outcome: | The proposed corpus outperforms the state-of-the-art dataset in stance detection and dialect and dialect classes. |
Massively Multilingual Token-Based Typology Using the Parallel Bible Corpus (2024.lrec-main)
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| Challenge: | linguistic typology data from the parallel Bible corpus is limited and not available for annotated corpora and automatic parsing tools. |
| Approach: | They analyze word order statistics extracted from the Bible corpus from two angles: stability across different translations in the same language and comparability with Universal Dependencies corpora and typological database classifications from URIEL and Grambank. |
| Outcome: | The results show that word order statistics extracted from the Bible corpus are reliable and generalisable across different translations in the same language. |
Mathematical Entities: Corpora and Benchmarks (2024.lrec-main)
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| Challenge: | a limited amount of annotated data is available for mathematical language processing . mathematics is a highly specialized domain with its own unique set of challenges . |
| Approach: | They provide annotated corpora that can be used to study the language of mathematics . they provide part-of-speech tags, lemmas, and dependency trees . |
| Outcome: | The proposed corpora provide part-of-speech tags, lemmas, and dependency trees . the learning assistant grants access to the content of the corporata in a context-sensitive manner . |
MccSTN: Multi-Scale Contrast and Fine-Grained Feature Fusion Networks for Subject-driven Style Transfer (2024.lrec-main)
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| Challenge: | Stylistic style transfer is an important part of the image processing field . due to the low semantic similarity between the original image and the style image, many fine-grained style features are discarded. |
| Approach: | They propose a new style representation and transfer framework that can be adapted to existing image style transfers. |
| Outcome: | The proposed framework can be adapted to existing image style transfers. |
MCIL: Multimodal Counterfactual Instance Learning for Low-resource Entity-based Multimodal Information Extraction (2024.lrec-main)
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| Challenge: | Existing methods to perform multimodal information extraction only investigated entity-based tasks under supervised learning with adequate labeled data. |
| Approach: | They propose to investigate the entity-based MIE tasks under the low-resource settings by decomposing the features into image, entity, and context factors. |
| Outcome: | The proposed method is able to perform on two public MIE benchmark datasets and the experimental results confirm it. |
MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)
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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang
| Challenge: | Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain. |
| Approach: | They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process. |
| Outcome: | The proposed dataset evaluates the performance of unsupervised methods and advanced large language models. |
MDS: A Fine-Grained Dataset for Multi-Modal Dialogue Summarization (2024.lrec-main)
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| Challenge: | Summarizing the dialogue into a short message has drawn much attention due to the explosion of various dialogue scenes. |
| Approach: | They develop a multi-modal dialogue summarization dataset to enhance the variety of data available for this research area. |
| Outcome: | The proposed dataset provides a demanding testbed for multi-modal dialogue summarization. |
Measuring Cross-Text Cohesion for Segmentation Similarity Scoring (2024.lrec-main)
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| Challenge: | a new segmentation similarity metric is being developed for text segmentation . current metrics are content-agnostic and do not provide fine-grained scoring . |
| Approach: | They propose a word-embedding-based metric of cross-textual cohesion based on the formal linguistic definition of cohetion and incorporate it into a new segmentation similarity metric, SED. |
| Outcome: | The proposed metric provides fine-grained segmentation similarity scoring for 3 basic errors, avoiding limitations of traditional metrics. |
Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge (2024.lrec-main)
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| Challenge: | Existing methods for medical entity disambiguation (MED) fail to fully utilize the knowledge within medical knowledge bases (KBs) Existing models overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguations performance. |
| Approach: | They propose to combine a mention relation fusion module and an entity knowledge fusion modules to map medical mentions to corresponding entities in a knowledge base (KB) |
| Outcome: | The proposed method outperforms state-of-the-art MED models on two publicly available real-world datasets. |
Medical Vision-Language Pre-Training for Brain Abnormalities (2024.lrec-main)
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| Challenge: | Existing vision-language models lack expertise for medical applications due to the scarcity and complexity of data. |
| Approach: | They propose a pipeline to collect medical image-text aligned data for pretraining from public resources such as PubMed and build a high-performance vision-language model tailored to specific medical tasks. |
| Outcome: | The proposed model is based on a large brain image-text dataset and will be released to the public. |
MedMT5: An Open-Source Multilingual Text-to-Text LLM for the Medical Domain (2024.lrec-main)
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Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata, Andrea Zaninello
| Challenge: | Existing studies on large language models for medical applications have focused on a single language . medical mT5 outperforms both encoders and similar sized text-to-text models in English, French, and Italian benchmarks . |
| Approach: | They propose to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. |
| Outcome: | The proposed model outperforms encoders and similar sized models on the Spanish, French, and Italian benchmarks while being competitive with current state-of-the-art models in English. |
MedQA-SWE - a Clinical Question & Answer Dataset for Swedish (2024.lrec-main)
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| Challenge: | MedQA-SWE is a clinical question & answering dataset in Swedish . it was created from exams aimed at evaluating doctors’ clinical understanding and decision making . |
| Approach: | They propose to create a multiple choice, clinical question & answering (Q&A) dataset in Swedish consisting of 3,180 questions. |
| Outcome: | The proposed dataset includes 3,180 questions and is the first open-source clinical Q&A dataset in Swedish. |
MemoryPrompt: A Light Wrapper to Improve Context Tracking in Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Transformer-based language models (LMs) track contextual information through large, hard-coded input windows. |
| Approach: | They propose a leaner approach where a pre-trained LM is augmented with a small auxiliary recurrent network that passes information to the LM by prefixing its regular input with . vectors. |
| Outcome: | The proposed method outperforms larger LMs with full input history on a long-distance dialogue dataset and does not suffer catastrophic forgetting when adapted to new tasks. |
MentalHelp: A Multi-Task Dataset for Mental Health in Social Media (2024.lrec-main)
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Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri
| Challenge: | Annotating social media data for mental health disorders is expensive and time-consuming, limiting their size and scope. |
| Approach: | They present a large-scale semi-supervised mental disorder detection dataset containing 14 million instances from Reddit and an ensemble of three separate models. |
| Outcome: | The proposed dataset contains 14 million instances of mental disorders . it was collected from reddit and labeled in a semi-supervised way . |
MentalRiskES: A New Corpus for Early Detection of Mental Disorders in Spanish (2024.lrec-main)
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Alba M. Mármol Romero, Adrián Moreno Muñoz, Flor Miriam Plaza-del-Arco, M. Dolores Molina González, María Teresa Martín Valdivia, L. Alfonso Ureña-López, Arturo Montejo Ráez
| Challenge: | Existing studies on the prevalence of mental disorders on the Web are limited to the English language. |
| Approach: | They propose to use user messages posted on Telegram groups to annotate the corpus for natural language processing and to conduct experiments on text classification and regression. |
| Outcome: | The proposed corpus contains over 1,300 subjects with more than 45,000 messages posted in different public Telegram groups. |
Meta-Adapter for Self-Supervised Speech Models: A Solution to Low-Resource Speech Recognition Challenges (2024.lrec-main)
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| Challenge: | Existing self-supervised learning models can learn latent representations from large amounts of unlabeled data, but they are expensive to fine-tune. |
| Approach: | They develop a meta-adapter to obtain meta-initialized parameters for self-supervised models . meta-Adapters show better generalization and extensibility than traditional pretraining methods . |
| Outcome: | Experiments on common voice and FLEURS datasets show Meta-Adapter performs better on low-resource languages . authors show it can be used on 12 low-source languages, but it requires huge computational resources . |
Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models have push NLP into a new era, moving away from traditional task-specific pre-train finetuning paradigm. |
| Approach: | They provide a comprehensive analysis of declarative and procedural knowledge for large language models and evaluate their effectiveness. |
| Outcome: | The proposed model can perform better with both kinds of knowledge, but at different speeds. |
Meta-Evaluation of Sentence Simplification Metrics (2024.lrec-main)
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| Challenge: | Automatic Text Simplification (ATS) is a major natural language processing task that aims to help people understand complex text. |
| Approach: | They propose to use a human-annotated dataset to study automatic text simplification models to determine which metrics to use when evaluating new models. |
| Outcome: | The proposed models reconstruct the text into a simpler format by deletion, substitution, addition or splitting, while preserving the original meaning and correct grammar. |
Metaphors in Online Religious Communication: A Detailed Dataset and Cross-Genre Metaphor Detection (2024.lrec-main)
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| Challenge: | figurative language plays a particularly important role in religious communication . linguistic metaphors relate entities from different semantic domains by drawing on an implicit similarity between them. |
| Approach: | They present a dataset of fine-grained metaphor annotations for online religious communication . they show that cross-genre transfer metaphor detection leads to a drop in performance . |
| Outcome: | The proposed dataset shows that adding in-genre data improves performance . the authors show that the proposed system can detect metaphors in religious forums . |
MEVTR: A Multilingual Model Enhanced with Visual Text Representations (2024.lrec-main)
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| Challenge: | Existing models that generate multilingual text representations perform poorly on low-resource languages due to lack of representation space and model capacity. |
| Approach: | They propose a multilingual model enhanced with visual text representations which complements textual representations and extends multilingual representation space with visual representations. |
| Outcome: | The proposed model outperforms state-of-the-art models on zero-shot cross-lingual transfer tasks without the target language adapter. |
mForms : Multimodal Form Filling with Question Answering (2024.lrec-main)
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| Challenge: | The paper presents a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA). |
| Approach: | They propose a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA) the paper introduces a multimodal form-filled dataset and an extension of the popular ATIS dataset to support future research and experimentation. |
| Outcome: | The proposed approach maintains robust accuracy for sparse training conditions and achieves state-of-the-art F1 of 0.97 on ATIS with approximately 1/10th the training data. |
MHGRL: An Effective Representation Learning Model for Electronic Health Records (2024.lrec-main)
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| Challenge: | Effective EHR representations are key to achieving high performance in healthcare applications. |
| Approach: | They propose a multimodal heterogeneous graph-enhanced representation learning to learn EHR representations using medical ontology and textual notes. |
| Outcome: | The proposed model outperforms baseline models on two real clinical datasets in downstream tasks. |
MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection (2024.lrec-main)
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| Challenge: | a new dataset of misinformation labels is being developed to detect misinformation on social media platforms . misinformation is spread in many domains including but not limited to health, politics, and disasters . |
| Approach: | They construct a dataset of 5,284 English and 5,064 Turkish tweets with misinformation labels . they use the dataset to analyze misinformation spread and to evaluate misinformation detection . |
| Outcome: | The proposed dataset includes 5,284 English and 5,064 Turkish tweets with misinformation labels for several recent events between 2020 and 2022. |
Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents (2024.lrec-main)
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| Challenge: | Rhetorical Role Labeling (RRL) of legal judgments presents challenges such as inferring sentence roles from context, interrelated roles, limited annotated data, and label imbalance. |
| Approach: | They propose techniques to enhance RRL performance by leveraging knowledge from semantically similar instances. |
| Outcome: | The proposed methods achieve remarkable improvements in challenging macro-F1 scores. |
MinT: Boosting Generalization in Mathematical Reasoning via Multi-view Fine-tuning (2024.lrec-main)
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| Challenge: | Existing methods focus on specializing LMs in mathematical reasoning and rely on knowledge distillation. |
| Approach: | They propose a multi-view fine-tuning method that exploits existing mathematical problem datasets with diverse annotation styles. |
| Outcome: | The proposed method outperforms existing methods that rely heavily on LLM teachers . it grants models generalization ability across views and datasets, and the capability to learn from inaccurate or incomplete data. |
Mitigating Linguistic Artifacts in Emotion Recognition for Conversations from TV Scripts to Daily Conversations (2024.lrec-main)
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| Challenge: | Existing studies on Emotion Recognition in Conversations (ERC) focus on training and testing models on the same datasets and there is no prior work on adaptability. |
| Approach: | They propose to use contrastive learning to prioritize emotional features over a linguistic style and refining emotion predictions with pseudo-emotion intensity score to improve model's robustness and accuracy in diverse conversational contexts. |
| Outcome: | The proposed techniques reduce reliance on linguistic artifacts found in TV transcripts and improve model’s robustness and accuracy in diverse conversational contexts. |
Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering (2024.lrec-main)
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| Challenge: | Large language models have demonstrated remarkable capabilities by leveraging chain-of-thought reasoning techniques to solve complex questions. |
| Approach: | They propose a method that assesses the entailment relationship between the question and the candidate reasoning chain and uses it to predict the answer. |
| Outcome: | The proposed approach improves the fine-tuned T5 baseline over the ScienceQA, ECQA, and LastLetter tasks. |
Mitigating Shortcuts in Language Models with Soft Label Encoding (2024.lrec-main)
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| Challenge: | Recent studies have shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. |
| Approach: | They propose a framework for debiasing shortcuts and a dummy class to encode shortcuts into a model and use it to generate soft labels. |
| Outcome: | The proposed framework significantly improves out-of-distribution generalization while maintaining satisfactory in-district accuracy. |
Mitigating Translationese in Low-resource Languages: The Storyboard Approach (2024.lrec-main)
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Garry Kuwanto, Eno-Abasi E. Urua, Priscilla Amondi Amuok, Shamsuddeen Hassan Muhammad, Anuoluwapo Aremu, Verrah Otiende, Loice Emma Nanyanga, Teresiah W. Nyoike, Aniefon D. Akpan, Nsima Ab Udouboh, Idongesit Udeme Archibong, Idara Effiong Moses, Ifeoluwatayo A. Ige, Benjamin Ajibade, Olumide Benjamin Awokoya, Idris Abdulmumin, Saminu Mohammad Aliyu, Ruqayya Nasir Iro, Ibrahim Said Ahmad, Deontae Smith, Praise-EL Michaels, David Ifeoluwa Adelani, Derry Tanti Wijaya, Anietie Andy
| Challenge: | Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which introduce the translationese effect. |
| Approach: | They propose a method that uses storyboards to elicit more fluent and natural sentences from native speakers without direct exposure to the source text. |
| Outcome: | The proposed method compared with traditional translation-based methods in terms of accuracy and fluency. |
MixRED: A Mix-lingual Relation Extraction Dataset (2024.lrec-main)
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| Challenge: | Existing research focuses on monolingual relation extraction, but there is a significant gap in understanding relation extraction in the mix-lingual scenario. |
| Approach: | They propose a task of considering relation extraction in the mix-lingual scenario . they construct a human-annotated dataset to support the task . |
| Outcome: | The proposed task evaluates state-of-the-art supervised models and large language models on the human-annotated dataset MixRED. |
Mixture-of-LoRAs: An Efficient Multitask Tuning Method for Large Language Models (2024.lrec-main)
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| Challenge: | Instruction Tuning has the potential to stimulate or enhance specific capabilities of large language models. |
| Approach: | They propose a mixture-of-LoRAs architecture which is a parameter-efficient tuning method designed for multi-task learning with LLMs. |
| Outcome: | The proposed method can be iteratively adapted to a new domain, enabling quick domain-specific adaptation. |
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding (2024.lrec-main)
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| Challenge: | Multimodal semantic understanding is crucial for developing machines capable of interpreting complex interplay of text and visual information. |
| Approach: | They propose a multi-modal soft prompt framework that integrates three experts of soft prompts . they propose sarcasm detection and sentiment analysis tasks that are critical for few-shot learning . |
| Outcome: | The proposed model outperforms the 8.2B model InstructBLIP with 2% parameters . it significantly outperformed other prompt methods on VLMs or task-specific methods . |
MKeCL: Medical Knowledge-Enhanced Contrastive Learning for Few-shot Disease Diagnosis (2024.lrec-main)
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| Challenge: | Existing approaches to disease classification are limited in real-world clinics due to insufficient data and inflexibility. |
| Approach: | They propose a medical knowledge-Enhanced Contrastive Learning approach to disease diagnosis . they incorporate medical knowledge graphs and medical licensing exams in modeling . |
| Outcome: | The proposed model outperforms existing models on real clinical EMRs on a single patient. |
MLDSP-MA: Multidimensional Attention for Multi-Round Long Dialogue Sentiment Prediction (2024.lrec-main)
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| Challenge: | Existing methods for dialogue sentiment prediction are weak, resulting in errors. |
| Approach: | They propose a multi-round long dialogue sentiment prediction model based on multidimensional attention that captures historical dialogues and integrates with local attention. |
| Outcome: | The proposed model improves by 3.5% in accuracy and 5.7% in Micro-F1 score on dialogue datasets. |
MMAD:Multi-modal Movie Audio Description (2024.lrec-main)
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| Challenge: | Current methods of creating accessible movies rely on manual work, resulting in high costs and limited scalability. |
| Approach: | They propose a multi-modal movie audio description pipeline that generates narrations of information that is not accessible through unimodal hearing in movies. |
| Outcome: | The proposed pipeline surpasses existing baselines in performance on widely used datasets. |
MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization (2024.lrec-main)
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| Challenge: | Existing product summarization methods lack end-to-end product summaries and multi-grained multi-modal modeling. |
| Approach: | They propose an end-to-end multi-grained multi-modal attribute-aware product summarization method that jointly models product attributes and generates product summaries. |
| Outcome: | The proposed method outperforms state-of-the-art product summarization methods on a large-scale Chinese e-commence dataset. |
MM-IGLU: Multi-Modal Interactive Grounded Language Understanding (2024.lrec-main)
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| Challenge: | In human-robot interaction, a robot interprets user commands related to its environment, aiming to discern whether a specific command can be executed. |
| Approach: | They propose to integrate user statements with environment's description to create a multi-modal interactive Grounded language understanding model that integrates both visual and textual data. |
| Outcome: | The proposed model integrates user’s statement with environment’s description and a cutting-edge Multi-Modal Large Language Model merges both visual and textual data. |
MNER-MI: A Multi-image Dataset for Multimodal Named Entity Recognition in Social Media (2024.lrec-main)
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| Challenge: | Recent research has focused on multimodal named entity recognition (MNER) but current approaches focus on text and a single accompanying image, leaving a significant research gap in multi-image scenarios. |
| Approach: | They propose to construct a human-annotated MNER dataset with multiple images called MNER-MI and a temporal prompt model with multiple image to address the new challenges in multi-image scenarios. |
| Outcome: | The proposed method achieves state-of-the-art results on both MNER-MI and MNER -MI-Plus, demonstrating its effectiveness. |
Modalities Should Be Appropriately Leveraged: Uncertainty Guidance for Multimodal Chinese Spelling Correction (2024.lrec-main)
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| Challenge: | Chinese spelling correction (CSC) aims to detect and correct spelling errors in Chinese texts. |
| Approach: | They propose a framework that incorporates uncertainty into feature learning and correction stages . they propose to combine the uncertainty of multimodal features with model learning . |
| Outcome: | The proposed framework improves on three public datasets. |
MoDE-CoTD: Chain-of-Thought Distillation for Complex Reasoning Tasks with Mixture of Decoupled LoRA-Experts (2024.lrec-main)
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| Challenge: | Current Chain-of-thought Distillation methods hinder CoT reasoning performance . student models are separately distilled from specific reasoning tasks . parameter update of student models severely harms CoT ability on unseen reasoning tasks. |
| Approach: | They propose a method which distills Chain-of-thought reasoning ability of large language models to much smaller student models. |
| Outcome: | The proposed method improves the reasoning ability of large language models on 14 datasets. |
Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing (2024.lrec-main)
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| Challenge: | Discourse Representation Structure (DRS) parsers are constrained when trained exclusively on monolingual data. |
| Approach: | They propose a cross-lingual training strategy that leverages cross-linguistic training data to train models in multiple languages. |
| Outcome: | The proposed method improves clause and graph parsing in English, German, Italian and Dutch. |
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation (2024.lrec-main)
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| Challenge: | Health coaching is a patient-centered clinical practice that aims to help patients achieve personalized and lifestyle-related goals to enhance their health behaviors. |
| Approach: | They propose a neuro-symbolic goal summarizer to support health coaches in keeping track of the goals and a text-units-text dialogue generation model that converses with patients and helps them create and accomplish specific goals for physical activities. |
| Outcome: | The proposed model outperforms existing state-of-the-art models while eliminating the need for predefined schema and corresponding annotations. |
Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin (2024.lrec-main)
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| Challenge: | Existing datasets for Nigerian Pidgin are characterised by noise in the form of orthographic variations. |
| Approach: | They propose a phonetic-theoretic framework to generate orthographic variations to augment training data. |
| Outcome: | The proposed framework improves machine translation and sentiment analysis by combining real and synthesized orthographic variations. |
Modeling the Quality of Dialogical Explanations (2024.lrec-main)
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Milad Alshomary, Felix Lange, Meisam Booshehri, Meghdut Sengupta, Philipp Cimiano, Henning Wachsmuth
| Challenge: | Existing studies have focused on the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. |
| Approach: | They construct a corpus of 399 reddit dialogues and analyze interaction flows and explainee quality using two language models that can handle long inputs. |
| Outcome: | The proposed model predicts that the interaction flows between the explainer and the explainee correlate with the quality of the explanations in terms of a successful understanding on the explain's side. |
Modelling and Linking an Old Latin-Portuguese Dictionary to the LiLa Knowledge Base (2024.lrec-main)
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| Challenge: | lexical and lexicographic information of Antonio Velez's bilingual Latin-Portuguese dictionary was modelled using the Lexicon Model for Ontologies and its lexicog module. |
| Approach: | This paper describes steps undertaken to include data from Antonio Velez’s bilingual Latin-Portuguese dictionary into the LiLa Knowledge Base of interoperable linguistic resources for Latin. |
| Outcome: | The proposed model includes lexical and lexicographic information from the source dictionary with those of the LiLa collection of Latin lemmas. |
Modelling Argumentation for an User Opinion Aggregation Tool (2024.lrec-main)
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| Challenge: | Existing methods for eliciting information from user opinion data are limited to high-level text and are prone to hallucination, degrading system performance or introduce biases. |
| Approach: | They propose an argumentation annotation scheme that models argumentative structure across user opinion domains. |
| Outcome: | The proposed model can predict arguments and contextual details from user opinions . the model can rank products based on user opinions and improve user experience . |
MoNMT: Modularly Leveraging Monolingual and Bilingual Knowledge for Neural Machine Translation (2024.lrec-main)
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| Challenge: | Existing models for multi-domain translation tasks only use monolingual data, whereas bilingual data is indispensable for improving the models. |
| Approach: | They propose a modular strategy that facilitates the cooperation of monolingual and bilingual knowledge in translation tasks by avoiding catastrophic forgetting. |
| Outcome: | The proposed model exhibits superior generalization and robustness over the conventional approach. |
Monolingual Paraphrase Detection Corpus for Low Resource Pashto Language at Sentence Level (2024.lrec-main)
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| Challenge: | Existing research on sentence-level paraphrase detection in Pashto has focused on English, but no work has been done on low-resource Pashtone. |
| Approach: | They propose to annotate sentences in Pashto to detect paraphrases . they will publicize a subset of 1,800 instances from their corpus, free from licensing issues. |
| Outcome: | The proposed corpus contains 6,727 sentences, encompassing 3,687 paraphrased and 3,040 non-paraphrased sentences. |
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking (2024.lrec-main)
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| Challenge: | Existing zero-shot dialogue state tracking models suffer from domain transferring and partial prediction problems. |
| Approach: | They propose to establish connections between similar slots in different domains to improve model transfer performance in unseen domains. |
| Outcome: | Empirical results show that the proposed model achieves the goal accuracy of 57.13% on MultiWOZ2.1 and 55.4. |
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces (2024.lrec-main)
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| Challenge: | Existing approaches to offline reinforcement learning (RL) focus on learning value functions or policy gradients, but they view it as a sequence modeling task. |
| Approach: | They propose a method that integrates multimodal and pre-trained language models to transform offline reinforcement learning into a supervised learning task by integrating state information derived from images and action-related data obtained from text. |
| Outcome: | The proposed approach outperforms baselines on Atari and OpenAI Gym environments while promoting long-term strategic thinking. |
Morpheme Sense Disambiguation: A New Task Aiming for Understanding the Language at Character Level (2024.lrec-main)
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| Challenge: | Morphemes are a strong linguistic feature to capture lexical semantics, but lack of morpheme-informed resources and the expense of manual annotations hinder morphme-enhanced methods. |
| Approach: | They propose a task of Morpheme Sense Disambiguation with two subtasks in-text and in-word to generalize morpheme features on more tasks. |
| Outcome: | The proposed tasks are based on two morpheme-annotated datasets for Chinese . the best model yields a promising precision of 77.66% on in-text and 88.19% on in word . |
Motion Capture Analysis of Verb and Adjective Types in Austrian Sign Language (ÖGS) (2024.lrec-main)
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Julia Krebs, Evguenia A. Malaia, Isabella Fessl, Hans-Peter Wiesinger, Dietmar Roehm, Ronnie Wilbur, Hermann Schwameder
| Challenge: | Temporal and spatial characteristics of dominant hand articulation are used to express semantic and grammatical features across sign languages. |
| Approach: | They use motion capture data from four Deaf signers to characterize kinematic parameters of sign production in verbs and adjectives. |
| Outcome: | The results show that the endpoint marking in verbs and marking of intensification in adjectives are expressed by movement modulation in GS. |
Motion Generation from Fine-grained Textual Descriptions (2024.lrec-main)
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| Challenge: | Existing models for motion generation from textual descriptions are limited to coarse-grained descriptions. |
| Approach: | They build a large-scale language-motion dataset specializing in fine-grained textual descriptions . they feed it with step-by-step instructions with pseudo-code compulsory checks . quantitative evaluation shows that the model outperforms MotionDiffuse in generating spatially or chronologically composite motions . |
| Outcome: | The proposed model outperforms existing models in generating human motion sequences from textual descriptions by a large margin. |
Motivational Interviewing Transcripts Annotated with Global Scores (2024.lrec-main)
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| Challenge: | Motivational interviewing (MI) is a counseling approach that aims to increase intrinsic motivation and commitment to change. |
| Approach: | They propose to annotate MI therapy sessions written in English from public sources . they explore the potential use of the dataset for training MI language models . |
| Outcome: | The proposed dataset includes 242 MI demonstration transcripts annotated with therapist behavioral codes and global scores and client language EAsy Rating (CLEAR) tags for client speech. |
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property (2024.lrec-main)
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Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu
| Challenge: | Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks. |
| Approach: | They propose a benchmark for the evaluation of large language models in the IP domain . they also propose supervised multilingual large language model called MoZi . |
| Outcome: | The proposed model outperforms four well-known LLMs on the MoZIP benchmark . the most powerful ChatGPT does not reach the passing level . |
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training (2024.lrec-main)
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| Challenge: | Experimental evaluations conducted on the CMeEE, a benchmark for Chinese nested medical named entity recognition (NER) model outperforms the compared state-of-the-art (SOTA) models. |
| Approach: | They propose a model based on machine reading comprehension that uses a task-adaptive pre-training strategy to improve the model’s capability in the medical field. |
| Outcome: | The proposed model outperforms the compared state-of-the-art models on the CMeEE, a benchmark for Chinese nested medical NER. |
MRT: Multi-modal Short- and Long-range Temporal Convolutional Network for Time-sync Comment Video Behavior Prediction (2024.lrec-main)
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| Challenge: | Using time-sync comments, it is difficult to understand user behavior due to complexity of interactions between users, videos, and comments. |
| Approach: | They propose a novel time-sync comment behavior prediction model that takes historical behavior into account and optimizes it on the basis of user preferences. |
| Outcome: | The proposed model improves the performance of time-sync comments on visual frames and textual comments on two cats playing simultaneously. |
MUCH: A Multimodal Corpus Construction for Conversational Humor Recognition Based on Chinese Sitcom (2024.lrec-main)
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| Challenge: | Existing multimodal corpora for conversational humor are coarse-grained and insufficient to support the conversational comprehension task. |
| Approach: | They constructed a multimodal humor corpus based on a Chinese sitcom and used both unimodal and multimodal methods to test the corpus. |
| Outcome: | The proposed method outperforms unimodal and multimodal methods in the evaluation of a Chinese sitcom for conversational humor recognition. |
Multi-Channel Spatio-Temporal Transformer for Sign Language Production (2024.lrec-main)
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| Challenge: | Sign language production models ignore structural correlations between channels and use multi-channel spatial attention to capture correlations across channels. |
| Approach: | They propose a novel approach to transform sign language into a unified feature representation using multi-channel spatial attention and temporal attention to learn sequential dependencies for each channel over time. |
| Outcome: | The proposed model outperforms state-of-the-art models on two sign language datasets from diverse cultures. |
MULTICOLLAB: A Multimodal Corpus of Dialogues for Analyzing Collaboration and Frustration in Language (2024.lrec-main)
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| Challenge: | Existing methods to study complex emotions when a speaker collaborates with a partner are limited. |
| Approach: | They propose to fuse a multimodal dialogue resource with transcribed speech and eye gaze data to create a highly multimodal corpus. |
| Outcome: | The proposed model improves classification accuracy by 21% over baseline using sensor and speech data in 4.5 seconds. |
Multi-Dimensional Machine Translation Evaluation: Model Evaluation and Resource for Korean (2024.lrec-main)
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| Challenge: | Existing studies on MT evaluation characterize quality of output with a single number . a recent advancement in MT technologies has enabled higher-quality, more nuanced translations . |
| Approach: | They propose a 1200-sentence MQM evaluation benchmark for English-Korean and a reference-free QE setup to evaluate the quality of the translations. |
| Outcome: | The proposed model outperforms the existing model in style and accuracy. |
Multi-domain Hate Speech Detection Using Dual Contrastive Learning and Paralinguistic Features (2024.lrec-main)
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| Challenge: | a recent study shows that hate speech is spread on social networks and can have social and cultural effects . 41% of americans who took the survey have experienced some type of online harassment . |
| Approach: | They propose a hate speech detection model using contrastive learning loss combined with traditional cross-entropy loss. |
| Outcome: | The proposed model outperforms comparable models on heated topics from two datasets . the model scored macro-F1 on two- and five-class tasks and averaged for four domains compared . |
Multi-Grained Conversational Graph Network for Retrieval-based Dialogue Systems (2024.lrec-main)
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| Challenge: | Existing methods for retrieval-based dialogues concatenate all turns in the dialogue history as input, ignoring dialogue dependency and structural information between the utterances. |
| Approach: | They propose a multi-grained conversational graph network that considers multiple levels of abstraction from dialogue histories and semantic dependencies within multi-turn dialogues for addressing. |
| Outcome: | The proposed method improves on two benchmarks on open domain dialogues. |
Multi-Granularity Fusion Text Semantic Matching Based on WoBERT (2024.lrec-main)
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| Challenge: | Existing text-matching methods struggle with semantic nuances in short texts . a novel approach to improve text semantic matching is being developed . |
| Approach: | They propose a multi-granularity fusion model that harnesses a pre-trained language model to capture text semantic nuances. |
| Outcome: | The proposed model improves on Chinese short text matching datasets compared to traditional methods . the proposed model captures individual text semantic nuances and improves accuracy . |
MultiLeg: Dataset for Text Sanitisation in Less-resourced Languages (2024.lrec-main)
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| Challenge: | Text sanitization is the task of detecting and removing personal information from the text. |
| Approach: | They propose a dataset for multilingual named entities that can be used for text sanitization. |
| Outcome: | The proposed dataset is available in 8 languages and contains 3082 parallel text segments for each language. |
MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations (2024.lrec-main)
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Dagmar Gromann, Hugo Goncalo Oliveira, Lucia Pitarch, Elena-Simona Apostol, Jordi Bernad, Eliot Bytyçi, Chiara Cantone, Sara Carvalho, Francesca Frontini, Radovan Garabik, Jorge Gracia, Letizia Granata, Fahad Khan, Timotej Knez, Penny Labropoulou, Chaya Liebeskind, Maria Pia Di Buono, Ana Ostroški Anić, Sigita Rackevičienė, Ricardo Rodrigues, Gilles Sérasset, Linas Selmistraitis, Mahammadou Sidibé, Purificação Silvano, Blerina Spahiu, Enriketa Sogutlu, Ranka Stanković, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene, Slavko Zitnik, Katerina Zdravkova
| Challenge: | Prior work has focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs) with some exceptions. |
| Approach: | They propose to use a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian as an experiment on cross-lingual transfer of relational knowledge. |
| Outcome: | The proposed dataset is adapted from a BATS-based dataset in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian. |
Multilingual Brain Surgeon: Large Language Models Can Be Compressed Leaving No Language behind (2024.lrec-main)
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| Challenge: | Existing methods for MC focus on quantization and network pruning. |
| Approach: | They propose a calibration method that samples calibration data from various languages proportionally to the language distribution of the model training datasets. |
| Outcome: | The proposed method improves the performance of existing English-centric compression methods on the BLOOM multilingual LLM. |
Multilingual Coreference Resolution in Low-resource South Asian Languages (2024.lrec-main)
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| Challenge: | Existing coreference resolution models for South Asian languages are limited . a a sanity check for the prediction of translations is required to ensure accuracy of the model, authors say . |
| Approach: | They evaluate an end-to-end coreference resolution model on a Hindi golden set . they use translation and word-alignment tools to translate a translated dataset into 31 languages . |
| Outcome: | The proposed model scored 64 and 68 on a Hindi golden set. |
Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)
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| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
| Approach: | They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing . |
| Outcome: | The proposed model improves in high-resource, low-resourced, and zero-shot scenarios. |
Multilinguality or Back-translation? A Case Study with Estonian (2024.lrec-main)
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| Challenge: | a limited amount of parallel data is available for machine translation, and synthetic data is often used to improve translation quality. |
| Approach: | They propose a large-scale synthetic corpus of Estonian translations that contains over 1 billion parallel sentences. |
| Outcome: | The proposed model improves the baseline model while maintaining multilinguality . the proposed model is 6 times larger than the Estonian corpus and twice the size of the Estonial part of the CulturaX corpus. |
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications (2024.lrec-main)
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| Challenge: | Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference data to build high-performance models can outperform conventional methods. |
| Approach: | They propose a multilingual sentence embedding model by extending an existing monolingual model by using the low-rank adaptation technique. |
| Outcome: | The proposed model outperforms the previous approach and shows that languages with fewer resources or those with less linguistic similarity to English benefit more from the parameter increase. |
Multilingual Substitution-based Word Sense Induction (2024.lrec-main)
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| Challenge: | Word Sense Induction is the task of finding senses of an ambiguous word . many approaches to WSI are language-specific and are not easily adaptable to new languages. |
| Approach: | They propose to use multilingual substitution-based WSI methods that generalize to any language supported by the underlying multilingual language model with minimal to no adaptation required. |
| Outcome: | The proposed methods perform on par with monolingual approaches on popular English datasets while being language-specific. |
Multilingual Turn-taking Prediction Using Voice Activity Projection (2024.lrec-main)
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| Challenge: | a monolingual model does not make good predictions when applied to other languages, but a multilingual model is able to discern the language of the input signal. |
| Approach: | They propose to use a multilingual voice activity projection model to predict voice activities of spoken dialogue participants in English, Mandarin, and Japanese data. |
| Outcome: | The proposed model predicts the upcoming voice activities of participants in dyadic dialogue on multilingual data, encompassing English, Mandarin, and Japanese. |
Multimodal and Multilingual Laughter Detection in Stand-Up Comedy Videos (2024.lrec-main)
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| Challenge: | Using TED talks, we use laughter detection software to capture humor in the sitcom genre. |
| Approach: | They develop a multimodal multilingual dataset in Russian and English with a particular emphasis on laughter detection techniques. |
| Outcome: | The proposed model outperforms peak detection and machine learning, while the latter shows promise and warrants further study. |
Multimodal Behaviour in an Online Environment: The GEHM Zoom Corpus Collection (2024.lrec-main)
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| Challenge: | Several studies have discussed pros and cons of videoconferencing for group meetings, international conference organisation and teaching. |
| Approach: | They propose to use 12 video recordings of Zoom meetings held in English by an international group of researchers from September 2021 to March 2023 to study group communication in a reallife setting. |
| Outcome: | The proposed corpus was developed under the auspices of the international network on Gesture and Head Movement in Language (GEHM) it shows that the participants' speech transcription and visual keypoint values can be visualised to see how gestural behaviour supports feedback words during the interaction. |
Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles (2024.lrec-main)
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Abhijnan Nath, Huma Jamil, Shafiuddin Rehan Ahmed, George Arthur Baker, Rahul Ghosh, James H. Martin, Nathaniel Blanchard, Nikhil Krishnaswamy
| Challenge: | Existing methods for cross-document coreference resolution do not provide images for all mentions of events. |
| Approach: | They propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models. |
| Outcome: | The proposed method improves on a popular ECB+ and AIDA datasets. |
Multimodal Cross-lingual Phrase Retrieval (2024.lrec-main)
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| Challenge: | Existing approaches to cross-lingual phrase retrieval only deal with textual modality, leaving the question of the effectiveness of using multimodal information unanswered. |
| Approach: | They propose a multimodal cross-lingual phrase retrieval resource that integrates a Wikimedia Commons media store and a large multimodal pre-trained model to bridge the gap between different modalities. |
| Outcome: | The proposed approach performs significantly better than pure textual cross-lingual phrase retrieval on a benchmarked dataset covering eight language pairs. |
Multimodal Language Models Show Evidence of Embodied Simulation (2024.lrec-main)
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| Challenge: | Multimodal large language models (MLLMs) are gaining popularity as partial solutions to the “symbol grounding problem” faced by language models trained on text alone. |
| Approach: | They propose to use multimodal large language models to integrate linguistic representations with data from other modalities to investigate whether they are integrated into a model. |
| Outcome: | The proposed models are sensitive to visual features like object shape when it is implied by a verbal description of an event. |
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment (2024.lrec-main)
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| Challenge: | Current work on multi-modal semantic understanding primarily exploits a dual-encoder structure to separate image and text, but fails to learn cross-modal feature alignment. |
| Approach: | They propose a CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment by projecting features from different modalities into a unified deep space. |
| Outcome: | The proposed model outperforms baseline models on sarcasm detection and sentiment analysis tasks and is simple to implement without using task-specific external knowledge. |
Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization (2024.lrec-main)
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| Challenge: | Product review summarization aims to generate a concise summary based on product reviews . factual accuracy, aspect comprehensiveness, and content relevance are challenges . |
| Approach: | They propose an FB-Thinker framework to improve product review summarization ability . they propose two Chinese product review summary datasets for instruction-tuning and evaluation . |
| Outcome: | The proposed framework improves product review summarization with forward reasoning and backward refinement. |
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)
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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs). |
| Approach: | They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives. |
| Outcome: | The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives. |
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition (2024.lrec-main)
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Yash Jain, David M. Chan, Pranav Dheram, Aparna Khare, Olabanji Shonibare, Venkatesh Ravichandran, Shalini Ghosh
| Challenge: | Existing methods for pre-training for automatic speech recognition (ASR) focus on single-stage pre-train followed by fine-tuning on downstream task. |
| Approach: | They propose a multi-modal pre-training method that combines unsupervised pre-training with translation-based supervised mid-training. |
| Outcome: | The proposed method improves WERs by 38.45% over baselines on both Librispeech and SUPERB. |
Multi-stream Information Fusion Framework for Emotional Support Conversation (2024.lrec-main)
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| Challenge: | Existing methods for ESC do not capture the dynamic transition of emotion intensity due to the difficulty to model its dynamic transition. |
| Approach: | They propose to fuse three streams for the effective modelling of emotion intensity using a multi-stream fusion unit. |
| Outcome: | The proposed model reduces the emotional distress of users with high-intensity of negative emotions by incorporating three different kinds of streams for the dynamic transition of emotion intensity. |
Multi-Tiered Cantonese Word Segmentation (2024.lrec-main)
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| Challenge: | Existing work on word segmentation for Chinese does not have conventional word boundaries as English does. |
| Approach: | They propose a linguistically motivated, multi-tiered word segmentation system for Cantonese . they propose linguisticly motivated, linguistic-motivated system that can cater to different needs . |
| Outcome: | The proposed system can be adapted to Cantonese corpus data. |
Murre24: Dialect Identification of Finnish Internet Forum Messages (2024.lrec-main)
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| Challenge: | 94 million messages posted on the largest Finnish internet forum, Suomi24, are classified to present either the standard language, one of the seven traditional dialects, a colloquial style or the Helsinki slang. |
| Approach: | They present a collection of dialectal messages posted on the largest Finnish internet forum, Suomi24 . they manually annotated a dataset and used it to train dialect identification models . |
| Outcome: | The proposed method is the best for differentiating standard Finnish from non-standard Finnish, while fine-tuning a BERT-based model achieves best scores on the final dialect identification task. |
MVP: Minimal Viable Phrase for Long Text Understanding (2024.lrec-main)
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| Challenge: | Renewed interest in understanding long texts has sparked interest in benchmarks based on length of input text . |
| Approach: | They propose a new metric that determines the shortest average text length that needs to be preserved to execute the task with limited performance degradation. |
| Outcome: | The proposed benchmarks show that models outperform the previous generation on the QuALITY task due to their limited understanding of long-range dependencies. |
MWE-Finder: A Demonstration (2024.lrec-main)
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| Challenge: | MWE Finder is an application to search for flexible multiword expressions in text corpora. |
| Approach: | They introduce and demonstrate an application to search for flexible multiword expressions in Dutch text corpora. |
| Outcome: | The proposed system can find flexible multiword expressions in large text corpus faster and more reliable than other search applications. |
myMediCon: End-to-End Burmese Automatic Speech Recognition for Medical Conversations (2024.lrec-main)
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| Challenge: | Existing medical conversation speech corpora for Burmese are limited, despite advances in ASR. |
| Approach: | They propose to use a manually curated medical conversation speech corpus for Burmese to examine the performance of ASR models. |
| Outcome: | The proposed model outperforms the Transformer model and the Recurrent Neural Network (RNN) models. |
My Science Tutor (MyST)–a Large Corpus of Children’s Conversational Speech (2024.lrec-main)
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| Challenge: | a 13-year project was conducted between 2007 and 2019 to improve students' learning proficiency in elementary school science using conversational multimedia virtual tutor, Marni. |
| Approach: | They propose to use the corpus-name corpus to improve automatic speech recognition models and algorithms by training and developing a model on the training and development portion of the corpuse. |
| Outcome: | The corpus comprises 400 hours of speech, spanning some 230K utterances spread across about 10,500 virtual tutor sessions. |
NAIST-SIC-Aligned: An Aligned English-Japanese Simultaneous Interpretation Corpus (2024.lrec-main)
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| Challenge: | Simultaneous interpretation data is a task where an utterance is translated in real-time. |
| Approach: | They propose to use an automatically-aligned parallel English-Japanese SI dataset to make it suitable for model training. |
| Outcome: | The proposed model improves translation quality and latency over baselines. |
NarrativeTime: Dense Temporal Annotation on a Timeline (2024.lrec-main)
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| Challenge: | e.g. TimeBank contains 1-5% of all possible tlinks, and this information is underspecified in the text. |
| Approach: | They propose a timeline-based framework that achieves full coverage of all possible TLINKs. |
| Outcome: | The proposed framework achieves full coverage of all possible TLINKs in a text. |
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science (2024.lrec-main)
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Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song
| Challenge: | Existing instruction-tuned Large Language Models (LLMs) have impressive language understanding and the capacity to generate responses that follow specific prompts. |
| Approach: | They evaluate the zero-shot performance of two publicly accessible LLMs, ChatGPT and OpenAssistant, in the context of six Computational Social Science classification tasks. |
| Outcome: | The proposed LLMs perform better than state-of-the-art models on social science tasks. |
NB Uttale: A Norwegian Pronunciation Lexicon with Dialect Variation (2024.lrec-main)
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| Challenge: | lexicon is based on the NST Bokml lexiconic for East Norwegian . lexica are an essential linguistic resource in speech recognition and speech synthesis systems . |
| Approach: | They propose to use Bokml orthographic word forms and up to eight alternate phonological transcriptions per word form to generate a Norwegian pronunciation lexicon. |
| Outcome: | The proposed model improves the accuracy of the proposed model and its outputs with word- and phoneme-error-rate metrics. |
Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset (2024.lrec-main)
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| Challenge: | Negation scope resolution models that use pre-trained language models perform worse when fine-tuned on a combined dataset. |
| Approach: | They propose to automatically convert the negation scopes of BioScope and SFU to those of Sherlock and merge them into a unified dataset. |
| Outcome: | The proposed method improves on the unified dataset compared to the simply combined dataset. |
Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency (2024.lrec-main)
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| Challenge: | Negation understanding is crucial to many downstream tasks such as sentiment analysis, question answering, Web search and natural language inference. |
| Approach: | They propose a novel negation triplet extraction task which aims to extract negation subject along with negation cue and scope. |
| Outcome: | The proposed model is based on a generative pretrained language model with a multi-task learning framework and achieves the best performance compared to baselines. |
nEMO: Dataset of Emotional Speech in Polish (2024.lrec-main)
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| Challenge: | Existing datasets covering Slavic languages do not accurately represent basic emotional states. |
| Approach: | They propose to use a Polish corpus of emotional speech to represent basic emotional states. |
| Outcome: | The proposed corpus represents six emotional states in Polish, with 9 actors participating in the study. |
NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)
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| Challenge: | Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy. |
| Approach: | They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies . |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets. |
Nested Event Extraction upon Pivot Element Recognition (2024.lrec-main)
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Weicheng Ren, Zixuan Li, Xiaolong Jin, Long Bai, Miao Su, Yantao Liu, Saiping Guan, Jiafeng Guo, Xueqi Cheng
| Challenge: | Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. |
| Approach: | They propose a new model that extracts nested events mainly based on recognizing PEs. |
| Outcome: | The proposed model can extract nested events based on recognizing PEs . it incorporates information from both event types and argument roles to improve performance . |
Nested Noun Phrase Identification Using BERT (2024.lrec-main)
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| Challenge: | a number of methods for identifying noun phrases have been developed . chunking-like methods do not represent the fact that noun phrase can be nested. |
| Approach: | They propose a method of finding all noun phrases in a sentence nested to an arbitrary depth using the BERT model for token classification. |
| Outcome: | The proposed method achieves very good results for both Swedish and English . it is based on the BERT model for token classification . |
Neural Machine Translation between Low-Resource Languages with Synthetic Pivoting (2024.lrec-main)
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| Challenge: | Pivot-based neural machine translation systems overcome data scarcity by including a high-resource pivot language in the process of translating between low-resourced languages. |
| Approach: | They propose a novel approach to pivot-based translation in which pivot sentences are generated synthetically from both the source and target languages. |
| Outcome: | The proposed approach improves pivot-based systems translating between low-resource Southern African languages by up to 5.6 BLEU points after fine-tuning. |
Neural Multimodal Topic Modeling: A Comprehensive Evaluation (2024.lrec-main)
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| Challenge: | Neural topic models can find coherent and diverse topics in textual data, but they are limited in dealing with multimodal datasets. |
| Approach: | They propose two new topic modeling solutions and two new evaluation metrics for document multimodality. |
| Outcome: | The proposed models generate coherent and diverse topics on a rich dataset. |
New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French (2024.lrec-main)
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| Challenge: | DACCORD is a dataset for automatic detection of contradictions between sentences . it is the first dataset exclusively dedicated to this task . |
| Approach: | They introduce DACCORD, a dataset in French for automatic detection of contradictions between sentences. |
| Outcome: | The proposed datasets are more challenging than existing datasets for the mainstream task in French. |
New Evaluation Methodology for Qualitatively Comparing Classification Models (2024.lrec-main)
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| Challenge: | Text Classification is one of the most common tasks in Natural Language Processing. |
| Approach: | They propose a method for performing qualitative assessment over multiple classification models using a fine-tuned BERT and Logistic Regression evaluation methodology. |
| Outcome: | The proposed evaluation methodology outperforms the baseline model in linguistic clustering and Sentiment Analysis. |
New Intent Discovery with Attracting and Dispersing Prototype (2024.lrec-main)
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| Challenge: | Existing methods for detecting new intents with labeled data are not cluster-friendly . a robust prototypical attracting learning (RPAL) method is designed to compel instances to gravitate toward their corresponding prototype . |
| Approach: | They propose a robust and adaptive prototypical learning framework for globally distinct decision boundaries for both known and new intent categories. |
| Outcome: | The proposed method improves on CLINC, BANKING, and StackOverflow benchmarks on three challenging benchmarks. |
New Methods for Exploring Intonosyntax: Introducing an Intonosyntactic Treebank for Nigerian Pidgin (2024.lrec-main)
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Emmett Strickland, Anne Lacheret-Dujour, Sylvain Kahane, Marc Evrard, Perrine Quennehen, Bernard Caron, Francis Egbokhare, Bruno Guillaume
| Challenge: | Using a syntactic treebank, we have augmented a corpus of transcribed Nigerian Pidgin tokens with syllable-level alignments and phonetizations. |
| Approach: | They propose to add a syntactic treebank of Nigerian Pidgin to the corpus and add syllable-level alignments and phonetizations to it. |
| Outcome: | The proposed resource can be used to study the prosodic characteristics of Nigerian Pidgin, a low-resource language of West Africa. |
New Proposal of Greenberg’s Universal 14 from Typometrics (2024.lrec-main)
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| Challenge: | In his Universal 14, Greenberg stated that the normal and dominant order in all world languages was to place the condition before the conclusion in conditional sentences. |
| Approach: | They propose to review Greenberg's Universal 14 and reformulate it quantitatively based on occurrences in real texts in 50 languages. |
| Outcome: | The proposed Universal 14 is based on occurrences in real texts in 50 languages and is fulfilled in 100% of the cases compared to 60% in the sample. |
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark (2024.lrec-main)
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| Challenge: | Intent classification and slot-filling tasks are essential tasks of Spoken Language Understanding (SLU). |
| Approach: | They propose to use a MEDIA SLU dataset to train a multilingual model to achieve both tasks jointly. |
| Outcome: | The proposed model can be trained on multiple datasets including the MEDIA dataset and extends to more tasks and use cases. |
NGLUEni: Benchmarking and Adapting Pretrained Language Models for Nguni Languages (2024.lrec-main)
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| Challenge: | Nguni languages have over 20 million home language speakers in South Africa . there has been considerable growth in the datasets for these languages, but no analysis of the performance of NLP models for these language has been reported across languages and tasks. |
| Approach: | They compile publicly available datasets for natural language understanding and generation, spanning 6 tasks and 11 datasets. |
| Outcome: | The proposed models outperform existing models and large-scale adapted models on cross-lingual transfer and machine translation. |
NLoPT: N-gram Enhanced Low-Rank Task Adaptive Pre-training for Efficient Language Model Adaption (2024.lrec-main)
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| Challenge: | Pre-trained Language Models (PLMs) have superior performance on downstream tasks . however, conventional TAPT adjusts all parameters of the PLMs, which distorts the learned generic knowledge embedded in the original PLM's weights. |
| Approach: | They propose a two-step n-gram enhanced low-rank task adaptive pre-training method to customize a PLM to the downstream task. |
| Outcome: | The proposed method improves performance on six datasets from four domains. |
NLPre: A Revised Approach towards Language-centric Benchmarking of Natural Language Preprocessing Systems (2024.lrec-main)
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| Challenge: | GLUE benchmarking system enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance. |
| Approach: | They propose a language-centric benchmarking system that enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance. |
| Outcome: | The proposed system is configured for Polish and integrated with the thoroughly assembled NLPre-PL benchmark. |
No Need for Large-Scale Search: Exploring Large Language Models in Complex Knowledge Base Question Answering (2024.lrec-main)
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| Challenge: | Knowledge Base Question Answering (KBQA) systems are a key research area in the field of natural language processing and information retrieval (IR). |
| Approach: | They propose to use large language models to convert natural language questions to structured knowledge representations by using a three-step fine-tune strategy to implement the KBQA system. |
| Outcome: | The proposed method achieves state-of-the-art performance across three datasets with a 79.9% F1 score. |
Non-Essential Is NEcessary: Order-agnostic Multi-hop Question Generation (2024.lrec-main)
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| Challenge: | Existing multi-hop question generation methods treat answer-irrelevant documents as non-essential and remove them as impurities, which can lead to a decrease in model performance. |
| Approach: | They propose a task which leverages non-essential data in the training phase to create a robust model and extract the consistent embeddings in real-world inference environments. |
| Outcome: | The proposed model can perform ranker and generator without external modules and achieves state-of-the-art on a hotpotQA dataset. |
NSina: A News Corpus for Sinhala (2024.lrec-main)
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Hansi Hettiarachchi, Damith Premasiri, Lasitha Randunu Chandrakantha Uyangodage, Tharindu Ranasinghe
| Challenge: | introducing large language models (LLMs) has advanced natural language processing (NLP), but their effectiveness is largely dependent on pre-training resources. |
| Approach: | They propose a large news corpus for Sinhala with a set of NLP tasks for the language . NSina is the largest news corpuse for Sinha, available up to date . |
| Outcome: | The proposed model outperforms existing models in many benchmarks and outperformed previous models in high-resource languages. |
Null Subjects in Spanish as a Machine Translation Problem (2024.lrec-main)
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| Challenge: | Existing methods for detecting null subjects and impersonal constructions in Spanish are limited. |
| Approach: | They adapt a machine translation methodology to detect null subjects and impersonal constructions in Spanish using an AnCora corpus. |
| Outcome: | The proposed approach surpasses the state-of-the-art in the detection of null subjects and impersonal constructions in Spanish while using modest computational resources. |
NumHG: A Dataset for Number-Focused Headline Generation (2024.lrec-main)
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| Challenge: | a lack of fine-grained annotations for accurate numeral generation in headlines is a major roadblock . a new dataset, the NumHG, provides over 27,000 annotated numeral-rich news articles for detailed investigation . |
| Approach: | They propose a dataset that provides annotated numerals for headline generation . they evaluate five well-performing headline-generation models using human evaluation . |
| Outcome: | The proposed dataset provides annotated numeral-rich news articles for detailed investigation. |
NutFrame: Frame-based Conceptual Structure Induction with LLMs (2024.lrec-main)
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| Challenge: | Existing studies focus on syntactic knowledge and world knowledge, but conceptual structure is not well-understood. |
| Approach: | They propose a benchmark for coNceptual structure induction based on FrameNet . they use prompts to induce conceptual structure of Framenet with LLMs . |
| Outcome: | The proposed model is able to induce conceptual structure of FrameNet with LLMs. |
OATS: A Challenge Dataset for Opinion Aspect Target Sentiment Joint Detection for Aspect-Based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Aspect-based sentiment analysis (ABSA) focuses on understanding sentiments specific to distinct elements within a user-generated review. |
| Approach: | They propose to use Aspect-based sentiment analysis to understand specific aspects of a user-generated review to identify the target entity being reviewed, the aspect to which it belongs, the opinion phrase, and the sentiment expressed toward the aspects. |
| Outcome: | The proposed dataset bridges the gaps observed in existing datasets and sheds light on various ABSA subtasks. |
OLViT: Multi-Modal State Tracking via Attention-Based Embeddings for Video-Grounded Dialog (2024.lrec-main)
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| Challenge: | Existing video dialog models struggle with questions requiring both spatial and temporal localization within videos, long-term temporal reasoning, and accurate object tracking across multiple dialog turns. |
| Approach: | They propose a multi-modal attention-based model for video dialog operating over a dialog state tracker. |
| Outcome: | The proposed model can learn multi-modal dialog state representations of the most relevant objects and rounds. |
On an Intermediate Task for Classifying URL Citations on Scholarly Papers (2024.lrec-main)
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| Challenge: | Citations using URLs can be used as information source for research resource search engines. |
| Approach: | They propose a method to classify URL citations using a simple fine-tuning strategy. |
| Outcome: | The proposed method outperforms methods using a simple fine-tuning strategy with higher macro F-scores for different model sizes and architectures. |
On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation (2024.lrec-main)
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| Challenge: | a new study examines the use of encoder-only pre-trained language models in keyphrase generation (KPG) keyphrases are phrases that condense salient information of a document. |
| Approach: | They propose to use encoder-only pre-trained language models in keyphrase generation . they also examine optimal architectural decisions for employing encoder only PLMs in KPG . |
| Outcome: | The proposed model outperforms general-domain seq2seq models in keyphrase generation. |
On Modelling Corpus Citations in Computational Lexical Resources (2024.lrec-main)
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| Challenge: | TEI and OntoLex deal with corpus citations in lexicons. |
| Approach: | They argue that TEI and OntoLex can be used to model corpus citations in lexicons . they also argue that they should be combined to achieve a more accurate encoding . |
| Outcome: | The proposed approach favours a combination of TEI and OntoLex . the proposed approach is based on a model of an example entry from a legacy dictionary . |
On the Adaptation of Unlimiformer for Decoder-Only Transformers (2024.lrec-main)
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| Challenge: | despite efforts in the community, most common models have a context length of 4k or less. |
| Approach: | They propose to adapt a vector-retrieval augmentation method to decoder-only transformers . they also expand the experimental setup on summarization to include a new task and an instruction-tuned model . |
| Outcome: | The proposed model performs on par with a model with 2x the context length. |
On the Relationship between Skill Neurons and Robustness in Prompt Tuning (2024.lrec-main)
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| Challenge: | Prompt Tuning is a parameter-efficient finetuning method for pre-trained large language models (PLMs). |
| Approach: | They propose to use RoBERTa to fine tune pre-trained large language models by finetuning only a small set of parameters to adjust for downstream tasks. |
| Outcome: | The proposed method activates specific neurons in the transformer’s feed-forward networks that are highly predictive and selective for the given task. |
On the Scaling Laws of Geographical Representation in Language Models (2024.lrec-main)
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| Challenge: | Language models embed geographical information in their hidden representations, but larger models cannot mitigate this bias. |
| Approach: | They propose to extend this finding to Large Language Models by observing how geographical knowledge evolves when scaling language models. |
| Outcome: | The proposed model scales consistently with increasing model size, but smaller models cannot mitigate geographic bias inherent in training data. |
On the Use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction (2024.lrec-main)
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| Challenge: | Existing zero-shot methods for information extraction use large amounts of gold standard data. |
| Approach: | They propose a framework to utilize silver data to enhance zero-shot classification methods . they propose to use off-the-shelf models of other NLP tasks to perform inference on test data . |
| Outcome: | The proposed framework outperforms baseline methods on TACRED and Wiki80 datasets by 5% and 6% on the zero-shot relation classification task and by 3% 7 % on Smile (Korean and Polish) |
On the Way to Lossless Compression of Language Transformers: Exploring Cross-Domain Properties of Quantization (2024.lrec-main)
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Nikita Martynov, Aleksei Goncharov, Gleb Kumichev, Evgeniy Egorov, Stanislav Vladimirovich Pavlov, Mikhail Sergeevich Durinov, Aleksandr Sergeevich Zuev, Egor Anatolievich Filimonov
| Challenge: | Modern Natural Language Processing models have a huge capacity, but this makes it difficult to employ. |
| Approach: | They propose a method to quantize at least 95% of Transformer weights without access to task-specific data so the drop in performance does not exceed 0.02%. |
| Outcome: | The proposed method quantizes 95% of Transformer weights and corresponding activations to INT8 without access to task-specific data so the drop in performance does not exceed 0.02%. |
On Zero-Shot Counterspeech Generation by LLMs (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) are used in numerous NLP tasks, including counterspeech generation. |
| Approach: | They propose three different prompting strategies for generating different types of counterspeech and propose a set of prompting techniques for counterspeak generation. |
| Outcome: | The proposed prompting strategies improve the performance of the models for counterspeech generation in two datasets, but with high toxicity with increase in model size. |
OOVs in the Spotlight: How to Inflect Them? (2024.lrec-main)
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| Challenge: | Inflection is a process of word formation in which a base word form (lemma) is modified to express grammatical categories. |
| Approach: | They develop a retrograde model and two sequence-to-sequence models based on LSTM and Transformer. |
| Outcome: | The proposed systems outperform the existing systems on 9 out of 16 languages in the OOV evaluation. |
OpenMSD: Towards Multilingual Scientific Documents Similarity Measurement (2024.lrec-main)
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| Challenge: | Existing methods for finding related papers in different languages are not effective for multilingual SDSM. |
| Approach: | They propose to use Open-access Multilingual Scientific Documents to develop multilingual SDSM models that adjust and extend state-of-the-art methods for English SDSM tasks. |
| Outcome: | The proposed model outperforms baseline methods on multilingual SDSM tasks while preserving the performance of the existing methods. |
Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States (2024.lrec-main)
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Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, SeyedAbolghasem Mirroshandel, Owen Rambow
| Challenge: | Existing research on opinion mining has focused on a small subset of the MPQA 2.0 dataset . a recent study focused on the subjective expressions of people who express opinions, sentiments, and attitudes toward targets. |
| Approach: | They propose to use MPQA 2.0 to analyze the entire dataset . they propose to provide a clean version of the MPQA Opinion Corpus in a more interpretable format . |
| Outcome: | The proposed methods establish high baselines for future work. |
Opinions Are Not Always Positive: Debiasing Opinion Summarization with Model-Specific and Model-Agnostic Methods (2024.lrec-main)
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| Challenge: | Existing opinion summarization frameworks are reluctant to generate negative summaries given input of negative opinions. |
| Approach: | They propose to disentangle input into sentiment-relevant and sentiment-irrelevant components through adversarial loss. |
| Outcome: | The proposed approaches reduce sentiment bias in the existing opinion summarization dataset . the proposed approaches generate better summaries with a more balanced emotional polarity distribution . |
Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean (2024.lrec-main)
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ChangSu Choi, Yongbin Jeong, Seoyoon Park, Inho Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim
| Challenge: | Large language models (LLMs) use pretraining to predict the subsequent word, but less-resourced languages are being overlooked. |
| Approach: | They propose to expand the MLLM vocabularies to enhance expressiveness and use bilingual data for pretraining to align the high- and less-resourced languages. |
| Outcome: | The proposed model outperforms existing models in qualitative analyses compared to Korean monolingual models. |
ORTicket: Let One Robust BERT Ticket Transfer across Different Tasks (2024.lrec-main)
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| Challenge: | Pretrained language models are susceptible to subtle perturbations and require multiple adversarial training during fine-tuning to improve their robustness. |
| Approach: | They propose a novel adversarial defense method ORTicket that fine-tunes a model for downstream tasks. |
| Outcome: | The proposed method achieves comparable robustness to other defense methods while maintaining the efficiency of fine-tuning. |
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts (2024.lrec-main)
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| Challenge: | Existing methods for OOD intent detection are limited to single dialogue turns. |
| Approach: | They propose a context-aware OOD intent detection framework to model multi-turn contexts in OOD context detection tasks using unlabeled data. |
| Outcome: | The proposed framework improves the F1-OOD score by 29% on multi-turn OOD detection tasks compared to the previous best method. |
Out of the Mouths of MPs: Speaker Attribution in Parliamentary Debates (2024.lrec-main)
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| Challenge: | Identifying who says what to whom is an essential prerequisite for analysing human communication. |
| Approach: | They propose a new corpus for speaker attribution in german parliamentary debates . the data includes more than 7,700 manually annotated events of speech, thought and writing . they then apply their model to predict speech events in 20 years of debates and investigate the use of factives in the rhetoric of MPs. |
| Outcome: | The proposed model predicts speech events in 20 years of debates and investigates the use of factives in the rhetoric of MPs. |
PACAR: Automated Fact-Checking with Planning and Customized Action Reasoning Using Large Language Models (2024.lrec-main)
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| Challenge: | Existing studies rely on idealized "gold" evidence for predictions, which is unrealistic due to its limited availability in real-world scenarios. |
| Approach: | They propose a fact-checking framework based on planning and customized action reasoning using LLMs. |
| Outcome: | The proposed framework outperforms baseline methods across three datasets and with varying complexity levels. |
PAD: A Robustness Enhancement Ensemble Method via Promoting Attention Diversity (2024.lrec-main)
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| Challenge: | Existing approaches to enhance robustness of deep neural networks focus on perturbation . weak robustness is a problem for many types of adversarial attacks, authors say . |
| Approach: | They propose a lightweight framework for enhancing robustness by perturbing parameters of a model and diversifying adversarial example distributions among different models. |
| Outcome: | The proposed method can improve robustness against adversarial attacks while maintaining accuracy on clean data. |
Palmyra 3.0: A User-Friendly Cloud-Based Platform for Morphology and Dependency Syntax Annotation (2024.lrec-main)
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| Challenge: | Palmyra 3.0 is a cloud-based platform for morphology and syntax annotation. |
| Approach: | They present Palmyra 3.0, a cloud-based platform for morphology and syntax annotation. |
| Outcome: | Palmyra 3.0 provides configuration files for a number of predefined formalisms, such as UD and CATiB, and a variety of user-friendly features to support annotators. |
Parameter-Efficient Transfer Learning for End-to-end Speech Translation (2024.lrec-main)
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| Challenge: | Existing approaches to improve end-to-end speech translation are limited by the availability of labeled data. |
| Approach: | They propose a method which utilizes two lightweight adaptation techniques to modulate Attention and the Feed-Forward Network while preserving the capabilities of pre-trained models. |
| Outcome: | The proposed method outperforms baseline models and significantly improves performance in low-resource settings. |
ParaNames 1.0: Creating an Entity Name Corpus for 400+ Languages Using Wikidata (2024.lrec-main)
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| Challenge: | ParaNames is a massively multilingual parallel name resource . it provides names for 16.8 million entities in over 400 languages . |
| Approach: | They propose a massively multilingual parallel name resource with 140 million names . they use Wikidata to standardize the data and perform canonical name translation . |
| Outcome: | The proposed resource is the largest of its type to date and performs well on 10 languages. |
PaReNT (Parent Retrieval Neural Tool): A Deep Dive into Word Formation across Languages (2024.lrec-main)
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| Challenge: | a significant portion of words in a language share one or more roots with other existing lexemes as a result of word-formation processes. |
| Approach: | They present a deep-learning-based multilingual tool that performs retrieval and word formation classification in seven languages. |
| Outcome: | The proposed tool performs retrieval and word formation classification in English, German, Dutch, Spanish, French, Russian, and Czech. |
Parsing for Mauritian Creole Using Universal Dependencies (2024.lrec-main)
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| Challenge: | a paper demonstrates the construction of a 161-sentence treebank for Mauritian Creole . the parser trained with UD reached F1 scores of UPOS=86.2, UAS=80.8 and LAS=69.8. |
| Approach: | They propose to use Universal Dependencies to train a parser for Mauritian Creole . they demonstrate the construction of a 161-sentence treebank and evaluate the performance . |
| Outcome: | The proposed treebank achieves F1 scores compared to models for other under-resourced Creole languages. |
Parsing Headed Constituencies (2024.lrec-main)
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| Challenge: | Using constituency and dependency trees, syntactic representations are preferred for tasks such as nominal phrase extraction and identification of terminology. |
| Approach: | They propose a parsing technique that generates headed constituency trees which combine information typically contained in constituency and dependency trees. |
| Outcome: | The proposed method generates headed constituency trees with discontinuities and can generate constituency tree with discontinuity. |
PASUM: A Pre-training Architecture for Social Media User Modeling Based on Text Graph (2024.lrec-main)
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| Challenge: | Existing studies have incorporated different digital traces to better learn the representations of social media users, limited by overloaded text information and hard-to-collect social network information. |
| Approach: | They propose a Pre-training Architecture for Social Media User Modeling based on Text Graph and combine microblogs to represent social media users based upon the text graph model. |
| Outcome: | The proposed framework can represent users based on text even without social network information on microblogs. |
Pater Incertus? There Is a Solution: Automatic Discrimination between Cognates and Borrowings for Romance Languages (2024.lrec-main)
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Liviu P. Dinu, Ana Sabina Uban, Ioan-Bogdan Iordache, Alina Maria Cristea, Simona Georgescu, Laurentiu Zoicas
| Challenge: | Existing methods for discriminating between cognates and borrowings are difficult, but they provide a deeper insight into the history of a language and allow for a better characterization of language relatedness. |
| Approach: | They propose a computational approach for discriminating between cognates and borrowings based on a comprehensive database of Romance cognates. |
| Outcome: | The proposed approach is the most comprehensive in terms of covered languages. |
PDAMeta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | Existing text data augmentation methods can not ensure the diversity and quality of the generated data, which leads to sub-optimal performance. |
| Approach: | They propose a meta-learning framework with progressive data augmentation for few-shot text classification using prompt-based data augmented by attention-based methods. |
| Outcome: | The proposed framework outperforms state-of-the-art models and shows better robustness on four public few-shot text classification datasets. |
PEaCE: A Chemistry-Oriented Dataset for Optical Character Recognition on Scientific Documents (2024.lrec-main)
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| Challenge: | Existing open-source OCR models focus on scientific texts or generic printed English . Nougat is unable to parse tables in PubMed articles . |
| Approach: | They propose to train OCR models for scientific or generic printed English . Nougat is a popular tool for parsing academic documents, but unable to parse PubMed tables . |
| Outcome: | The proposed models perform better when trained on real-world records than those trained on synthetic records. |
PECC: Problem Extraction and Coding Challenges (2024.lrec-main)
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| Challenge: | Existing benchmarks evaluate tasks in isolation, yet the extent to which LLMs can understand prose-style tasks, identify the underlying problems, and then generate appropriate code solutions remains mostly unexplored. |
| Approach: | They propose a benchmark derived from Advent Of Code challenges and Project Euler, which requires LLMs to interpret narrative-embedded problems, extract requirements, and generate executable code. |
| Outcome: | The proposed benchmarks show that LLMs can understand prose-style tasks, identify underlying problems, and generate appropriate code solutions in a variety of tasks. |
PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets (2024.lrec-main)
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Arianna Muti, Federico Ruggeri, Cagri Toraman, Alberto Barrón-Cedeño, Samuel Algherini, Lorenzo Musetti, Silvia Ronchi, Gianmarco Saretto, Caterina Zapparoli
| Challenge: | Disambiguating the meaning of pejorative words might help misogyny detection . state-of-the-art models struggle to correctly classify misogoyne when sentences contain such terms. |
| Approach: | They present a corpus of 1,200 manually annotated Italian tweets for pejorative language at the word level and misogyny at the sentence level. |
| Outcome: | The proposed model improves on 1,200 manually annotated Italian tweets and on two benchmarks. |
Persona-aware Multi-party Conversation Response Generation (2024.lrec-main)
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| Challenge: | Recent advances in natural language generation have addressed multi-turn dialogues . interactions with more than 2 participants pose new and interesting challenges for MPC modeling . |
| Approach: | They propose to include persona attributes of speaker and addressee relevant to each utterance in a multi-party conversation dataset and a persona-aware heterogeneous graph transformer response generation model. |
| Outcome: | The proposed model includes persona attributes of speaker and addressee relevant to each utterance. |
Phonetic Segmentation of the UCLA Phonetics Lab Archive (2024.lrec-main)
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| Challenge: | ''big data'' does not exist for the majority of the world's languages . a corpus of audited phonetic transcriptions and phone-level alignments is available for free . |
| Approach: | They present a corpus of audited phonetic transcriptions and phone-level alignments from the UCLA Phonetics Lab Archive . they discuss the utility of the corpus for general research and pedagogy in crosslinguistic phonetics . |
| Outcome: | The VoxAngeles corpus improves the original corpus for phonetic typology and word- and phone duration measurements. |
Phonotactic Complexity across Dialects (2024.lrec-main)
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| Challenge: | Recent studies show a moderate negative correlation between phonotactic complexity and word length in 106 languages. |
| Approach: | They propose to use a phone-level language model to measure phonotactic complexity . they find a tradeoff between word length and phonomactic complex . |
| Outcome: | The proposed model shows that low phonotactic complexity dialects concentrate around capital regions. |
PILA: A Historical-Linguistic Dataset of Proto-Italic and Latin (2024.lrec-main)
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| Challenge: | Historical linguists hypothesize systems of sound change to explain the evolution of language over time, but the evidence is limited. |
| Approach: | They propose a dataset that consists of roughly 3,000 pairs of forms from Proto-Italic and Latin. |
| Outcome: | The proposed dataset enables historical linguists to enhance other datasets by enhancing them with the existing datasets. |
PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods (2024.lrec-main)
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| Challenge: | PIRB is a framework for text information retrieval in Polish . existing and new datasets are evaluated to evaluate the performance of 41 models . |
| Approach: | They propose a framework for 41 text information retrieval tasks in Polish . they evaluate over 20 dense and sparse retrieval models and build sparser-dense hybrid retrievers . |
| Outcome: | The proposed framework outperforms the best available methods in 41 tasks for Polish . the proposed models outperformed the best solutions available to date . |
PLAES: Prompt-generalized and Level-aware Learning Framework for Cross-prompt Automated Essay Scoring (2024.lrec-main)
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| Challenge: | Existing cross-prompt automatic essay scoring systems focus on obtaining shared knowledge specific to the target prompt, but this may not be feasible in practical situations because the target essay may not exist as training data. |
| Approach: | They propose a novel learning framework for cross-prompt automatic essay scoring to capture more general knowledge across different prompts and improve the model’s capacity to distinguish between writing levels. |
| Outcome: | The proposed learning framework captures more general knowledge across prompts and improves its capacity to distinguish between writing levels. |
Plots Made Quickly: An Efficient Approach for Generating Visualizations from Natural Language Queries (2024.lrec-main)
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| Challenge: | Existing methods for generating visualizations from natural language queries have not fully incorporated state-of-the-art techniques such as pre-trained LMs. |
| Approach: | They propose to generate a valid Vega-Lite specification from a data frame and a query as input and render it as a visualization. |
| Outcome: | The proposed model scales better with pre-trained LMs than current state-of-the-art models on the NL2VIS benchmark nvBench. |
Pluggable Neural Machine Translation Models via Memory-augmented Adapters (2024.lrec-main)
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| Challenge: | Recent years, neural machine translation systems are often developed with large-scale parallel data extracted from the Web. |
| Approach: | They propose a memory-augmented adapter to steer pretrained neural machine translation models in a pluggable manner by combining model representations and retrieved results. |
| Outcome: | The proposed method outperforms several representative pluggable baselines on style- and domain-specific experiments. |
Pointing Out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials (2024.lrec-main)
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| Challenge: | Recent large language models have achieved state-of-the-art performance on many NLP tasks, but they rely on shortcut features and are unreliable when put under pressure. |
| Approach: | They propose to use semantically-motivated strategies to generate adversarial examples by replacing entity mentions to generate relation extraction models. |
| Outcome: | The proposed models show a lack of robustness when put under pressure. |
Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish (2024.lrec-main)
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| Challenge: | Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. |
| Approach: | They propose to use customer opinions of hotels and purchased products in Polish to extract ASTE triplets that contain an aspect, its associated sentiment polarity, and an opinion phrase that serves as a rationale for the assigned polarities. |
| Outcome: | The proposed datasets contain customer opinions about hotels and purchased products expressed in Polish and are available under a permissive licence and have the same file format as the English datasets. |
Polish Discourse Corpus (PDC): Corpus Design, ISO-Compliant Annotation, Data Highlights, and Parser Development (2024.lrec-main)
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Maciej Ogrodniczuk, Aleksandra Tomaszewska, Daniel Ziembicki, Sebastian Żurowski, Ryszard Tuora, Aleksandra Zwierzchowska
| Challenge: | The Polish Discourse Corpus employs ISO 24617-8 for discourse relation annotation. |
| Approach: | They propose to adopt ISO 24617-8 standard for discourse relation annotation for Polish and to develop a parser tailored for the framework. |
| Outcome: | The Polish Discourse Corpus adopts ISO 24617-8, a segment of the Language Resource Management – Semantic Annotation Framework (SemAF) the paper examines the corpus architecture, annotation procedures, and the challenges encountered by annotators. |
PolitiCause: An Annotation Scheme and Corpus for Causality in Political Texts (2024.lrec-main)
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| Challenge: | PolitiCAUSE is a new corpus of political texts annotated for causality . it provides a detailed and robust annotation scheme for analyzing causal information . |
| Approach: | They propose a new corpus of political texts annotated for causality . they provide a detailed and robust annotation scheme for annotating causal information . |
| Outcome: | The proposed method achieves a moderate performance on the dataset, with a MCC score of 0.62. |
PolQA: Polish Question Answering Dataset (2024.lrec-main)
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| Challenge: | Recent proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. |
| Approach: | They propose an efficient annotation strategy that increases passage retrieval accuracy@10 by 10.55 p.p. while reducing the annotation cost by 82%. |
| Outcome: | The proposed approach increases passage retrieval accuracy @10 by 10.55 p.p. while reducing the annotation cost by 82%. |
PolyNERE: A Novel Ontology and Corpus for Named Entity Recognition and Relation Extraction in Polymer Science Domain (2024.lrec-main)
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| Challenge: | a new ontology for polymer-relevant entities and relations is available for training data . the ontologies are customizable to adapt to specific research needs. |
| Approach: | They propose a polymer-relevant ontology featuring crucial entities and relations . the ontologies are customizable to adapt to specific research needs . |
| Outcome: | The proposed ontology can extract polymer-relevant information from scientific papers . it can be customized to adapt to specific research needs . |
PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction (2024.lrec-main)
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| Challenge: | Recent work focuses on generic human responses without considering popularity factors in the social contexts. |
| Approach: | They propose Popularity-Aligned Language Models to distinguish responses liked by a larger audience through reinforcement learning. |
| Outcome: | The proposed model can distinguish responses liked by a larger audience through reinforcement learning. |
PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments (2024.lrec-main)
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| Challenge: | Populism is a phenomenon that is noticeably present in political landscapes worldwide . prior work on populism analysis focused on analyzing populist content expressed by politicians . |
| Approach: | They present a corpus of news comments annotated for populism in the german language . they use machine learning to detect populist comments in text . |
| Outcome: | The proposed corpus outperforms existing dictionaries for populism detection in text . it features 1,200 comments collected between 2019-2021 . |
Positive and Risky Message Assessment for Music Products (2024.lrec-main)
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| Challenge: | a new approach to content assessment is needed to assess positive and potentially harmful messages in music. |
| Approach: | They propose a multi-task predictive model fortified with ordinality-enforcement to assess positive and potentially harmful messages within music products. |
| Outcome: | The proposed method outperforms task-specific alternatives and can assess multiple aspects simultaneously. |
POS Tagging for the Endangered Dagur Language (2024.lrec-main)
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| Challenge: | a recent study has focused on the so-called "dominant" languages, but it has not been inclusive in terms of language equality. |
| Approach: | They propose to use POS tagging to automatically annotate Dagur, an endangered Mongolic language . they use a manually annotated corpus to test transfer of models from other languages . |
| Outcome: | The proposed method can be used to document and revitalize endangered languages . the proposed model can be trained on Buryat, the only Mongolic language included in the corpus . |
Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview (2024.lrec-main)
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| Challenge: | End-to-end (E2E) models are replacing hybrid models for automatic speech recognition tasks. |
| Approach: | They propose a method to optimize E2E models for automatic speech recognition . they propose MED-IT, a multi-turn consultation speech dataset . |
| Outcome: | The proposed method improves on subsets of rare words appearing in training speech. |
PPORTAL_ner: An Annotated Corpus of Portuguese Literary Entities (2024.lrec-main)
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| Challenge: | Annotated corpus of 25 literary texts provides a rich set of annotations for Named Entity Recognition models. |
| Approach: | They propose an annotation dataset that simplifies the development of Named Entity Recognition models for Portuguese literary texts. |
| Outcome: | The proposed dataset simplifies the development of Named Entity Recognition models for Portuguese literary works. |
Predictive and Distinctive Linguistic Features in Schizophrenia-Bipolar Spectrum Disorders (2024.lrec-main)
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| Challenge: | Using this data, we analyze different linguistic features’ predictive power by computing and comparing their frequency distributions. |
| Approach: | They analyze speech transcripts from Hungarian patients with schizophrenia, schizoaffective, and bipolar disorders and compare their linguistic features to identify distinctive linguistic characteristics. |
| Outcome: | The proposed method outperforms baseline methods in distinguishing between schizophrenia, schizoaffective, and bipolar disorders. |
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning (2024.lrec-main)
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| Challenge: | Existing image captioning models require large trainable parameters to bridge visual and textual representations. |
| Approach: | They propose a lightweight image captioning network in combination with continuous diffusion that injects prefix image embeddings into denoising process of diffusion model. |
| Outcome: | The proposed method generates diverse captions with relatively less parameters while maintaining fluency and relevance compared with other models. |
Pre-Trained Language Models Represent Some Geographic Populations Better than Others (2024.lrec-main)
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| Challenge: | Existing studies have focused on measuring the degree to which pre-trained language models capture purely linguistic knowledge and reasoning abilities and world knowledge. |
| Approach: | They use geography to demarcate different populations around the world and comparable corpora to measure how well two families of LLMs perform across these different populations. |
| Outcome: | The results show that pre-trained models perform better for some populations than others. |
Pre-training Cross-Modal Retrieval by Expansive Lexicon-Patch Alignment (2024.lrec-main)
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| Challenge: | Recent large-scale vision-language pre-training relies on image-text global alignment by contrastive learning and is further boosted by fine-grained alignment in a weakly contrastive manner for cross-modal retrieval. |
| Approach: | They propose expansive lexicon-patch alignment (ELA) to align image patches with a vocabulary rather than only the words explicitly in the text for annotation-free alignment and information augmentation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on cross-modal retrieval and can learn representative fine-grained information. |
PRIMO: Progressive Induction for Multi-hop Open Rule Generation (2024.lrec-main)
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| Challenge: | Existing approaches focus on single-hop open rule generation, ignoring scenarios involving multiple hops, leading to logical inconsistencies between premise and hypothesis atoms, as well as semantic duplication of generated rule atom. |
| Approach: | They propose a multi-stage open rule generation method called PRIMO that introduces ontology information during the rule generation stage to reduce ambiguity and improve rule accuracy. |
| Outcome: | The proposed method reduces the repetition rate of rule atoms while preserving the latent knowledge within the model. |
Principal Component Analysis as a Sanity Check for Bayesian Phylolinguistic Reconstruction (2024.lrec-main)
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| Challenge: | Existing methods to reconstruct the evolutionary history of languages rely on the tree model . however, this assumption is violated to varying degrees due to contact and other factors . |
| Approach: | They propose a Bayesian tree model that assumes languages descended from a common ancestor and underwent modifications over time. |
| Outcome: | The proposed method visualizes anomalies in the form of jogging using synthetic and real data. |
Prior Relational Schema Assists Effective Contrastive Learning for Inductive Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing knowledge graphs lack robustness and incompleteness to provide link prediction. |
| Approach: | They propose to capture prior schema-level interactions related to relations by leveraging entity type information and introduce schema-guided negatives to bolster the efficiency of normal contrastive representation learning. |
| Outcome: | The proposed method achieves state-of-the-art performance on multiple established metrics across multiple datasets for link prediction. |
Probe Then Retrieve and Reason: Distilling Probing and Reasoning Capabilities into Smaller Language Models (2024.lrec-main)
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| Challenge: | Recent research efforts have focused on distilling Large Language Models into Small Language Model (SLMs) however, the results of CoT distillation are inadequate for knowledge-intensive reasoning tasks. |
| Approach: | They propose a retrieval-based framework which distills question probing and reasoning capabilities from Large Language Models into SLMs. |
| Outcome: | The proposed framework improves probing and reasoning capabilities of large language models in knowledge-intensive reasoning tasks. |
Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity Pragmatics (2024.lrec-main)
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| Challenge: | Scalar adjectives describe different domain scales and vary in intensity . they can be triggered by scalar adjective and require listeners to reason pragmatically about them. |
| Approach: | They probe different families of Large Language Models for their knowledge of the lexical semantics of scalar adjectives and one specific aspect of their pragmatics. |
| Outcome: | The proposed models encode rich lexical-semantic information about scalar adjectives but lack a good understanding of skalar diversity. |
Probing Multimodal Large Language Models for Global and Local Semantic Representations (2024.lrec-main)
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| Challenge: | Existing studies have focused on the ability of MLLMs to generate single tokens one by one, while lacking studies about how their representation vectors can encode global multimodal information. |
| Approach: | They propose to use image-caption corpus to train Multimodal Large Language Models (MLLMs) . they find that the topmost layers encode more global semantic information . |
| Outcome: | The proposed models can encode more global semantic information, rather than the topmost layers, and perform better on visual-language entailment tasks. |
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search (2024.lrec-main)
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| Challenge: | Existing approaches to code question answering use bi-modal and unimodal pretraining to align text and code representations. |
| Approach: | They propose a modality-agnostic contrastive pre-training approach to improve alignment of text and code representations of current code language models. |
| Outcome: | The proposed model exhibits significant performance improvements across a wide range of code retrieval benchmarks. |
PRODIS - a Speech Database and a Phoneme-based Language Model for the Study of Predictability Effects in Polish (2024.lrec-main)
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| Challenge: | acoustic predictability is operationalised by surprisal in Polish, but cross-linguistic differences depend on prosodic system. |
| Approach: | They present a speech database and a phoneme-level language model of Polish . they aim to study contextual predictability effects on acoustic distinctiveness . |
| Outcome: | The proposed model is the first large, publicly available speech database of Polish . it is based on a light GPT architecture and can be expanded to other languages . |
Producing a Parallel Universal Dependencies Treebank of Ancient Hebrew and Ancient Greek via Cross-Lingual Projection (2024.lrec-main)
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| Challenge: | Using parallel treebanks, syntactic changes can be identified and evaluated in translations, redactions, and commentaries. |
| Approach: | They propose to construct a treebank of Ancient Greek containing portions of the Septuagint by word-aligning and projecting from the parallel Ancient Hebrew text. |
| Outcome: | The proposed treebank contains portions of the Hebrew Scriptures, which are translated into Ancient Greek, and is based on the results of a collaborative effort to create a crosslinguistically consistent treebank annotation scheme. |
Projective Methods for Mitigating Gender Bias in Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Mitigating gender bias in NLP has a long history tied to debiasing static word embeddings. |
| Approach: | They propose a masked language modelling task where content is developed around known social stereotypes and a projective debiasing method is used to reduce bias. |
| Outcome: | The proposed methods reduce intrinsic bias and mitigat observed bias in a downstream setting, but the two outcomes are not necessarily correlated. |
Project MOSLA: Recording Every Moment of Second Language Acquisition (2024.lrec-main)
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| Challenge: | Second language acquisition (SLA) is a complex and dynamic process. |
| Approach: | They created a longitudinal, multimodal, multilingual, and controlled dataset by inviting participants to learn one of three target languages from scratch over a span of two years, exclusively through online instruction. |
| Outcome: | The proposed dataset sheds light on the complex and dynamic nature of the acquisition of a second language and its implications for proficiency assessment, language and speech processing, and multimodal learning analytics. |
PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization (2024.lrec-main)
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| Challenge: | Existing summarization strategies are abstractive and extractive, but are hard to control. |
| Approach: | They propose a PhRase-level cOpying Mechanism that enhances attention on n-grams and calculates an auxiliary loss for the copying prediction. |
| Outcome: | Empirical studies show that PROM improves copying accuracy and faithfulness on benchmarks. |
PromISe: Releasing the Capabilities of LLMs with Prompt Introspective Search (2024.lrec-main)
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| Challenge: | Existing evaluation benchmarks for large language models use uniform manual prompts, resulting in underestimation of performance. |
| Approach: | They propose a prompt introspective search framework that integrates self-introspect and self-refine to unlock the capabilities of LLMs. |
| Outcome: | The proposed framework significantly boosts the performance of 12 well-known LLMs compared to baseline methods. |
Prompt-based Generation of Natural Language Explanations of Synthetic Lethality for Cancer Drug Discovery (2024.lrec-main)
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| Challenge: | Synthetic lethality (SL) is a genetic interaction where a single gene mutation allows cell survival, but simultaneous mutations in two genes lead to cell death. |
| Approach: | They propose a prompt-based pipeline for generating natural language explanations using a dataset derived from New Bing . |
| Outcome: | The proposed pipeline improves on existing biomedical language models in terms of text quality and explainability. |
Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation (2024.lrec-main)
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| Challenge: | Existing approaches to recognize unseen relations for which there are no training instances are lacking in the real-world setting. |
| Approach: | They propose a prompt-based model with semantic knowledge augmentation to recognize unseen relations under zero-shot setting. |
| Outcome: | The proposed model outperforms existing methods under zero-shot setting on three datasets. |
Prompt-fused Framework for Inductive Logical Query Answering (2024.lrec-main)
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| Challenge: | Existing methods for addressing logical queries on knowledge graphs neglect missing edges in KGs . Existing approaches focus on addressing missing edges, thereby neglecting the emergence of new entities . |
| Approach: | They propose a query-aware prompt-fused framework that addresses embedding of emerging entities . they propose to use a symbolic query to gather information relevant to the query . |
| Outcome: | The proposed framework addresses embedding of emerging entities through contextual information aggregation. |
Prompting-based Synthetic Data Generation for Few-Shot Question Answering (2024.lrec-main)
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| Challenge: | Language models have boosted the performance of Question Answering, but data annotation is costly. |
| Approach: | They propose to use large language models to improve Question Answering performance . they argue that domain-agnostic knowledge from LMs is sufficient to create a well-curated dataset. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on few-shot Question Answering. |
Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process (2024.lrec-main)
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| Challenge: | Existing studies have not explored the link between PLMs’ pre-training-based knowledge and input passages. |
| Approach: | They propose a framework that uses prompts to connect explicit and implicit knowledge to elicit type-specific reasoning via prompts, a form of implicit knowledge. |
| Outcome: | The proposed model performs comparable to the state-of-the-art on HotpotQA. |
Prompting for Numerical Sequences: A Case Study on Market Comment Generation (2024.lrec-main)
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| Challenge: | Large language models have been applied to data-to-text generation tasks, but their effectiveness is limited to tasks where the input data is structured and their components are represented as words. |
| Approach: | They propose to use large language models to generate text from numerical sequences. |
| Outcome: | The proposed models perform better than natural languages and longer formats, while resembling natural languages yield less effective results. |
Prompting Large Language Models for Counterfactual Generation: An Empirical Study (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks, but their ability to generate counterfactuals has not been examined systematically. |
| Approach: | They propose a framework to evaluate LLMs' ability to generate counterfactuals based on key factors including intrinsic properties and prompt design. |
| Outcome: | The proposed framework examines the strengths and weaknesses of large language models (LLMs) and identifies factors that influence their ability to generate counterfactuals. |
PromptStream: Self-Supervised News Story Discovery Using Topic-Aware Article Representations (2024.lrec-main)
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| Challenge: | Existing methods for news story discovery relied on sparse document representations such as keywords and TF-IDF vectors. |
| Approach: | They propose a method that constructs article embeddings using cloze-style prompting and self-supervised contrastive learning techniques to tackle this task. |
| Outcome: | The proposed model is able to identify coherent news stories within a news stream and to monitor their progress. |
Prompt Tuning for Few-shot Relation Extraction via Modeling Global and Local Graphs (2024.lrec-main)
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| Challenge: | Recent studies show that prompt-tuning is effective for few-shot relation extraction tasks. |
| Approach: | They propose to incorporate the knowledge in relation labels into prompt-tuning by inserting prompt templates into the input. |
| Outcome: | The proposed method improves on four datasets under low-resource conditions. |
PrOnto: Language Model Evaluations for 859 Languages (2024.lrec-main)
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| Challenge: | Evaluation datasets are scarce for most languages other than English due to high cost of annotation . authors present method for evaluating pretrained language models using evaluation datasets . |
| Approach: | They propose a method which enables any language with a New Testament translation to receive evaluation datasets suitable for pretrained language models. |
| Outcome: | The proposed method can be used in any language with a New Testament translation without manual annotation. |
Prophecy Distillation for Boosting Abstractive Summarization (2024.lrec-main)
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| Challenge: | Abstractive summarization models with maximum likelihood estimation generate unfaithful facts alongside ambiguous focus. |
| Approach: | They propose a framework which learns a regular summarization model to mimic the behavior of being guided by prophecy for boosting abstractive summaries. |
| Outcome: | The proposed model achieves new or matched state-of-the-art on four well-known datasets. |
Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection (2024.lrec-main)
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| Challenge: | Few-shot Event Detection (FSED) requires limited labeled data and expensive manual labeling. |
| Approach: | They propose a prototype-based prompt-instance Interaction with causal Intervention model to utilize both prompts and verbalizers and effectively eliminate all biases. |
| Outcome: | The proposed model utilizes both prompts and verbalizers and eliminates all biases on RAMS and ACE datasets. |
Pruning before Fine-tuning: A Retraining-free Compression Framework for Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Structured pruning is an effective technique for compressing pre-trained language models (PLMs), but it requires retraining, leading to additional computational overhead. |
| Approach: | They propose a task-specific pruning framework that prunes redundant modules of pre-trained language models before fine-tuning them. |
| Outcome: | The proposed pruning framework achieves higher performance on GLUE, SQUAD, WikiText-2, Wik-103, and PTB datasets while reducing the time required for fine-tuning. |
PSentScore: Evaluating Sentiment Polarity in Dialogue Summarization (2024.lrec-main)
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| Challenge: | Existing studies have focused on summarizing factual information, leaving out affective content. |
| Approach: | They propose to quantify the preservation of affective content in dialogue summaries using PSentScore measures. |
| Outcome: | The proposed measures show that state-of-the-art summarization models do not preserve well affective content in their summaries. |
Pseudonymization Categories across Domain Boundaries (2024.lrec-main)
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Maria Irena Szawerna, Simon Dobnik, Therese Lindström Tiedemann, Ricardo Muñoz Sánchez, Xuan-Son Vu, Elena Volodina
| Challenge: | Linguistic data can contain personal information, which is limited in accessibility . a universal system of tags for categorizing PIIs could be developed to replace them . |
| Approach: | They analyze tagsets used for anonymization and pseudonymization to find out what kinds of PII appear in different domains. |
| Outcome: | The proposed system would allow for dynamic pseudonymization while keeping the data readable and useful for future research. |
PSE v1.0: The First Open Access Corpus of Public Service Encounters (2024.lrec-main)
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Ingrid Espinoza, Steffen Frenzel, Laurin Friedrich, Wassiliki Siskou, Steffen Eckhard, Annette Hautli-Janisz
| Challenge: | a dataset of public service encounters in germany provides a new research directive . data from the public service encounters are used to investigate bias, bureaucratic discrimination and other power-driven dynamics in the actual communication . |
| Approach: | They propose to compile a dataset of transcribed public service encounters in germany . they propose to open up the black box of direct state-citizen interaction . |
| Outcome: | The proposed dataset allows the community to open up the black box of direct state-citizen interaction. |
PSYDIAL: Personality-based Synthetic Dialogue Generation Using Large Language Models (2024.lrec-main)
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| Challenge: | a new pipeline for personality-based synthetic dialogues is being developed in Korea . a dataset curated by large language models is needed to generate human-like dialogues . |
| Approach: | They propose a personality-based synthetic dialogue data pipeline to elicit responses from large language models via prompting. |
| Outcome: | The proposed pipeline generates human-like dialogues considering real-world scenarios when users engage with chatbots. |
Puntuguese: A Corpus of Puns in Portuguese with Micro-edits (2024.lrec-main)
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Marcio Lima Inacio, Gabriela Wick-Pedro, Renata Ramisch, Luís Espírito Santo, Xiomara S. Q. Chacon, Roney Santos, Rogério Sousa, Rafael Anchiêta, Hugo Goncalo Oliveira
| Challenge: | Existing corpus of punning humor in Portuguese is unfit for machine learning due to data leakage. |
| Approach: | They propose to use Puntuguese to create a corpus of punning humor in Portuguese that is significantly more difficult to recognize than the previous corpus. |
| Outcome: | The proposed corpus achieves an F1-Score of 68.9% and is significantly more difficult than the previous corpus. |
PWESuite: Phonetic Word Embeddings and Tasks They Facilitate (2024.lrec-main)
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Vilém Zouhar, Kalvin Chang, Chenxuan Cui, Nate B. Carlson, Nathaniel Romney Robinson, Mrinmaya Sachan, David R. Mortensen
| Challenge: | Existing word embedding methods overlook phonetic information that is crucial for many tasks. |
| Approach: | They propose three methods that use articulatory features to build phonetically informed word embeddings. |
| Outcome: | The proposed methods improve word retrieval and correlation with sound similarity and on rhyme and cognate detection tasks. |
PyRater: A Python Toolkit for Annotation Analysis (2024.lrec-main)
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| Challenge: | PyRater is an open-source Python toolkit for analysing corpora annotations. |
| Approach: | They propose to use PyRater to analyse corpora annotations. |
| Outcome: | The proposed model can be used to identify the best annotations and retrieve the gold standard. |
Qabas: An Open-Source Arabic Lexicographic Database (2024.lrec-main)
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| Challenge: | Qabas is an open-source Arabic lexicon designed for NLP applications. |
| Approach: | They propose to link lemmas from 110 lexicons into a morphologically annotated Arabic lexicoma. |
| Outcome: | Qabas lexical entries (lemmas) are assembled by linking lemmas from 110 lexicons. |
QA-based Event Start-Points Ordering for Clinical Temporal Relation Annotation (2024.lrec-main)
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| Challenge: | Temporal relation annotation in the clinical domain is crucial but challenging due to its workload and the medical expertise required. |
| Approach: | They propose an annotation method that integrates event start-points ordering and question-answering as the annotation format. |
| Outcome: | The proposed method achieves a 0.72 F1 score and enables collaboration among medical experts and non-experts. |
QCAW 1.0: Building a Qatari Corpus of Student Argumentative Writing (2024.lrec-main)
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| Challenge: | Existing studies have highlighted the importance of and need to create learner corpora. |
| Approach: | They propose to create a Qatari corpus of argumentative writing (QCAW) the corpus contains 200,000 tokens of argumentation written by Qatari university students . |
| Outcome: | The QCAW contains 195 essays written by 195 students, 159 females and 36 males. |
QDMR-based Planning-and-Solving Prompting for Complex Reasoning Tasks (2024.lrec-main)
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| Challenge: | Existing Plan-and-Solve prompting methods are difficult to implement for complex questions. |
| Approach: | They propose a plan-and-solve prompting method based on Question Decomposition Meaning Representation (QDMR) it allows LLM to generate a QDMR graph to represent problem-solving logic . |
| Outcome: | The proposed method can represent and execute the problem-solving logic of complex questions more accurately than existing methods. |
Qsnail: A Questionnaire Dataset for Sequential Question Generation (2024.lrec-main)
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| Challenge: | Questionnaires are a professional research methodology used for qualitative and quantitative analysis of human opinions, preferences, and behaviors. |
| Approach: | They propose a questionnaire-based dataset that consists of 13,168 human-written questionnaires. |
| Outcome: | The proposed dataset contains 13,168 human-written questionnaires gathered from online platforms. |
Quantifying the Impact of Disfluency on Spoken Content Summarization (2024.lrec-main)
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| Challenge: | a recent study has found that disfluencies negatively impact spoken content summarization . |
| Approach: | They aim to quantify the impact of disfluency on spoken content summarization . they also investigate two methods towards improving summarizing in the presence of disflouencies . |
| Outcome: | The proposed methods improve summarization quality in the presence of disfluencies. |
QUEEREOTYPES: A Multi-Source Italian Corpus of Stereotypes towards LGBTQIA+ Community Members (2024.lrec-main)
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Alessandra Teresa Cignarella, Manuela Sanguinetti, Simona Frenda, Andrea Marra, Cristina Bosco, Valerio Basile
| Challenge: | a dataset of social media texts addressing LGBTQIA+ individuals is presented in this paper . the dataset is based on two sources in italian: Facebook and Twitter . |
| Approach: | They describe a dataset composed of two sub-corpora from two different sources in Italian . the dataset includes social media texts regarding LGBTQIA+ individuals, behaviors, ideology and events . |
| Outcome: | The QUEEREOTYPES dataset includes social media texts regarding LGBTQIA+ individuals, behaviors, ideology and events. |
Query-driven Relevant Paragraph Extraction from Legal Judgments (2024.lrec-main)
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| Challenge: | Legal professionals struggle with navigating lengthy legal judgements to pinpoint information that directly addresses their queries. |
| Approach: | They construct a specialized dataset to extract relevant paragraphs from legal judgements based on query . they assess the performance of current retrieval models in a zero-shot way . |
| Outcome: | The proposed model outperforms the current retrieval models in a zero-shot way and fine-tunes them using various models. |
QueryNER: Segmentation of E-commerce Queries (2024.lrec-main)
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| Challenge: | Prior work on aspect-value extraction has focused on extracting portions of a product title or query for narrowly defined aspects. |
| Approach: | They propose a manually-annotated dataset and model for e-commerce query segmentation. |
| Outcome: | The proposed model can recover from null and low recall queries with token and entity dropping. |
Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) are showing emerging abilities, but they are not large enough to assess their capabilities. |
| Approach: | They propose a benchmark that compares large language models with open and closed source models. |
| Outcome: | The proposed benchmark compares open and closed-source models with open-source and closed source models. |
Quite Good, but Not Enough: Nationality Bias in Large Language Models - a Case Study of ChatGPT (2024.lrec-main)
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| Challenge: | Nationality is a key demographic element that enhances the performance of large language models, but it has received less scrutiny regarding inherent biases. |
| Approach: | They investigated nationality bias in ChatGPT, a large language model for text generation. |
| Outcome: | The proposed model generates 4,680 discourses about nationality in Chinese and English, with 195 countries, 4 temperature settings, and 3 prompt types. |
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)
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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu
| Challenge: | RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts. |
| Approach: | They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts. |
| Outcome: | The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness. |
RADCoT: Retrieval-Augmented Distillation to Specialization Models for Generating Chain-of-Thoughts in Query Expansion (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have demonstrated superior performance to that of small language models in information retrieval for various subtasks including dense retrieval, reranking, query expansion, and pseudo-document generation. |
| Approach: | They propose a retrieval-augmented model specialization that distills the capability of LLMs to generate the chain-of-thoughts (CoT) for query expansion into a RADCoT. |
| Outcome: | The proposed model can generate the chain-of-thoughts (CoT) for query expansion, reducing the burden of internalizing and retaining world knowledge in model parameters. |
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners (2024.lrec-main)
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| Challenge: | Existing solutions to reasoning tasks require extensive human annotations or fail in scenarios with inconsistent responses. |
| Approach: | They propose a new method that enables LLMs to self-rank their responses without additional resources. |
| Outcome: | The proposed method improves reasoning performance of ChatGPT and GPT-4 with 13% improvement over existing methods. |
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have aimed to refine their capacity to accurately follow human instructions and navigate intricate scenarios. |
| Approach: | They propose a method that uses a set of instructions to translate English into Japanese and then generates Japanese instruction data using GPT-4. |
| Outcome: | The proposed method outperforms Japanese-Alpaca models in the evaluation benchmarks without human references. |
Rapidly Piloting Real-time Linguistic Assistance for Simultaneous Interpreters with Untrained Bilingual Surrogates (2024.lrec-main)
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Alvin C. Grissom II, Jo Shoemaker, Benjamin Goldman, Ruikang Shi, Craig Stewart, C. Anton Rytting, Leah Findlater, Jordan Boyd-Graber
| Challenge: | Simultaneous interpretation is a cognitively taxing task, and even seasoned professionals benefit from real-time assistance. |
| Approach: | They propose a simultaneous interpretation task that mimics the cognitive load of interpretation with crowdworker surrogates. |
| Outcome: | The proposed task mimics the cognitive load of interpretation with crowdworker surrogates . the evaluation setup provides consistent results between expert and proxy participants . |
Rationale-based Learning Using Self-Supervised Narrative Events for Text Summarisation of Interactive Digital Narratives (2024.lrec-main)
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| Challenge: | Using rationale-based learning with supervised attention to train text summarisation models on words and sentences surrounding choice points for Interactive Digital Narratives (IDNs) |
| Approach: | They use word-level and sentence-level rationales to focus model training on words and sentences surrounding self-supervised choice points for Interactive Digital Narratives. |
| Outcome: | The proposed model training improves the quality of the summarised text. |
Reading Does Not Equal Reading: Comparing, Simulating and Exploiting Reading Behavior across Populations (2024.lrec-main)
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| Challenge: | Existing corpora of eye-tracking-while-reading corporata lack diversity, limiting their ability to include primarily native speakers. |
| Approach: | They expand the eye-tracking-while-reading dataset CopCo by incorporating a new dataset of L2 readers with diverse L1 backgrounds. |
| Outcome: | The extended CopCo corpus comprises neurotypical L1 and L1 readers with dyslexia as well as L2 readers reading the same materials. |
ReadLet: A Dataset for Oral, Visual and Tactile Text Reading Data of Early and Mature Readers (2024.lrec-main)
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| Challenge: | The paper presents the design and construction of a time-aligned multimodal dataset for reading research, including multiple time-aligned temporal signals elicited with four experimental trials of connected text reading by both child and adult readers. |
| Approach: | They propose to use a time-stamped multimodal dataset to analyze time-aligned temporal signals elicited by connected text reading by both child and adult readers. |
| Outcome: | The proposed dataset includes multiple time-aligned temporal signals elicited with four experimental trials of connected text reading by both child and adult readers. |
Reassessing Semantic Knowledge Encoded in Large Language Models through the Word-in-Context Task (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have propelled significant progress, extending their application across various domains including dialogue systems, text generation, translation systems, and beyond. |
| Approach: | They propose to use the Word-in-Context (WiC) task to reassess the semantic knowledge encoded in large language models (LLMs) they prompt LLMs to generate natural language descriptions that contrast the meanings of the target word in two contextual sentences given in the WiC dataset. |
| Outcome: | The proposed model significantly improves the classification accuracy of the two models. |
Rebalancing Label Distribution While Eliminating Inherent Waiting Time in Multi Label Active Learning Applied to Transformers (2024.lrec-main)
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| Challenge: | Data annotation is a resourceintensive endeavor, necessitating human involvement and expertise. |
| Approach: | They propose to annotate instances to rebalance label distribution by judiciously selecting and limiting the data to be annotated. |
| Outcome: | The proposed method mitigates biases, improves model performance and reduces strategy-dependent disparities. |
ReCAP: Semantic Role Enhanced Caption Generation (2024.lrec-main)
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| Challenge: | Current vision language models lack specificity and overlook various aspects of the image. |
| Approach: | They propose to use semantic roles as control signals to guide captions to specific argument structures by focusing on specific objects and their associated semantic roles instead of general descriptions. |
| Outcome: | The proposed framework produces captions that exhibit enhanced quality, diversity, and controllability. |
Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations (2024.lrec-main)
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| Challenge: | Personalization is a multifaceted process that requires multiple definitions and varies between individuals. |
| Approach: | They propose to systemically survey the recent landscape of personalized dialogue generation including the datasets employed, methodologies developed, and evaluation metrics applied. |
| Outcome: | The proposed model can generate fluent and coherent responses to human queries in a language-based conversational agent. |
RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education (2024.lrec-main)
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| Challenge: | generative AI is expanding in education, yet empirical analyses of large-scale and real-world interactions between students and AI systems remain limited. |
| Approach: | They present a dataset based on a semester-long experiment with 212 college students in English as Foreign Language (EFL) writing courses. |
| Outcome: | The proposed dataset includes conversation logs, students’ intent, students' self-rated satisfaction, and students’ essay edit histories. |
Recognizing Social Cues in Crisis Situations (2024.lrec-main)
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| Challenge: | During natural disasters, observations of other people's behavior can play an essential role in a person's decision-making. |
| Approach: | They propose a task to categorize social cues in tweets during crisis situations using an annotated dataset of 6,000 tweets. |
| Outcome: | The proposed task is challenging for existing systems and a manual task is based on a dataset of 6,000 tweets labeled with eight social cue categories. |
Recognizing Value Resonance with Resonance-Tuned RoBERTa Task Definition, Experimental Validation, and Robust Modeling (2024.lrec-main)
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Noam K. Benkler, Scott Friedman, Sonja Schmer-Galunder, Drisana Marissa Mosaphir, Robert P. Goldman, Ruta Wheelock, Vasanth Sarathy, Pavan Kantharaju, Matthew D. McLure
| Challenge: | Understanding the implicit values and beliefs of diverse groups and cultures using qualitative texts is a fundamental goal of social anthropology. |
| Approach: | They propose to use a novel hand-annotated dataset and a transformer-based model to model the NLP task of Recognizing Value Resonance (RVR) they extend existing work by refining the task definition and releasing the WVC dataset. |
| Outcome: | The proposed models outperform top-performing Recognizing Textual Entailment models in recognizing value resonance and zero-shot GPT-3.5 under several different prompt structures, emphasizing its practical applicability. |
Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines (2024.lrec-main)
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| Challenge: | Existing citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft paper. |
| Approach: | They propose a task of Recommending Missed Citations Identified by Reviewers to help improve citations of full papers. |
| Outcome: | The proposed framework outperforms existing methods in all metrics and will motivate future research on this challenging task. |
Reconstruction of Cuneiform Literary Texts as Text Matching (2024.lrec-main)
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| Challenge: | cuneiform fragment identification is a slow and unsystematic process for reconstructing ancient texts . fragments of cuniform script are often found in fragments written on clay tablets . cnl is able to identify fragments and match them with existing text collections . |
| Approach: | They propose a character-level n-gram-based similarity matching approach to identify fragments . they compare different approaches to identify overlaps between fragments and texts . |
| Outcome: | The proposed approach speeds up the process and reduces the time it takes to complete the work. |
Reduce Redundancy Then Rerank: Enhancing Code Summarization with a Novel Pipeline Framework (2024.lrec-main)
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| Challenge: | Existing code summarization models lack redundant tokens and are plagued by exposure bias. |
| Approach: | They propose a pipeline framework to reduce redundancy then rerank that eliminates redundant information in code representation space and a re-ranking model to select more suitable summary candidates. |
| Outcome: | The proposed framework overrides state-of-the-art approaches on six datasets from the CodeSearchNet benchmark. |
Re-evaluating the Tomes for the Times (2024.lrec-main)
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| Challenge: | Literature is to some degree a snapshot of the time it was written and the societal attitudes of the period. |
| Approach: | They exploit known text co-occurrence metrics to identify problematic descriptors . they propose a method for making explicit such problematic associations . |
| Outcome: | The proposed method could be used by publishing houses, libraries and organisations concerned with social justice to make explicit such problematic associations. |
REFeREE: A REference-FREE Model-Based Metric for Text Simplification (2024.lrec-main)
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| Challenge: | Existing methods for text simplification lack a universal standard of quality and require a small number of human annotations. |
| Approach: | They propose to introduce a reference-free model-based metric with a 3-stage curriculum that can be applied to any quality standard with fewer annotations. |
| Outcome: | The proposed metric outperforms existing reference-based metrics in predicting ratings while requiring no reference simplifications at inference time. |
Reference-guided Style-Consistent Content Transfer (2024.lrec-main)
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| Challenge: | Text style transfer involves changing the style of a text while preserving its original style. |
| Approach: | They propose a task of style-consistent content transfer which involves modifying a text’s content based on a provided reference statement while preserving its original style. |
| Outcome: | The proposed approach meets three important conditions: reference faithfulness, style adherence, and coherence. |
Reference-less Analysis of Context Specificity in Translation with Personalised Language Models (2024.lrec-main)
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Sebastian Vincent, Rowanne Sumner, Alice Dowek, Charlotte Prescott, Emily Preston, Chris Bayliss, Chris Oakley, Carolina Scarton
| Challenge: | Conventional approaches to NLP tasks build models in a one-size-fits-all fashion disregarding the context of the processed text. |
| Approach: | They build LMs which leverage rich contextual information to reduce perplexity by up to 6.5% compared to a non-contextual model. |
| Outcome: | The proposed models reduce perplexity by up to 6.5% compared to non-contextual models and generalise well to a scenario with no speaker-specific data. |
Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention (2024.lrec-main)
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| Challenge: | Existing methods based on deep learning struggle to grasp idiom semantics due to the figurative meanings of many idiomas deviating from their literal interpretations. |
| Approach: | They propose a Chinese idiom cloze test to capture comprehensive idiomatics and a semantic sense contrastive learning module to enhance the representation of idiomics. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Chinese idiom cloze test and on other benchmark datasets. |
Refining rtMRI Landmark-Based Vocal Tract Contour Labels with FCN-Based Smoothing and Point-to-Curve Projection (2024.lrec-main)
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| Challenge: | Existing methods for labeling articulator contours are based on a ground truth label, but there are occasional errors. |
| Approach: | They propose to refine landmark-based vocal-tract contour labels using outlier removal, full convolutional network and a landmark point-to-edge curve projection technique. |
| Outcome: | The proposed labels outperform existing labels and their accuracy through subjective assessments of several contour areas. |
Reflecting the Male Gaze: Quantifying Female Objectification in 19th and 20th Century Novels (2024.lrec-main)
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| Challenge: | a framework for analyzing gender bias in terms of female objectification is proposed . male gaze refers to a phenomenon in which women are depicted as objects of aesthetic pleasure . |
| Approach: | They propose a framework for analyzing gender bias in terms of female objectification . they compute an agency bias score that indicates whether male entities are more likely to appear in the text as grammatical agents than female entities . |
| Outcome: | The proposed framework measures female objectification along two axes. |
Reflections & Resonance: Two-Agent Partnership for Advancing LLM-based Story Annotation (2024.lrec-main)
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| Challenge: | Existing methods for story annotation require a meticulous and resourceintensive effort, but the advent of advanced computational tools like GPT-4 can streamline the process and mitigate common limitations. |
| Approach: | They propose a multi-agent system that generates tailored prompts for a large language model and provides feedback to refine the initial prompts. |
| Outcome: | The proposed system significantly improves the model's reconstruction accuracy and confidence, demonstrating that dynamic interaction between agents significantly boosts the annotation process's precision and efficiency. |
ReflectSumm: A Benchmark for Course Reflection Summarization (2024.lrec-main)
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| Challenge: | Existing research has focused on standard summarization benchmarks within domains like news, scientific articles, and opinions. |
| Approach: | They propose a summarization dataset specifically designed for summarizing students’ reflective writing. |
| Outcome: | The proposed summarization dataset can be used in opinion summarizing scenarios and in educational domains. |
Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders (2024.lrec-main)
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| Challenge: | Existing approaches to intent prediction are limited in highly specialized fields, such as closed-domain dialogue systems, where context comprehension is of paramount importance. |
| Approach: | They propose a method that uses scenario dialog graphs to model dialogues as sequences of transitions between intents, representing distinct goals or requests. |
| Outcome: | The proposed method significantly advances the field of dialogue systems, providing valuable insights into the effectiveness and potential limitations of the proposed approaches. |
Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated impressive capabilities in generating coherent, informative, and fluent verbal reasoning. |
| Approach: | They propose to use fine-grained feedback with reinforcement retrieval to enhance fact-checking on news claims by using black-box LLM. |
| Outcome: | The proposed model improves on two public datasets for fact-checking on news claims using fine-grained feedback with reinforcement retrieval (FFRR). |
Related Work Is All You Need (2024.lrec-main)
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| Challenge: | a corpus of 400k annotations of related work is used to generate a "related work" section . authors and researchers often turn to tools like Google Scholar to find related research for their papers . |
| Approach: | They propose to use a corpus with 400k annotations to generate a "related work" section . they propose to automate the process by using a newly-released corpus that contains human annotations . |
| Outcome: | The proposed technique can be automated by using human annotations of related work sections. |
Relation between Cross-Genre and Cross-Topic Transfer in Dependency Parsing (2024.lrec-main)
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| Challenge: | LAS scores in cross-genre transfer within and across treebanks align with topic distances. |
| Approach: | They propose to use topic modelling to assess whether genre is stable in dependency parsing with respect to topic distribution. |
| Outcome: | The proposed method is highly likely to capture topic similarity, but it is not clear whether it captures only genre features. |
Relation Classification via Bidirectional Prompt Learning with Data Augmentation by Large Language Model (2024.lrec-main)
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| Challenge: | Recent studies investigate Relation Extraction task from two different aspects. |
| Approach: | They propose to use Large Language Model (LLM) to do data augmentation and propose a bidirectional prompt template for prompt learning. |
| Outcome: | The proposed model outperforms the state-of-the-art on four datasets and outperformed existing methods on TACREV, RETACRED and Semeval. |
Release of Pre-Trained Models for the Japanese Language (2024.lrec-main)
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Kei Sawada, Tianyu Zhao, Makoto Shing, Kentaro Mitsui, Akio Kaga, Yukiya Hono, Toshiaki Wakatsuki, Koh Mitsuda
| Challenge: | democratization of AI aims to create a world where everyone can use AI . pre-trained models with high performance in Japanese are lagging in non-English-speaking communities . |
| Approach: | et al. released large-scale pre-trained models trained on large-data to improve access to AI . authors say the models are more accurate and more accurate than those trained in the English language . e-mail protected: email protected. |
| Outcome: | a new study shows that pre-trained models specialized for Japanese can achieve high performance in Japanese tasks. |
Releasing the Capacity of GANs in Non-Autoregressive Image Captioning (2024.lrec-main)
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| Challenge: | Existing non-autoregressive (NAR) models suffer from their inherent multi-modality problem. |
| Approach: | They propose an Adversarial Non-autoregressive Transformer for Image Captioning that improves model performance by modifying model structure to be compatible with contrastive learning. |
| Outcome: | The proposed model achieves 26.72 times faster than the autoregressive model on the MSCOCO dataset. |
RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing methods for temporal knowledge graph embedding do not account for structural dependencies between relations. |
| Approach: | They propose a framework that enhances temporal knowledge graph completion through rule embedding. |
| Outcome: | The proposed framework improves temporal knowledge graph completion through rule embedding. |
Replace, Paraphrase or Fine-tune? Evaluating Automatic Simplification for Medical Texts in Spanish (2024.lrec-main)
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Leonardo Campillos-Llanos, Ana Rosa Terroba, Rocío Bartolomé, Ana Valverde-Mateos, Cristina González, Adrián Capllonch-Carrión, Jonathan Heras
| Challenge: | lexicon-based simplification methods can help patients understand medical documents . but they must ensure that the content is transmitted rigorously and not creating wrong information. |
| Approach: | They tested automatic simplification techniques using a Spanish lexicon of technical and laymen terms. |
| Outcome: | The proposed methods improve the quantitative results and the human evaluation of medical documents. |
Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding (2024.lrec-main)
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| Challenge: | Prompt-based fine-tuning (PF) models have shown improved performance on few-shot natural language understanding benchmarks. |
| Approach: | They propose a framework to alleviate anisotropy in the embedding space by aligning with pre-trained language models' training objective. |
| Outcome: | The proposed method outperforms mainstream methods on many NLU benchmarks. |
Representing Compounding with OntoLex. An Evaluation of Vocabularies for Word Formation Resources (2024.lrec-main)
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| Challenge: | OntoLex is a de facto standard for the modelling of lexical resources in the framework of Linguistic Linked Open Data. |
| Approach: | They propose to use OntoLex to convert Linked Open Data into compounds by using the RDF model. |
| Outcome: | The proposed model can be applied to all resources harmonized in that format, potentially allowing for the conversion into Linked Open Data of a large amount of structured data. |
Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems (2024.lrec-main)
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| Challenge: | End-to-end task-oriented dialogue systems fall into the so-called ‘likelihood trap’, resulting in generated responses which are dull, repetitive, and inconsistent with dialogue history. |
| Approach: | They propose a reranking method to select high-quality items from the initial overgenerated list without the availability of the gold response. |
| Outcome: | The proposed method is based on a multi-woz dataset and human evaluation. |
Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents (2024.lrec-main)
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| Challenge: | Negation scope resolution is a challenging task for NLP because of the complexity of legal texts and lack of annotated in-domain negation corpora. |
| Approach: | They propose to use annotated court decisions to improve negation scope resolution . they release annotations in german, french, and italian to train models without legal data . |
| Outcome: | The proposed models achieve token-level F1-scores of up to 86.7% in zero-shot and multilingual settings. |
Restoring Ancient Ideograph: A Multimodal Multitask Neural Network Approach (2024.lrec-main)
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| Challenge: | despite efforts to preserve cultural relics, many ancient artefacts have fallen prey to ravages of time, natural deterioration, or deliberate human actions. |
| Approach: | They propose a multimodal multitask restoration model that uses visual and context understanding to restore ancient texts. |
| Outcome: | The proposed model predicts damaged characters and generates restored images simultaneously. |
Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models (2024.lrec-main)
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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun
| Challenge: | Pre-trained language models have shown remarkable memory formation, but vanilla networks without pre-training suffer catastrophic forgetting problem. |
| Approach: | They conduct experiments to investigate the retentive-forgetful contradiction between vanilla and pre-trained language models by controlling the target knowledge types, learning strategies and learning schedules. |
| Outcome: | The results show that pre-trained language models are forgetful and pre-training leads to retentive models . |
Rethinking Word-level Adversarial Attack: The Trade-off between Efficiency, Effectiveness, and Imperceptibility (2024.lrec-main)
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| Challenge: | Neural language models have demonstrated impressive performance but remain vulnerable to word-level adversarial attacks. |
| Approach: | They propose two standardized search spaces to address the problem of word-level adversarial attacks. |
| Outcome: | The proposed search spaces improve performance and trade-offs in different scenarios. |
Retrieval-Augmented Modular Prompt Tuning for Low-Resource Data-to-Text Generation (2024.lrec-main)
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| Challenge: | Data-to-text generation methods are often limited by data sparsity and lack of training data. |
| Approach: | They propose a retrieval-augmented modular prompt tuning method that generates texts with few hallucinations from structured data inputs. |
| Outcome: | The proposed method generates texts with few hallucinations and achieves state-of-the-art performance on a dataset for drone handover message generation. |
Retrieval-based Question Answering with Passage Expansion Using a Knowledge Graph (2024.lrec-main)
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| Challenge: | Recent advances in dense neural retrievers and language models have hindered performance, especially for less common entities and facts. |
| Approach: | They propose a multi-modal passage retrieval model that combines entity features and textual data to improve retrieval precision for less common entities. |
| Outcome: | The proposed model improves retrieval precision on less common entities and facts on common benchmarks. |
Revisiting Context Choices for Context-aware Machine Translation (2024.lrec-main)
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| Challenge: | Recent work has cast doubt on whether context-aware machine translation models learn useful signals from context or are improvements in automatic evaluation metrics just a side-effect. |
| Approach: | They propose to use separate encoders for source sentence and context as multiple sources for one target sentence to train context-aware machine translation models. |
| Outcome: | The proposed model improves translation quality even with empty lines as context, but the correct context improves it and random out-of-domain context degrades it. |
Revisiting Data Reconstruction Attacks on Real-world Dataset for Federated Natural Language Understanding (2024.lrec-main)
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Zhuo Zhang, Jintao Huang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Yue Yu, Qifan Wang, Lizhen Qu, Zenglin Xu
| Challenge: | Existing DRA methods fail to accurately recover the original text of real-world privacy data. |
| Approach: | They propose to use a real-world privacy dataset to examine the performance of federated learning (FL) methods. |
| Outcome: | The proposed method improves on a real-world privacy dataset and shows that the tokens within a recovery sentence are disordered and intertwined with tokens from other sentences in the same training batch. |
Revisiting the Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems (2024.lrec-main)
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| Challenge: | This study highlights the pervasive existence of gender stereotypes in literary works and proposes a model with 97% accuracy to identify gender bias. |
| Approach: | They propose a large language model with 97% accuracy to identify gender bias in rhymes and poems and a model with a comparative survey against human educator rectifications. |
| Outcome: | The proposed model has 97% accuracy and can be used to identify gender biases in rhymes and poems. |
Revisiting the Self-Consistency Challenges in Multi-Choice Question Formats for Large Language Model Evaluation (2024.lrec-main)
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| Challenge: | Multi-choice questions (MCQs) are a common method for assessing the world knowledge of large language models. |
| Approach: | They propose three knowledge-equivalent question variants to assess LLMs' world knowledge . they propose option position shuffle, option label replacement, and conversion to a True/False format . |
| Outcome: | The proposed questions are shuffle, label replacement, and True/False format. |
Revisiting Three Text-to-Speech Synthesis Experiments with a Web-Based Audience Response System (2024.lrec-main)
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| Challenge: | Audience Response System (ARS) evaluations are not well understood for text-to-speech synthesis (TTS) evaluation is a key weakness in the field and needs to adapt to be better-suited for this new generation of voices. |
| Approach: | They revisit three published TTS studies and perform an ARS-based evaluation on the stimuli used in each study. |
| Outcome: | The results show that Audience Response System (ARS) is highly useful for evaluating long and continuous stimuli. |
Rewiring the Transformer with Depth-Wise LSTMs (2024.lrec-main)
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| Challenge: | Stacking non-linear layers allows deep neural networks to model complicated functions . but residual connections within each layer fail to fuse information from previous layers effectively . |
| Approach: | They propose a Transformer with depth-wise LSTMs connecting cascading Transformer layers and sub-layers. |
| Outcome: | The proposed model improves in English-German / French and multilingual tasks with BLEU. |
RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation (2024.lrec-main)
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| Challenge: | Existing systems for risk assessment are prone to incorrectly predicting risk severity and have no early detection mechanisms. |
| Approach: | They propose a novel mechanism for accurate early detection of suicide risk by ensembling Hyperbolic Internal Classifiers equipped with an abstention mechanism and early exit inference capabilities. |
| Outcome: | The proposed model abstains from 84% incorrect predictions on Reddit data while out-predicting state of the art models upto 3.5x earlier. |
RoBERTa Low Resource Fine Tuning for Sentiment Analysis in Albanian (2024.lrec-main)
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| Challenge: | Recent advances in the education domain have provided new opportunities for solving interesting, but difficult problems. |
| Approach: | They propose to use EduSenti to fine-tune language models for assigning sentiment to reviews of educators' performance annotated for sentiment, emotion and educational topic. |
| Outcome: | The proposed model is compared with an Albanian masked language trained model from the last XLM-RoBERTa checkpoint and shows that it is a good fit for the proposed model. |
RoboVox: A Single/Multi-channel Far-field Speaker Recognition Benchmark for a Mobile Robot (2024.lrec-main)
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Mohammad Mohammadamini, Driss Matrouf, Michael Rouvier, Jean-Francois Bonastre, Romain Serizel, Theophile Gonos
| Challenge: | In this paper, we introduce a new far-field speaker recognition benchmark called RoboVox. |
| Approach: | They introduce a new far-field speaker recognition benchmark called RoboVox which measures the far-feet of a French corpus recorded by a mobile robot. |
| Outcome: | The proposed benchmarks show a significant decline in far-field speaker recognition and urge the community to further research in this domain. |
Robust and Scalable Model Editing for Large Language Models (2024.lrec-main)
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Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun
| Challenge: | Existing methods that ignore contextual knowledge fail to reliably fall back to parametric knowledge when presented with irrelevant context. |
| Approach: | They propose to use contextual knowledge to update and correct LLMs' knowledge by in-context editing instead of retraining. |
| Outcome: | The proposed method outperforms current state-of-the-art methods by a large margin on a dataset that contains irrelevant questions. |
RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian (2024.lrec-main)
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| Challenge: | Large language models are capable of solving tasks in natural language, but most tests assume they are written in English. |
| Approach: | They propose to use a dataset to measure the generalization power of large language models in a language other than English to evaluate their code intelligence. |
| Outcome: | The proposed dataset provides a benchmark for evaluating the code intelligence of language models trained on Romanian / multilingual text and a fine-tuning set for pretrained Romanian models. |
RoCoIns: Enhancing Robustness of Large Language Models through Code-Style Instructions (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in following human instructions and solving NLU tasks. |
| Approach: | They propose to use code style instructions to replace typically natural language instructions to provide more precise instructions and strengthen the robustness of LLMs. |
| Outcome: | The proposed method outperforms natural language models on eight robustness datasets and achieves an improvement of 5.68% in test set accuracy and a reduction of 5.66 points in Attack Success Rate (ASR). |
RT-VQ2A2: Real Time Vector Quantized Question Answering with ASR (2024.lrec-main)
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| Challenge: | Existing frameworks for QA with large language models are difficult to implement due to noise, limited context length and latency. |
| Approach: | They propose a model-agnostic framework to address problems in QA with large language models. |
| Outcome: | The proposed framework reduces noise in the ASR output and the limited context length of LLMs and improves performance on the widely used Spoken-SQuAD dataset. |
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict (2024.lrec-main)
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| Challenge: | Existing methods to verify factuality of claims do not provide sufficient evidence for explainable fact-checking systems. |
| Approach: | They propose a method to automatically retrieve and summarize evidence from the Web and a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022. |
| Outcome: | The proposed method can retrieve and summarize evidence from the Web and generate explanations in 16 languages. |
RuBia: A Russian Language Bias Detection Dataset (2024.lrec-main)
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| Challenge: | Large language models (LLMs) tend to learn the social and cultural biases present in the raw pre-training data. |
| Approach: | They present a bias detection dataset specifically designed for the Russian language, dubbed RuBia, which is divided into 4 domains: gender, nationality, socio-economic status, and diverse. |
| Outcome: | The proposed dataset is designed to detect bias in the Russian language and is based on 2,000 unique sentence pairs spread over 19 subdomains. |
Russian Learner Corpus: Towards Error-Cause Annotation for L2 Russian (2024.lrec-main)
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Daniil Kosakin, Sergei Obiedkov, Ivan Smirnov, Ekaterina Rakhilina, Anastasia Vyrenkova, Ekaterina Zalivina
| Challenge: | Russian Learner Corpus (RLC) is a large collection of learner texts written by native speakers of over forty languages. |
| Approach: | They propose an automatic error annotation tool that locates and labels errors according to a simplified version of the RLC error-type system. |
| Outcome: | The proposed tool locates and labels errors according to a simplified version of the RLC error-type system. |
S3Prompt: Instructing the Model with Self-calibration, Self-recall and Self-aggregation to Improve In-context Learning (2024.lrec-main)
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| Challenge: | Large language models have limitations in practical applications, such as unsupervised generation and recall of in-context examples. |
| Approach: | They propose a self-calibration, self-recall and self-aggregation prompt pipeline to solve these problems. |
| Outcome: | The proposed pipeline improves the performance of large language models without annotating datasets and model parameter updates. |
SaGE: Evaluating Moral Consistency in Large Language Models (2024.lrec-main)
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| Challenge: | Existing studies on Large Language Models (LLMs) have focused on accuracy but lack universally agreed-upon answers for moral scenarios. |
| Approach: | They propose a measure called Semantic Graph Entropy to measure a model's moral consistency grounded in "Rules of Thumb" they construct a moral Consistency Corpus (MCC) with 50K moral questions and the RoTs they followed to investigate LLM consistency on two popular datasets. |
| Outcome: | The proposed measure measures moral consistency on two popular datasets . |
Saliency-Aware Interpolative Augmentation for Multimodal Financial Prediction (2024.lrec-main)
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Samyak Jain, Parth Chhabra, Atula Tejaswi Neerkaje, Puneet Mathur, Ramit Sawhney, Shivam Agarwal, Preslav Nakov, Sudheer Chava, Dinesh Manocha
| Challenge: | Recent advances in the Financial AI realm have expanded the scope of data and methods they use, such as textual and audio cues from financial earnings calls, but limitations exist. |
| Approach: | They propose a Saliency-guided Hierarchical Mixup augmentation technique for multimodal financial prediction tasks. |
| Outcome: | The proposed technique outperforms state-of-the-art methods by 3-7% on financial earnings and conference call datasets. |
Samayik: A Benchmark and Dataset for English-Sanskrit Translation (2024.lrec-main)
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Ayush Maheshwari, Ashim Gupta, Amrith Krishna, Atul Kumar Singh, Ganesh Ramakrishnan, Anil Kumar Gourishetty, Jitin Singla
| Challenge: | Existing Sanskrit corpora focus on poetry and offer limited coverage of contemporary written materials. |
| Approach: | They release a dataset of 53,000 parallel English-Sanskrit sentences . they use spoken content that covers contemporary world affairs and interpretations . |
| Outcome: | a new dataset of 53,000 parallel English-Sanskrit sentences is released . the dataset outperforms existing models trained on older classical-era poetry datasets . |
SamróMur MilljóN: An ASR Corpus of One Million Verified Read Prompts in Icelandic (2024.lrec-main)
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| Challenge: | samrómur is a crowdsourcing web application designed to collect speech data for the advancement of language technologies in Icelandic. |
| Approach: | They propose to use a crowdsourcing web application to collect and verify Icelandic speech data for automatic speech recognition (ASR) they introduce a dataset comprising one million audio clips from the application . |
| Outcome: | The proposed system can produce high-quality speech data for Icelandic . the proposed system is based on a crowdsourced web application built on Mozilla's Common Voice . |
Sarcasm Detection in a Disaster Context (2024.lrec-main)
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| Challenge: | During natural disasters, people often use social media platforms to express contempt or sarcasm . despite being widely researched as an NLP task, sarkasmatic detection has not been explored in a specific context . |
| Approach: | They propose a dataset of 15,000 tweets annotated for intended sarcasm . they propose sarkasmatic detection using pre-trained language models . |
| Outcome: | The proposed model can obtain as much as 0.70 F1 on the dataset. |
SarcNet: A Multilingual Multimodal Sarcasm Detection Dataset (2024.lrec-main)
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| Challenge: | Sarcasm is an implicit form of sarcasm, involving an intended meaning that contradicts the literal expression . human use conflict between factual information and a statement as cues to detect sarcasm . sarkasmatic analysis is challenging due to its implicit nature . |
| Approach: | They propose a multimodal sarcasm detection dataset that uses multiple modalities to detect sarcasm. |
| Outcome: | The proposed model improves on previous models based on a single label . human sarcasm cannot be detected using a unified label across multiple modalities . |
Scalable Patent Classification with Aggregated Multi-View Ranking (2024.lrec-main)
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| Challenge: | Existing classification-based models struggle with scaling to large numbers of labels and generalizing to new labels. |
| Approach: | They propose a ranking-based method that integrates different views of patents and a meta-model that aggregates and ranks the labels given by the ranking models. |
| Outcome: | The proposed method outperforms the current state-of-the-art methods on two datasets . it can alleviate the limitations and achieve a significant performance improvement . |
Scale-VAE: Preventing Posterior Collapse in Variational Autoencoder (2024.lrec-main)
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| Challenge: | Variational autoencoder (VAE) is a widely used generative model . but when employing strong autoregressive generation networks, VAE tends to converge to a degenerate local optimum known as posterior collapse. |
| Approach: | They propose a model called Scale-VAE to solve a posterior collapse problem . they use a factor to keep the posterior dimension discriminative across data instances . |
| Outcome: | The proposed model outperforms state-of-the-art models in density estimation and representation learning. |
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment (2024.lrec-main)
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| Challenge: | Large language models (LLMs) can reveal toxic or offensive content inadvertently or intentionally. |
| Approach: | They propose to control the diversity of both sides according to the number of samples for fine-tuning, which can directly reflect their impact. |
| Outcome: | The proposed approach improves the performance of large language models after fine-tuning. |
Scansion-based Lyrics Generation (2024.lrec-main)
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| Challenge: | a new method for generating lyrics for Mandarin songs is based on scansion . the number of syllables required is variable due to the number and number of notes . |
| Approach: | They propose a method to generate Mandarin lyrics with a good match between melody and tonal contour. |
| Outcome: | The proposed system outperforms all other systems in lyric-melody fit and uses proxies for quantifying creativity. |
Schema-based Data Augmentation for Event Extraction (2024.lrec-main)
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| Challenge: | Existing data augmentation methods rely on language models to train event extraction models. |
| Approach: | They propose a schema-based data augmentation method that utilizes event schemas to guide the data generation process. |
| Outcome: | The proposed method produces high-quality generated data and significantly improves model performance. |
Schema Learning Corpus: Data and Annotation Focused on Complex Events (2024.lrec-main)
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| Challenge: | The Schema Learning Corpus is a linguistic resource designed to support research into the structure of complex events in multilingual data. |
| Approach: | The Schema Learning Corpus is a linguistic resource that includes large volumes of background data in English, Spanish and Russian. |
| Outcome: | The SLC defines 100 complex events (CEs) across 12 domains and multiple documents labeled for each . multiple documents contain evidence for each step, plus labeles events and relations along with their arguments across a large tag set. |
Schroedinger’s Threshold: When the AUC Doesn’t Predict Accuracy (2024.lrec-main)
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| Challenge: | AUC is a useful tool for evaluating diverse models without calibration. |
| Approach: | They show that the AUC yields an academic notion of accuracy that can misalign with actual accuracy observed in application. |
| Outcome: | The AUC yields an academic and optimistic notion of accuracy that can misalign with actual accuracy observed in application. |
SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions (2024.lrec-main)
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| Challenge: | SciDMT is an enhanced and expanded corpus for scientific mention detection . existing corpora are limited by their small volume and entity linking capabilities . |
| Approach: | They propose to enhance SciDMT, an annotated scientific corpus for scientific mention detection. |
| Outcome: | The proposed corpus is the largest for scientific entity mention detection . it is based on deep learning architectures like SciBERT and GPT-3.5 . |
SciMRC: Multi-perspective Scientific Machine Reading Comprehension (2024.lrec-main)
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| Challenge: | Existing datasets focused on single-perspective question-answer pairs overlooking inherent variation in comprehension levels among different readers. |
| Approach: | They propose a multi-perspective scientific machine reading comprehension dataset . their dataset comprises 741 scientific papers and 6,057 question-answer pairs . |
| Outcome: | The proposed dataset includes questions from beginners, students, and experts. |
SciNews: From Scholarly Complexities to Public Narratives – a Dataset for Scientific News Report Generation (2024.lrec-main)
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| Challenge: | Scientific news reports are a bridge between academic and scientific publications . however, the pursuit of automated news reports faces challenges due to the insufficient availability of parallel corpora. |
| Approach: | They propose to use a corpus of scientific news reports to facilitate this paradigm development . they highlight the divergences in readability and brevity between scientific news narratives and academic manuscripts . |
| Outcome: | The proposed corpus includes academic publications and scientific news reports across nine disciplines. |
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus (2024.lrec-main)
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Stephanie M. Lukin, Claire Bonial, Matthew Marge, Taylor A. Hudson, Cory J. Hayes, Kimberly Pollard, Anthony Baker, Ashley N. Foots, Ron Artstein, Felix Gervits, Mitchell Abrams, Cassidy Henry, Lucia Donatelli, Anton Leuski, Susan G. Hill, David Traum, Clare Voss
| Challenge: | The corpus contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |
| Approach: | They present the Situated Corpus Of Understanding Transactions, a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. |
| Outcome: | The Situated Corpus Of Understanding Transactions (SCOUT) contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning (2024.lrec-main)
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| Challenge: | Existing methods for data augmentation have not been well explored. |
| Approach: | They propose to use punctuation insertion, modal verbs, and double negation to produce diverse forms of sentences. |
| Outcome: | The proposed methods perform better on diverse datasets with semantic similarity and standard negation. |
Searching by Code: A New SearchBySnippet Dataset and SnippeR Retrieval Model for Searching by Code Snippets (2024.lrec-main)
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| Challenge: | Existing code search algorithms use code comments rather than full-text descriptions as text . existing algorithms use a code snippet and/or error traceback to find code . |
| Approach: | They propose a new search-by-code use case using a code snippet and error traceback . they propose implementing the search- by-code query in a StackOverflow dataset . |
| Outcome: | The proposed dataset outperforms strong baselines on SearchBySnippet with 0.451 Recall@10 . a code snippet and/or error traceback are used as queries to find bugs . |
Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data (2024.lrec-main)
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Siyao Peng, Zihang Sun, Huangyan Shan, Marie Kolm, Verena Blaschke, Ekaterina Artemova, Barbara Plank
| Challenge: | Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects. |
| Approach: | They present the first dialectal NER dataset for German, BarNER, with 161K tokens annotated on Bavarian Wikipedia articles and tweets. |
| Outcome: | The proposed dataset improves on bar-wiki and moderately on bartweet with training first on Bavarian . |
Seeing Eye-to-Eye: Cross-Modal Coherence Relations Inform Eye-gaze Patterns During Comprehension & Production (2024.lrec-main)
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| Challenge: | Xu and Stone et al., 2014, show eye movements are correlated with discourse goals but the relationship between eye movements and coherence is a missing link. |
| Approach: | They propose an eye gaze pattern ranking algorithm and a semantic gaze visualization technique to study eye gaze patterns and coherence relations in multimodal language contexts. |
| Outcome: | The proposed method combines eye-tracking and a semantic gaze visualization technique to study eye movements in multimodal language contexts. |
Seeing Is Believing! towards Knowledge-Infused Multi-modal Medical Dialogue Generation (2024.lrec-main)
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Abhisek Tiwari, Shreyangshu Bera, Preeti Verma, Jaithra Varma Manthena, Sriparna Saha, Pushpak Bhattacharyya, Minakshi Dhar, Sarbajeet Tiwari
| Challenge: | Existing models of disease diagnosis using AI do not use knowledge infusion. |
| Approach: | They propose a transformer-based, knowledge-infused multi-modal medical dialogue generation framework . they propose 'discourse-aware' image identifier that recognizes signs and their severity . |
| Outcome: | The proposed model outperforms state-of-the-art models by 7.84% in the english language. |
Segmentation of Complex Question Turns for Argument Mining: A Corpus-based Study in the Financial Domain (2024.lrec-main)
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| Challenge: | Earnings Conference Calls (ECCs) are a favoured domain for the study of argumentation in context and the extraction of Argumentative Discourse Units (ADUs). |
| Approach: | Earnings Conference Calls (ECCs) are favoured domain for study of argumentation in context and extraction of Argumentative Discourse Units (ADUs). |
| Outcome: | ECCs are favoured for study of argumentation in context and extraction of Argumentative Discourse Units (ADUs). |
Select and Reorder: A Novel Approach for Neural Sign Language Production (2024.lrec-main)
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| Challenge: | Sign languages face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets. |
| Approach: | They propose a method that breaks down the translation process into two distinct steps: Gloss Selection (GS) and GlosSelection (GR) they use non-autoregressive decoding to achieve faster inference speeds and reduced computation . |
| Outcome: | The proposed method achieves state-of-the-art BLEU and Rouge scores on the Meine DGS Annotated dataset, demonstrating a substantial improvement of 37.88% in Text to Gloss (T2G) Translation. |
Select High-quality Synthetic QA Pairs to Augment Training Data in MRC under the Reward Guidance of Generative Language Models (2024.lrec-main)
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| Challenge: | Existing approaches focus on downstream metrics to select QA pairs, which lack generalization across different datasets. |
| Approach: | They propose a general selection method that uses a large pre-trained language model as a reward model in a Reinforcement Learning framework for the training of the selection agent. |
| Outcome: | The proposed method improves performance on generative and extractive datasets. |
Selective Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing models cannot abstain from uncertain predictions, which will bring risks in real-world applications. |
| Approach: | They propose to abstain from uncertain future facts by using a confidence estimator . they take both the certainty of the current prediction and the accuracy of historical predictions into account . |
| Outcome: | The proposed abstention mechanism helps existing models make selective predictions instead of indiscriminate ones. |
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have achieved great success in various NLP tasks, but the vast model parameters pose challenges in downstream fine-tuning. |
| Approach: | They propose a task-agnostic prompting strategy that analyzes each dialogue utterance before task execution to enhance LLMs' comprehension in multi-turn dialogues. |
| Outcome: | The proposed strategy outperforms other zero-shot prompts and matches or exceeds efficacy of few-shot ones. |
Self-Improvement Programming for Temporal Knowledge Graph Question Answering (2024.lrec-main)
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| Challenge: | Existing methods implicitly model time constraints by learning time-aware embeddings of questions and candidate answers, which is far from understanding the question comprehensively. |
| Approach: | They propose a temporal-based temporal programming method that leverages the in-context learning ability of Large Language Models to understand combinatory time constraints in questions. |
| Outcome: | The proposed method outperforms existing methods on multiTQ and CronQuestions datasets and is highly efficient on multi-level questions. |
Self-Knowledge Distillation for Knowledge Graph Embedding (2024.lrec-main)
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| Challenge: | Knowledge graph embedding (KGE) is an important task for many downstream applications. |
| Approach: | They propose to use self-knowledge distillation to learn a low-dimensional model from a pre-trained high-dimensional one. |
| Outcome: | The proposed model can improve model performance while maintaining lightweight structure. |
Self-reported Demographics and Discourse Dynamics in a Persuasive Online Forum (2024.lrec-main)
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| Challenge: | Research on language as interactive discourse demonstrates the deliberate use of demographic parameters such as gender, ethnicity, and class to shape social identities. |
| Approach: | They propose to investigate the role and effects of gender self-disclosures on online discourse dynamics by focusing on author gender. |
| Outcome: | The proposed dataset will provide a further impulse for research on the interplay between gender disclosures, community interaction, and persuasion in online discourse. |
Semantic Frame Extraction in Multilingual Olfactory Events (2024.lrec-main)
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| Challenge: | Despite the interest in studying this domain, little effort has been devoted to develop tools and models that can extract olfactory information from large amounts of text in a structured and scalable way. |
| Approach: | They propose a system for multilingual olfactory information extraction covering six European languages, namely English, French, Italian, Dutch, German and Slovene. |
| Outcome: | The proposed system detects olfactory related text adopting a FrameNet-like structure and identifies the lexical units triggering the smell event and a set of frame elements. |
Semantic Map-based Generation of Navigation Instructions (2024.lrec-main)
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| Challenge: | Existing approaches to navigation instruction generation use a sequence of panorama images as visual input. |
| Approach: | They propose a new approach to navigation instruction generation using semantic maps as visual input and frame it as an image captioning task. |
| Outcome: | The proposed model is based on a dataset of a human vision and language navigation task and human subjects are asked to manually assess the quality of the generated instructions. |
Semantic Role Labeling Guided Out-of-distribution Detection (2024.lrec-main)
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Jinan Zou, Maihao Guo, Yu Tian, Yuhao Lin, Haiyao Cao, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi
| Challenge: | Existing methods for identifying domain-shifted instances are prone to OOD and adversarial inputs. |
| Approach: | They propose an unsupervised method that separates, extracts, and learns the semantic role labeling guided out-of-distribution Detection (SRLOOD) they propose a self-supervised approach to enhance global-local feature learning by predicting SRL extracted role. |
| Outcome: | The proposed method achieves SOTA performance on four OOD benchmarks. |
Semantics-Aware Dual Graph Convolutional Networks for Argument Pair Extraction (2024.lrec-main)
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| Challenge: | Argument pair extraction (APE) aims to extract interactive argument pairs from two separate passages. |
| Approach: | They propose to tackle the lexical and semantic relevance of arguments with a pre-trained Rouge-guided Transformer (ROT) by using a word graph and a gating mechanism to fuse two graphs. |
| Outcome: | The proposed approach achieves state-of-the-art on F1 score and significantly improves on existing best alternative. |
Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training (2024.lrec-main)
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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for vision-language pre-training lack high-level semantics and text is not sufficiently involved in masked modeling. |
| Approach: | They propose a semantics-enhanced cross-modal MIM framework for vision-language representation learning that harvests high-level semantics from global image features via self-supervised agreement learning and transfers them to local patch encodings by sharing the encode space. |
| Outcome: | The proposed model achieves state-of-the-art or competitive performance on multiple vision-language tasks. |
Sense of the Day: Short Timeframe Temporal-Aware Word Sense Disambiguation (2024.lrec-main)
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| Challenge: | Existing models that consider the predominant sense of a lemma can be tailored to a specific timeframe and based on the timeframe of the text. |
| Approach: | They use Twitter to explore whether different senses are favoured within specific timeframes and how they are used to create short timeframe temporal-aware word sense disambiguation models. |
| Outcome: | The proposed model outperforms temporal agnostic models and author-aware models. |
SENTA: Sentence Simplification System for Slovene (2024.lrec-main)
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| Challenge: | Sentence simplification involves converting complex sentences into more accessible forms while preserving their meaning and context. |
| Approach: | They propose a system for sentence simplification in Slovene that uses a neural classifier to identify sentences that need simplification and a large Slovenen language model to refine sentences into a simpler form. |
| Outcome: | The proposed system achieves an excellent SARI score of 41 for a large Slovene language model based on T5 architecture . it is integrated into a freely accessible, user-friendly user interface, offering a valuable service to less-fluent Slovenen users. |
SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity (2024.lrec-main)
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| Challenge: | Sentiment-aware pre-trained language models (PLMs) demonstrate impressive results in downstream sentiment analysis tasks, but they neglect to evaluate the quality of constructed sentiment representations. |
| Approach: | They propose a new metric for evaluating the quality of sentiment representations that is based on the degree of equivalence in sentiment polarity between two sentences. |
| Outcome: | The proposed framework outperforms the existing sentiment-aware models in sentiment analysis tasks. |
Sequence Reducible Holdout Loss for Language Model Pretraining (2024.lrec-main)
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| Challenge: | Data selection techniques have shown empirical benefits in reducing the number of gradient steps to train neural models. |
| Approach: | They propose to modify an existing data selection technique to adapt it to the sequence losses typical in language modeling. |
| Outcome: | The proposed technique reduces the number of steps required to train neural models by 4.3% and improves generalization ability on out of domain datasets. |
Sequence-to-Sequence Language Models for Character and Emotion Detection in Dream Narratives (2024.lrec-main)
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| Challenge: | Sigmund Freud's interpretation of dreams has been central to understanding human consciousness for centuries. |
| Approach: | They propose to automate the annotation process by using a natural language framework . they evaluate the impact of model size, prediction order of characters, and consideration of proper names and character traits . |
| Outcome: | The proposed model performs better with a large language model while having 28 times fewer parameters. |
Sequence-to-Sequence Spanish Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Spanish language models have demonstrated proficiency in natural language understanding and generation, but there is a scarcity of encoder-decoder models specifically designed for sequence-to-sequence tasks. |
| Approach: | They propose to implement encoder-decoder architectures pre-trained on Spanish corpora . they use them to assess sequence-to-sequence tasks including summarization, question answering . |
| Outcome: | The proposed models outperform models on sequence-to-sequence tasks in Spanish . the models show that they perform well across all tasks, the authors note . |
Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing methods to learn temporal evolutional representations of entities are hard to capture the complex temporal patterns such as sequential and repetitive. |
| Approach: | They propose a Sequential and Repetitive Pattern Learning method that captures both sequential and repetitive patterns. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four representative benchmarks on GDELT dataset, where performance improvement of MRR reaches up to 18.84%. |
SGCM: Salience-Guided Context Modeling for Question Generation (2024.lrec-main)
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| Challenge: | Identifying relevant sentences to answers is crucial for reasoning the possible questions before generation. |
| Approach: | They propose a salience-guided approach to enhance Paragraph-level Question Generation by identifying salient sentences that manifest relevance. |
| Outcome: | The proposed approach achieves Rouge-L, BLEU4, BERTScore, Q-BLUE-3 and F1-scores compared to baseline on FairytaleQA. |
ShadowSense: A Multi-annotated Dataset for Evaluating Word Sense Induction (2024.lrec-main)
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| Challenge: | Existing word sense induction datasets are annotated by multiple annotators whose inter-annotator agreement is key reliability score . |
| Approach: | They propose a dataset that is annotated by multiple annotators with a key reliability score for evaluation of systems automatically inducing word senses. |
| Outcome: | The proposed dataset shows that it is more reliable than existing paradigms for word sense induction evaluation. |
Sharing the Cost of Success: A Game for Evaluating and Learning Collaborative Multi-Agent Instruction Giving and Following Policies (2024.lrec-main)
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| Challenge: | Recent advances in natural language processing have led to language model-based systems that do a good job at creating natural dialogue behaviour but are often verbose and brittle. |
| Approach: | They propose a game that requires two players to coordinate on vision and language observations. |
| Outcome: | The proposed game achieves high success rates when bootstrapped with heuristic partner behaviors that implement insights from the analysis of human-human interactions. |
SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable Adjectives (2024.lrec-main)
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| Challenge: | scalar implicatures are a phenomenon by which a speaker conveys the negation of a more informative utterance by producing a less informative . |
| Approach: | They propose to use a dataset to investigate the ability of language models to interpret utterances with scalar implicatures. |
| Outcome: | The proposed models perform significantly worse on in-domain and out-of-domain examples than other types of NLI examples. |
SignBLEU: Automatic Evaluation of Multi-channel Sign Language Translation (2024.lrec-main)
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| Challenge: | Sign languages communicate information through the hands, but also facial expressions and upper body movements. |
| Approach: | They propose a task to generate a single sequence of glosses for sign language translation and a new metric to capture multiple signal channels. |
| Outcome: | The proposed metric captures multiple signal channels and correlates with human judgment on a system-level task. |
SilverAlign: MT-Based Silver Data Algorithm for Evaluating Word Alignment (2024.lrec-main)
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| Challenge: | Word alignments are crucial for a variety of NLP tasks. |
| Approach: | They propose a method to automatically create silver data for evaluation of word aligners by exploiting machine translation and minimal pairs. |
| Outcome: | The proposed method correlates with gold benchmarks for 9 language pairs, making it a valid resource for evaluation of different languages and domains when gold data is not available. |
Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering (2024.lrec-main)
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| Challenge: | lexical approaches to find passages have outperformed lexicals due to their superior performance . however, for some languages, such as Polish, few models are available . a recent study shows that neural retrievers are more efficient and efficient than lexica. |
| Approach: | They present a neural retriever for Polish trained on a diverse collection of manual and weakly labeled datasets. |
| Outcome: | The proposed model outperforms lexical retrieval models in Polish on three retrieval tasks. |
SimLex-999 for Dutch (2024.lrec-main)
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| Challenge: | Word embeddings have revolutionised natural language processing by effectively representing words as dense vectors. |
| Approach: | They developed a Dutch variant of the SimLex-999 word similarity dataset by gathering similarity judgements from 235 native Dutch speakers. |
| Outcome: | The proposed model outperforms Bertje and RobBERT in terms of human similarity ratings and better represents semantic similarities between words. |
Sinkhorn Distance Minimization for Knowledge Distillation (2024.lrec-main)
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Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang, Zihan Xu, Tong Wu, Ke Li, Xing Sun, Wengang Zhou, Houqiang Li
| Challenge: | Existing knowledge distillation methods investigate divergence measures but fail to deliver effective supervision when few distribution overlap exists between teacher and student. |
| Approach: | They propose a knowledge distillation method that exploits the Sinkhorn distance to ensure a nuanced assessment of the disparity between teacher and student distributions. |
| Outcome: | The proposed method outperforms state-of-the-art methods on all kinds of LLMs with encoder-only, encoder decoder, and decoded architectures. |
SI-NLI: A Slovene Natural Language Inference Dataset and Its Evaluation (2024.lrec-main)
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| Challenge: | Existing datasets for natural language inference (NLI) are limited to English and a few other well-resourced languages. |
| Approach: | They propose to use a dataset for natural language inference to extend the resources for the task. |
| Outcome: | The proposed dataset is constructed from scratch using knowledgeable annotators with carefully crafted guidelines aiming to avoid common problems in existing datasets. |
SkOTaPA: A Dataset for Skepticism Detection in Online Text after Persuasion Attempt (2024.lrec-main)
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| Challenge: | Persuasion attempts are a form of persuaded behavior that can be observed in various social settings, such as advertising, public health, political campaigns, and personal relationships. |
| Approach: | They propose to use multiple independent human annotations to detect skepticism in response to persuasion attempts on social media influencer marketing. |
| Outcome: | The proposed corpus detects skepticism in response to persuasion attempts on social media influencer marketing using multiple independent human annotations. |
SLaCAD: A Spoken Language Corpus for Early Alzheimer’s Disease Detection (2024.lrec-main)
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Shahla Farzana, Edoardo Stoppa, Alex Leow, Tamar Gollan, Raeanne Moore, David Salmon, Douglas Galasko, Erin Sundermann, Natalie Parde
| Challenge: | Recent studies show that cerebrospinal fluid (CSF) levels serve as useful early biomarkers for identifying early AD, but CSF biomarker collection is challenging. |
| Approach: | They propose to use speech data to identify early Alzheimer's disease (AD) trajectory to identify cognitive deficits in early disease stages. |
| Outcome: | The proposed dataset relates speech and speech characteristics with CSF and plasma biomarkers to clinical diagnoses, CSF levels, and biomarker scores. |
Slot and Intent Detection Resources for Bavarian and Lithuanian: Assessing Translations vs Natural Queries to Digital Assistants (2024.lrec-main)
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| Challenge: | xSID datasets for low-resource languages like English are lacking in translations of high-ressource languages . however, many native speakers of such languages may want to use virtual assistants in their mother tongue . |
| Approach: | They extend a dataset to include two underrepresented languages: Bavarian and Lithuanian . they provide "natural" queries to digital assistants generated by native speakers . |
| Outcome: | The proposed dataset includes two underrepresented languages: Bavarian and Lithuanian . the results show that translated data can produce overly optimistic scores . |
SlovakSum: A Large Scale Slovak Summarization Dataset (2024.lrec-main)
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| Challenge: | Existing datasets with hundreds and thousands of documents are mainly in the English language, but the available data is small or non-existent. |
| Approach: | They propose to use a large Slovak news summarization dataset to evaluate its performance . the dataset contains headlines, short abstracts, and full source text . |
| Outcome: | The proposed dataset is compared with a standard ROUGE metric and a mT5 model to evaluate its performance. |
Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification (2024.lrec-main)
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| Challenge: | Using small language models, we challenge the dominance of large models in text classification by prompting. |
| Approach: | They compare the performance of small and large language models in a zero-shot context using different architectures and scoring functions. |
| Outcome: | The proposed model outperforms large models in a zero-shot context. |
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)
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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin
| Challenge: | Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation. |
| Approach: | They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model. |
| Outcome: | The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks. |
SM-FEEL-BG - the First Bulgarian Datasets and Classifiers for Detecting Feelings, Emotions, and Sentiments of Bulgarian Social Media Text (2024.lrec-main)
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Irina Temnikova, Iva Marinova, Silvia Gargova, Ruslana Margova, Alexander Komarov, Tsvetelina Stefanova, Veneta Kireva, Dimana Vyatrova, Nevena Grigorova, Yordan Mandevski, Stefan Minkov
| Challenge: | SM-FEEL-BG is the first Bulgarian-language package for emotion detection and sentiment analysis. |
| Approach: | They introduce SM-FEEL-BG, a Bulgarian-language package that contains 6 datasets with Social Media (SM) texts with emotion, feeling, and sentiment labels and 4 classifiers trained on them. |
| Outcome: | The proposed package is the first to be released in Bulgarian and is available for free. |
SOBR: A Corpus for Stylometry, Obfuscation, and Bias on Reddit (2024.lrec-main)
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| Challenge: | Existing corpora are limited in scope and can be used to collect data on author attributes. |
| Approach: | They propose to use subreddits, flairs, and self-reports as distant labels for author attributes (age, gender, nationality, personality, and political leaning) . |
| Outcome: | The proposed method could be used to infer author attributes from public posts despite their discreetness and anonymity . |
Social Convos: Capturing Agendas and Emotions on Social Media (2024.lrec-main)
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| Challenge: | Social media traffic can provide valuable insights into prevailing opinions and social dynamics among different segments of the population. |
| Approach: | They propose a method to extract influence indicators from messages circulating among groups . they build upon the concept of a convo to identify influential authors . |
| Outcome: | The proposed approach extracts influence indicators from messages circulating among groups of users discussing particular topics. |
Social Orientation: A New Feature for Dialogue Analysis (2024.lrec-main)
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| Challenge: | Existing studies on social orientations in dialogues show they improve performance in low-resource settings. |
| Approach: | They propose to use social orientation tags to model dialogue outcomes . they introduce a new set of dialogue utterances machine-labeled with social orientation tag. |
| Outcome: | The proposed model improves on English and Chinese language benchmarks and shows that social orientation tags explain the outcomes of social interactions when used in neural models. |
SoftMCL: Soft Momentum Contrastive Learning for Fine-grained Sentiment-aware Pre-training (2024.lrec-main)
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| Challenge: | Existing methods for pre-training language models capture general language understanding but fail to distinguish affective impact of a particular context to a specific word. |
| Approach: | They propose a soft momentum contrastive learning method for fine-grained sentiment-aware pre-training that uses valence ratings as soft-label supervision instead of hard labels. |
| Outcome: | The proposed method improves on four sentiment-related tasks and the results are published online. |
Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning (2024.lrec-main)
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| Challenge: | Existing approaches to learn multi-modal tasks are based on chain-of-thought . however, human thought processes are non-linear and employ dynamic adjustment and updating mechanisms. |
| Approach: | They propose a chain-of-thought technique that adjusts the length of the chain to improve the performance of generated prompts. |
| Outcome: | The proposed model improves multi-modal representation learning in visual, visual, and audio-visual tasks and also has good domain generalization performance due to better reasoning. |
Soft Well-Formed Semantic Parsing with Score-Based Selection (2024.lrec-main)
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| Challenge: | Semantic parsing is the task of translating natural language into a structured, formal semantic representation that can be interpreted by machines. |
| Approach: | They propose a score-based method to select well-formed outputs from candidates generated by beam search algorithms. |
| Outcome: | The proposed method reduces the number of ill-formed outputs and improves F1 scores in English. |
So Hateful! Building a Multi-Label Hate Speech Annotated Arabic Dataset (2024.lrec-main)
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| Challenge: | Social media enables widespread propagation of hate speech targeting groups based on ethnicity, religion, or other characteristics. |
| Approach: | They analyze 70,000 Arabic tweets to identify hate speech patterns and train models . 15% of tweets contain offensive language while 6% have hate speech . authors hope to prevent spread of hateful content on social media platforms . |
| Outcome: | The analysis of 70,000 Arabic tweets shows that 15% of tweets contain offensive language while 6% have hate speech . 10% of tweet provide verifiable factual claims, and 7% are deemed important . |
Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants (2024.lrec-main)
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Chloe Sekkat, Fanny Leroy, Salima Mdhaffar, Blake Perry Smith, Yannick Estève, Joseph Dureau, Alice Coucke
| Challenge: | Recent studies show voice assistants do not perform equally well for everyone . however, research on demographic robustness of speech technologies is still scarce . |
| Approach: | They propose a statistical method to detect demographic bias using a large dataset with controlled demographic tags. |
| Outcome: | The proposed method shows statistically significant differences in performance across age, dialectal region and ethnicity. |
Source-free Domain Adaptation for Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis task is a data mining technique that involves aspect extraction and aspect sentiment classification subtasks. |
| Approach: | They propose a framework that allows model parameter transfer, not data transfer, between different domains. |
| Outcome: | The proposed framework performs competitively with traditional unsupervised domain adaptation methods under privacy conditions. |
SPACE-IDEAS: A Dataset for Salient Information Detection in Space Innovation (2024.lrec-main)
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| Challenge: | Detecting salient parts in text is widely used to mitigate information overload. |
| Approach: | They propose a dataset for salient information detection from space innovation that is manually annotated using a large generative language model. |
| Outcome: | The proposed dataset can be leveraged using multitask learning to train better classifiers. |
Spanish Resource Grammar Version 2023 (2024.lrec-main)
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| Challenge: | Using the Freeling morphological analyzer, we encode a strict notion of grammaticality in the Spanish resource grammar. |
| Approach: | They propose to use the HPSG formalism to encode a Spanish resource grammar with a manually verified treebank of 2,291 sentences. |
| Outcome: | The proposed grammars encode a complex set of hypotheses about syntax and a strict notion of grammaticality making them a resource for natural language processing applications in computer-assisted language learning. |
Spanless Event Annotation for Corpus-Wide Complex Event Understanding (2024.lrec-main)
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| Challenge: | Existing methods for annotating multilingual, multimedia data are limited by the availability of multilingual corpora for schema-based event representation. |
| Approach: | They propose a new approach to event annotation to promote whole-corpus understanding of complex events in multilingual, multimedia data. |
| Outcome: | The proposed method is part of the DARPA Knowledge-directed Artificial Intelligence Reasoning Over Schemas (KAIROS) Program. |
Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks (2024.lrec-main)
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| Challenge: | Descriptive grammars are valuable, but they lack quantitative data. |
| Approach: | They propose to extract and explore significant fine-grained grammar patterns and potential syntactic grammar rules from treebanks to create an easy-to-understand corpus-based grammar. |
| Outcome: | The proposed model captures well-known and less well- known significant grammar rules in Spanish, French, and Wolof. |
Specifying Genericity through Inclusiveness and Abstractness Continuous Scales (2024.lrec-main)
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| Challenge: | Using a pilot study, we created a small but crucial annotated dataset of 324 sentences, demonstrating the framework’s effectiveness in capturing nuanced aspects of genericity. |
| Approach: | They propose a framework for fine-grained modeling of noun phrases' genericity in natural language using a small but crucial annotated dataset of 324 sentences. |
| Outcome: | The proposed framework can be used to model genericity of noun phrases in natural language and can be easily compared with existing binary annotations. |
SpeechAlign: A Framework for Speech Translation Alignment Evaluation (2024.lrec-main)
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| Challenge: | Speech-to-Speech and Speech- to-Text translation are currently dynamic areas of research. |
| Approach: | They propose a framework to evaluate source-target alignment in speech models . they introduce a speech gold alignment dataset and introduce two new metrics . |
| Outcome: | The proposed framework evaluates source-target alignment quality within speech models. |
Speech Analysis of Language Varieties in Italy (2024.lrec-main)
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| Challenge: | Recent advances in self-supervised learning provide new opportunities to analyze Italy’s linguistic varieties using speech data alone. |
| Approach: | They propose to automatically identify the geographic region of origin of speech samples drawn from Italy's diverse language varieties. |
| Outcome: | The proposed model can identify regions from speech recording and improve classification accuracy and yields embeddings that distinctly separate regional varieties. |
Speech Corpus for Korean Children with Autism Spectrum Disorder: Towards Automatic Assessment Systems (2024.lrec-main)
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| Challenge: | Despite the growing demand for digital therapeutics for children with autism spectrum disorder, there is currently no speech corpus for Korean children with ASD. |
| Approach: | They propose to use Korean children with ASD to improve pronunciation and severity evaluation by transcribed speech and language evaluation sessions to assess their articulatory and linguistic characteristics. |
| Outcome: | The proposed corpus will be 300 children with ASD and 50 typically developing (TD) children. |
Speech Recognition Corpus of the Khinalug Language for Documenting Endangered Languages (2024.lrec-main)
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| Challenge: | Existing tools to document endangered languages are limited due to data scarcity and the need for training. |
| Approach: | They propose to use a speech corpus for Khinalug, an endangered language spoken in northern Azerbaijan, to create a model that can be used in language documentation scenarios. |
| Outcome: | The proposed model achieves 6.65 CER points and 25.53 WER points in low-resource scenarios. |
SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels (2024.lrec-main)
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| Challenge: | Existing semantic textual similarity (STS) datasets are not suitable for news similarity detection due to their specificity to a single topic. |
| Approach: | They propose to segment news similarity datasets into topics to improve model training . they propose four different levels of complexity specifically designed for news similarities detection task . |
| Outcome: | The proposed dataset includes seven topics: Crime & Law, Culture & Entertainment, Disasters & Accidents, Economy & Business, Politics / Conflicts, Science & Technology, Sports. |
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution (2024.lrec-main)
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| Challenge: | Existing attempts to integrate singleton mention detection into end-to-end coreference resolution for English have been hampered by the lack of singletont mention spans in the OntoNotes benchmark. |
| Approach: | They propose a two-step neural mention and coreference resolution system that integrates singleton mentions with OntoNotes syntax trees to achieve a near approximation of the Ontonotes dataset with all singletont mentions. |
| Outcome: | The proposed system achieves 94% recall on a sample of gold singletons. |
SPOTTER: A Framework for Investigating Convention Formation in a Visually Grounded Human-Robot Reference Task (2024.lrec-main)
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| Challenge: | Existing research has shown that conventions arise in repeated interactions over the same task, leading to a decrease in utterance length while maintaining informative content. |
| Approach: | They propose to elicit conventions for members of an inner circle of well-known individuals in common ground, as opposed to individuals from an outer circle, who are unfamiliar. |
| Outcome: | The proposed game platform elicits conventions for familiar and unfamiliar individuals in human-robot interaction. |
SpreadNaLa: A Naturalistic Code Generation Evaluation Dataset of Spreadsheet Formulas (2024.lrec-main)
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| Challenge: | Existing datasets primarily target the use of code generation models to aid expert programmers in writing code. |
| Approach: | They propose a natural language code generation model that can translate English descriptions to spreadsheet formulas that can be used to do everyday data processing tasks. |
| Outcome: | The proposed model performs best among the evaluated methods but generates formulas that differ from human-generated ones. |
STAF: Pushing the Boundaries of Test-Time Adaptation towards Practical Noise Scenarios (2024.lrec-main)
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| Challenge: | Pre-trained language models have demonstrated superior performance on NLP tasks . however, when the training domain and testing domain are taken from different distributions, the deployed model often violates this assumption. |
| Approach: | They propose a Stable Test-time Adaptation Framework to stabilize the adaptation process. |
| Outcome: | The proposed framework boosts model robustness to noise distribution shifts while minimizing error accumulation and catastrophic forgetting. |
STAGE: Simple Text Data Augmentation by Graph Exploration (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) are widely used for various tasks, but fine-tuning them requires sufficient data. |
| Approach: | They propose a method for data augmentation that utilizes a word-relation graph to select optimal words for each modification. |
| Outcome: | The proposed method is highly effective across diverse datasets and different PLMs. |
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning (2024.lrec-main)
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| Challenge: | Stance Reasoner is a model for zero-shot stance detection on social media platforms that can be used to extract opinions from opinionated content. |
| Approach: | They propose a method that leverages explicit reasoning over background knowledge to guide the model’s inference about the document’s stance on a target. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on 3 Twitter datasets, including fully supervised models. |
STEntConv: Predicting Disagreement between Reddit Users with Stance Detection and a Signed Graph Convolutional Network (2024.lrec-main)
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| Challenge: | Existing methods to detect disagreements on social media platforms have focused on supplementing textual information with user network information, such as Twitter's following system, retweets and hashtags. |
| Approach: | They propose a method which builds a graph of users and named entities and trains a Signed Graph Convolutional Network to detect disagreement between comment and reply posts. |
| Outcome: | The proposed model builds a graph of users and named entities weighted by stance and trains a Signed Graph Convolutional Network (SGCN) to detect disagreement between comment and reply posts. |
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for autoregressive text generation have low controllability and accumulating errors. |
| Approach: | They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy. |
| Outcome: | Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance. |
Step Feasibility-Aware and Error-Correctable Entailment Tree Generation (2024.lrec-main)
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| Challenge: | Existing methods for generating entailment trees suffer from false feasible steps, resulting in error propagation. |
| Approach: | They propose an iterative entailment tree generation framework with step feasibility perception and state error handling mechanisms to enhance the interpretability of QA systems. |
| Outcome: | The proposed framework improves the interpretation of QA systems by demonstrating that it is feasible to choose steps that are false feasible and error propagating. |
Still All Greeklish to Me: Greeklish to Greek Transliteration (2024.lrec-main)
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| Challenge: | Greeklish is a writing form that is used to avoid switching languages on multilingual keyboards . even native Greek speakers may struggle to understand Greeklished . |
| Approach: | They propose to use Greeklish to avoid switching languages on multilingual keyboards . they propose to train models on Greek datasets using the Greek alphabet . |
| Outcome: | The proposed model outperforms existing models on Greeklish data. |
Stories and Personal Experiences in the COVID-19 Discourse (2024.lrec-main)
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| Challenge: | 'storytelling' is a human strategy to use personal experiences to back-up one's position in debates about controversial topics. |
| Approach: | They analyse the use of storytelling in the COVID-19 discourse by automatically annotating three publicly available Reddit datasets for a total of 367K comments. |
| Outcome: | The proposed analysis on three publicly available Reddit datasets shows that storytelling is a powerful argumentative tool. |
Strengthening the WiC: New Polysemy Dataset in Hindi and Lack of Cross Lingual Transfer (2024.lrec-main)
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| Challenge: | a new study addresses the problem of natural language processing in low-resource languages such as Hindi . the paper focuses on Word Sense Disambiguation, a fundamental NLP task that deals with polysemous words. |
| Approach: | They propose a Hindi WSD dataset that allows training and testing of contextualized models. |
| Outcome: | The proposed dataset enables training and testing of contextualized models in Hindi . the results show that the proposed dataset can handle polysemy tasks in low-resource languages . |
StructAM: Enhancing Address Matching through Semantic Understanding of Structure-aware Information (2024.lrec-main)
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| Challenge: | Existing approaches to address matching rely on string-based similarity matching or manually-designed rules. |
| Approach: | They propose a method to match unstructured addresses to standard ones in a database using pre-trained language models and graph neural networks. |
| Outcome: | The proposed method outperforms state-of-the-art methods on real-world addresses . it incorporates spatial coordinates and contextual information from the surrounding area as auxiliary guidance. |
Structure-aware Fine-tuning for Code Pre-trained Models (2024.lrec-main)
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| Challenge: | Existing CodePTMs are mainly structure-free and structurebased, but how to fine-tune them remains a challenge. |
| Approach: | They propose a plug-and-play fine-tuning method that incorporates structural knowledge into pre-trained code models. |
| Outcome: | The proposed method can benefit CodePTMs more with limited training data. |
Structure-aware Generation Model for Cross-Domain Aspect-based Sentiment Classification (2024.lrec-main)
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| Challenge: | Existing generation models for cross-domain aspect-based sentiment classification ignore syntactic structures . syntaktic structures are pre-trained on natural language and can be catastrophic forgetting of distributional knowledge. |
| Approach: | They propose a structure-aware generation model that explicitly encodes syntactic structure into the model. |
| Outcome: | The proposed model can learn domain-irrelevant features based on syntactic pivot features. |
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer (2024.lrec-main)
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| Challenge: | Existing methods to separate content from style but some words contain both content and style information. |
| Approach: | They propose a method which uses a reversible encoder to improve content disentanglement. |
| Outcome: | The proposed method outperforms baselines on sentiment transfer and formality transfer tasks. |
Submodular-based In-context Example Selection for LLMs-based Machine Translation (2024.lrec-main)
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| Challenge: | Prior studies have focused on the role of well-chosen examples in in-context learning . |
| Approach: | They propose to use multiple translational factors for in-context example selection by using monotone submodular function maximization. |
| Outcome: | The proposed approach outperforms random selection and robust single-factor baselines across various NLP tasks. |
Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals (2024.lrec-main)
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| Challenge: | Existing models that extract discrete inputs into fixed-length representations are vulnerable to adversarial attacks that place perturbations on clean inputs to fool DNNs. |
| Approach: | They propose to inspect the subspaces of sample features through spectral analysis to better understand adversarial attacks. |
| Outcome: | The proposed strategy enables the model to inherently suppress adversaries, which boosts model robustness and motivates new directions of effective adversarial defense. |
Sub-Table Rescorer for Table Question Answering (2024.lrec-main)
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| Challenge: | Tabular language models truncate the sequence of a long table due to their input token limits. |
| Approach: | They propose a sub-table rescorer to improve the performance of an inner table retriever-based inference. |
| Outcome: | The proposed sub-table rescorer improves the performance of an ITR-based inference. |
SUK 1.0: A New Training Corpus for Linguistic Annotation of Modern Standard Slovene (2024.lrec-main)
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Špela Arhar Holdt, Jaka Čibej, Kaja Dobrovoljc, Tomaž Erjavec, Polona Gantar, Simon Krek, Tina Munda, Nejc Robida, Luka Terčon, Slavko Zitnik
| Challenge: | a training corpus for linguistic annotation of modern standard Slovene has been in continuous development for 15 years. |
| Approach: | They introduce an upgrade of a training corpus for linguistic annotation of modern standard Slovene. |
| Outcome: | The revised corpus, built on its predecessor, doubles in size and depth of annotation layers. |
SuperST: Superficial Self-Training for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | In few-shot text classification, self-training relies on pseudo-labels to expand data, which has shown success, but can accumulate errors due to noisy pseudo-labeled data. |
| Approach: | They propose a method to mitigate noise in noisy pseudo-labeled data by applying superficial learning to noisy data and fine-tuning to less noisy data. |
| Outcome: | The proposed framework improves the classifier accuracy for few-shot text classification by 18.5% at most and 8% in average, compared with the state-of-the-art SSL baselines. |
SwissSLi: The Multi-parallel Sign Language Corpus for Switzerland (2024.lrec-main)
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| Challenge: | Using a CC BY-NC-SA 4.0 license, this corpus contains parallel sign language videos and spoken language subtitles. |
| Approach: | They introduce SwissSLi, the first sign language corpus that contains parallel data of all three Swiss sign languages. |
| Outcome: | The proposed corpus contains parallel sign language videos and spoken language subtitles. |
Synergetic Interaction Network with Cross-task Attention for Joint Relational Triple Extraction (2024.lrec-main)
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| Challenge: | Existing approaches to joint entity-relation extraction are limited in their ability to capture the interdependence between the two sub-tasks. |
| Approach: | They propose a synergistic approach to capture interdependence between named entity recognition and relation extraction sub-tasks in a Synergetic Interaction Network. |
| Outcome: | The proposed model achieves significantly better performance on three benchmark datasets. |
SynPrompt: Syntax-aware Enhanced Prompt Engineering for Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods of prompt-tuning for Aspect-based Sentiment Analysis (ABSA) are crude and simple. |
| Approach: | They propose a Syntax-aware Enhanced Prompt method which mines syntactic information related to aspect words from the syntaktic dependency tree. |
| Outcome: | The proposed method exploits the syntactic knowledge embedded in PLMs and achieves favorable results on three benchmark datasets. |
Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation (2024.lrec-main)
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| Challenge: | a number of languages are used in online conversations, resulting in code-mixing . the problem is largely unexplored due to the lack of annotated data and noise . |
| Approach: | They propose a robust perturbation-based joint-training model that learns to handle noise in code-mixed text by parameter sharing across clean and noisy words. |
| Outcome: | The proposed model learns to handle noise in the real-world code-mixed text by parameter sharing across clean and noisy words. |
SynTOD: Augmented Response Synthesis for Robust End-to-End Task-Oriented Dialogue System (2024.lrec-main)
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| Challenge: | Task-oriented dialogue systems focus on training multiple tasks such as language understanding, tracking states, and generating appropriate responses to help users achieve their specific goals. |
| Approach: | They exploit the ability of pre-trained models to provide synthesis responses for fine-tuning end-to-end TOD systems. |
| Outcome: | The proposed model outperforms baseline models on multiwoz datasets and is available for further exploitation. |
Tackling Long Code Search with Splitting, Encoding, and Aggregating (2024.lrec-main)
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| Challenge: | Existing pretraining models take the first 256 tokens of code snippets by default, limiting the input length to 512. |
| Approach: | They propose a baseline SEA model which splits long code into code blocks and aggregates them to obtain a comprehensive long code representation. |
| Outcome: | The proposed model can model long code without changing their internal structure and re-pretraining. |
TacoERE: Cluster-aware Compression for Event Relation Extraction (2024.lrec-main)
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| Challenge: | Existing work on event relation extraction focuses on modeling the entire document . existing methods cannot handle long-range dependencies and information redundancy . |
| Approach: | They propose a compression-then-extraction paradigm for event relation extraction . they propose document clustering for modeling event dependencies and then a cluster summarization method . |
| Outcome: | The proposed method simplifies and highlights important text content of clusters for mitigating redundancy and event distance. |
TACO – Twitter Arguments from COnversations (2024.lrec-main)
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| Challenge: | Argument mining aims to identify the structural elements of arguments, denoted as information and inference, in online discourses. |
| Approach: | They propose to use Twitter Arguments to identify structural elements of arguments, denoted as information and inference, in a dataset that uses 1,814 tweets and an annotation framework that incorporates definitions from the Cambridge Dictionary to define and identify argument components. |
| Outcome: | The proposed dataset identifies arguments on Twitter and achieves an 85.06% macro F1 score in detecting arguments. |
TAeKD: Teacher Assistant Enhanced Knowledge Distillation for Closed-Source Multilingual Neural Machine Translation (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have produced impressive results in the field of Multilingual Neural Machine Translation (MNMT). |
| Approach: | They propose a Teacher Assistant enhanced Knowledge Distillation method to augment knowledge transfer capacity from closed-source MNMT models. |
| Outcome: | The proposed method outperforms the state-of-the-art KD methods on both WMT22 and FLORES-101 test sets. |
TaiChi: Improving the Robustness of NLP Models by Seeking Common Ground While Reserving Differences (2024.lrec-main)
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| Challenge: | Pre-trained Language Models are vulnerable to adversarial examples that introduce human-imperceptible perturbations to clean examples to deceive the models. |
| Approach: | They propose a Siamese network-based approach to teach adversarial models to focus on similarities . they propose combining two sub-networks sharing the same structure but trained on clean and adversarials . |
| Outcome: | The proposed approach reduces the differences between clean and adversarial samples and focuses more on similarities. |
Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction (2024.lrec-main)
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| Challenge: | Recent research shows that pre-trained language models suffer from “prompt bias” in factual knowledge extraction. |
| Approach: | They propose a representation-based approach to mitigate prompt bias during inference time by querying the model and removing it from its internal representations to generate debiased representations. |
| Outcome: | The proposed approach corrects the overfitted performance caused by prompt bias and significantly improves prompt retrieval capability. |
Take Its Essence, Discard Its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect (2024.lrec-main)
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| Challenge: | Existing methods to mitigate lexical bias in toxic language detection (TLD) do not exploit the “useful” and “misleading” impact of the bias. |
| Approach: | They propose a counterfactual Causal Debiasing Framework to mitigate lexical bias in toxic language detection (TLD) it preserves the “useful impact” of lexical bias and eliminates the "misleading impact" they propose to use the same framework to analyze the causal effect of a sentence and bias tokens . |
| Outcome: | The proposed framework preserves the “useful impact” of lexical bias and eliminates the ‘misleading impact’ Empirical evaluations show that the proposed model outperforms current debiased models for out-of-distribution data. |
TAPASGO: Transfer Learning towards a German-Language Tabular Question Answering Model (2024.lrec-main)
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| Challenge: | This paper examines the performance and limitations of fine-tuned models for tabular data analysis. |
| Approach: | They propose to use fine-tuning mechanics to fine- tune an English model towards a potential German model for tabular data analysis. |
| Outcome: | The proposed model outperforms the original model on German training data. |
Target-Adaptive Consistency Enhanced Prompt-Tuning for Multi-Domain Stance Detection (2024.lrec-main)
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| Challenge: | Stance detection is a fundamental task in natural language processing, but it is challenging due to diverse expressions and topics related to the targets from multiple domains. |
| Approach: | They propose a prompt-tuning method that incorporates target knowledge and prior knowledge to construct target-adaptive verbalizers for diverse domains. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on nine stance detection datasets from multiple domains. |
Targeted Syntactic Evaluation on the Chomsky Hierarchy (2024.lrec-main)
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| Challenge: | a novel evaluation paradigm for targeted syntactic evaluations is proposed . we create formal languages that abstract four syntaktic phenomena in natural languages . |
| Approach: | They propose a new evaluation paradigm for Targeted Syntactic Evaluations . they create formal languages that abstract syntactical phenomena in natural languages . |
| Outcome: | The proposed evaluation paradigm evaluates language models on language modeling tasks . it shows that they can capture the structural patterns of the (Adj)n NP type formal language . |
TARIC-SLU: A Tunisian Benchmark Dataset for Spoken Language Understanding (2024.lrec-main)
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| Challenge: | Existing SLU resources are limited in high-resource languages such as English, Mandarin and French. |
| Approach: | They propose to use a Tunisian dialect dataset to build a semantic model of the system that is continuously annotated with dialogue acts and slots. |
| Outcome: | The proposed dataset is based on train-based and ASR-based models of train-driven conversations in Tunisian dialect. |
TARN-VIST: Topic Aware Reinforcement Network for Visual Storytelling (2024.lrec-main)
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| Challenge: | Existing methods for visual storytelling ignore latent topic information. |
| Approach: | They propose a topic-aware reinforcement network for VIsual StoryTelling that takes topic information into account to generate a coherent story. |
| Outcome: | The proposed method outperforms most of the competing models across multiple evaluation metrics. |
Task-agnostic Distillation of Encoder-Decoder Language Models (2024.lrec-main)
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| Challenge: | Existing distillation methods that focus on encoder-only LMs fail to handle the distillation of encoder decoder LM. |
| Approach: | They propose a method that finetunes pretrained language models (LMs) they propose 'MiniEnD' that allows for task-agnostic distillation of LMs. |
| Outcome: | The proposed distillation method is generally effective and competitive compared to other alternatives. |
Task-Oriented Paraphrase Analytics (2024.lrec-main)
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| Challenge: | Existing studies on paraphrasing have applied different criteria to the task . authors have previously unmasked related tasks as paraphrases . |
| Approach: | They propose a taxonomy to organize 25 identified paraphrasing tasks . authors propose to use classifiers to identify tasks that a given paraphrased instance fits . |
| Outcome: | The proposed taxonomy identifies 25 paraphrasing tasks that fit the proposed task. |
tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework (2024.lrec-main)
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| Challenge: | Several initiatives release harmonized datasets or provide harmonization codes to preprocess datasets into a consistent format. |
| Approach: | They propose an annotation framework that enables concise, readable, and reusable annotations. |
| Outcome: | The proposed framework outperforms all publicly available text encoders on all tasks. |
Teaching Large Language Models to Translate on Low-resource Languages with Textbook Prompting (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive results in Machine Translation by following instructions, even without training on parallel data. |
| Approach: | They propose a Translate After LEarNing Textbook approach which aims to enhance LLMs’ ability to translate low-resource languages by learning from a textbook. |
| Outcome: | The proposed approach improves translation performance by 14.8% using 112 low-resource languages from FLORES-200 with two LLMs: ChatGPT and BLOOMZ. |
TECA: A Two-stage Approach with Controllable Attention Soft Prompt for Few-shot Nested Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing methods for few-shot nested named entity recognition (NER) ignore relationship between inner and outer entities, which is crucial for fewshot ner. |
| Approach: | They propose a span-based method with a controllable attention soft prompt for few-shot nested named entity recognition (TECA) the span part identification provides possible entity mentions without an extra filtering module. |
| Outcome: | The proposed method outperforms baseline models on four benchmark datasets and outperformed competing models on F1-score by 5.62% on ACE04, 5.11% on ace05, 3.41% on KBP2017 and 0.7% on GENIA on the 10-shot setting. |
TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu (2024.lrec-main)
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| Challenge: | Relevance-based headline classification is under-explored in low-resource languages like Telugu due to a lack of annotated data. |
| Approach: | They propose that relevance-based headline classification can greatly aid the task of generating relevant headlines. |
| Outcome: | The proposed model can generate relevant headlines with 78,534 annotations in Telugu . the model shows a 5 point increment in the ROUGE-L scores . |
TED-EL: A Corpus for Speech Entity Linking (2024.lrec-main)
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| Challenge: | Current entity linking tasks rely on textual information, but entities usually exist in textual, audio, and visual contexts in real-world data such as social media and video websites. |
| Approach: | They propose a speech entity linking task to recognize mentions from speech and link them to entities in knowledge bases. |
| Outcome: | The proposed model outperforms the existing models on the TED-EL dataset, scoring an F1 score of 60.68%. |
Tell Me Again! a Large-Scale Dataset of Multiple Summaries for the Same Story (2024.lrec-main)
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| Challenge: | Existing approaches to represent narratives on short-form texts are limited as narrative semantics are an open class. |
| Approach: | They propose to use Wikipedia summaries as a proxy for entire stories or for analysis of the summary itself. |
| Outcome: | The proposed dataset contains 96,831 individual summaries across 29,505 stories. |
Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding (2024.lrec-main)
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| Challenge: | Existing models that model temporal dynamics with knowledge graphs and graph convolution networks lack high-order interactions between objects in TKG, which is an important factor to predict future facts. |
| Approach: | They propose to embed temporal knowledge graph reasoning by constructing hypergraphs based on temporal information graphs at different timestamps and then adapt dynamic meta-embedding to fit TKG. |
| Outcome: | The proposed method outperforms baseline models on public TKG datasets and provides good interpretation for the predicted results. |
Term-Driven Forward-Looking Claim Synthesis in Earnings Calls (2024.lrec-main)
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| Challenge: | Existing arguments synthesis models excel in summarizing arguments, but lack accurate forward-looking perspectives. |
| Approach: | They propose a task called "forward-looking claim planning" that incorporates forward-looking perspectives. |
| Outcome: | The proposed method improves the existing models and improves performance. |
text2story: A Python Toolkit to Extract and Visualize Story Components of Narrative Text (2024.lrec-main)
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| Challenge: | Story components, namely events, time, participants, and their relations, are present in narrative texts from different domains such as journalism, medicine, finance, and law. |
| Approach: | They propose to use an array of narrative extraction tools to extract narratives from text . the package contains an array and an experimental module for evaluation . |
| Outcome: | The text2story python supports the narrative extraction and visualization pipeline. |
Text2Story Lusa: A Dataset for Narrative Analysis in European Portuguese News Articles (2024.lrec-main)
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Sérgio Nunes, Alípio Mario Jorge, Evelin Amorim, Hugo Sousa, António Leal, Purificação Moura Silvano, Inês Cantante, Ricardo Campos
| Challenge: | Access to annotated corpora with narrative elements is limited due to the lack of readily available datasets and copyright concerns. |
| Approach: | They developed a dataset that contains 357 news articles and 117 manually annotated articles with over 50 thousand individual annotations. |
| Outcome: | The proposed datasets are available in English and Portuguese and are based on 117 articles totaling over 50 thousand individual annotations. |
Text360Nav: 360-Degree Image Captioning Dataset for Urban Pedestrians Navigation (2024.lrec-main)
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| Challenge: | Existing image captioning datasets focus on the overall image description and lack detailed scene descriptions, overlooking features for pedestrians walking on urban streets. |
| Approach: | They develop a dataset to provide textual feedback from 360-degree camera images to visually impaired pedestrians . they generate meaningful captions focusing on obstacles on the streets . |
| Outcome: | The proposed dataset provides textual feedback from machinery visual perception to visually impaired individuals and distracted pedestrians . the results show that the models trained with the dataset can generate meaningful captions focusing on street objects and obstacles in urban scenes . |
Text Filtering Classifiers for Medium-Resource Languages (2024.lrec-main)
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| Challenge: | linguistic and NLP researchers use web-crawled corpora to filter low-quality texts . early Transformer-based language models were typically pre-trained on curated corporum . |
| Approach: | They compare the effectiveness of various text filtering classifiers on Icelandic, Estonian and Basque texts . they use a perplexity-based classifier and a self-supervised classifier trained on TQ-IS to discern between documents from curated and web-crawled corpora. |
| Outcome: | The proposed classifiers achieve F1 scores of 94.48%, 99.01% and 93.40% on the Icelandic, Estonian and Basque datasets. |
Text Style Transfer Evaluation Using Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated their capacity to match and even exceed average human performance across diverse, unseen tasks. |
| Approach: | They compare the results of different LLMs in TST evaluation using multiple input prompts and introduce the concept of prompt ensembling. |
| Outcome: | The proposed model outperforms human evaluations on multiple input prompts. |
Text-to-Multimodal Retrieval with Bimodal Input Fusion in Shared Cross-Modal Transformer (2024.lrec-main)
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| Challenge: | Multimodal video retrieval systems are needed for multimodal content retrieval . multimodal video search systems are sub-optimal for multi-modal content representations . |
| Approach: | They propose a model that learns retrieval cues for the textual query from multiple modalities and a shared embedding space with task-specific contrastive loss functions. |
| Outcome: | The proposed model outperforms state-of-the-art methods on the MSR-VTT and YouCook2 datasets and shows significant improvements from baseline. |
Textual Coverage of Eventive Entries in Lexical Semantic Resources (2024.lrec-main)
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| Challenge: | Several English, German, Spanish and Czech lexical semantic resources (which, for the most part, focus on verbs and predicates) have been selected for this experiment. |
| Approach: | They propose to quantify coverage gaps in lexical semantic resources when applied to running texts taken from the internet. |
| Outcome: | The proposed resources cover eventive entries (verbs, predicates, etc.) of well-known lexical semantic resources when applied to running texts taken from the internet. |
The Challenges of Creating a Parallel Multilingual Hate Speech Corpus: An Exploration (2024.lrec-main)
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| Challenge: | Hate speech is one of the most demanding topics in Natural Language Processing, as its multifaceted nature is accompanied by a handful of challenges, such as multilinguality and cross-linguality. |
| Approach: | They propose a pipeline that could be used to create a parallel multilingual hate speech dataset using machine translation. |
| Outcome: | The proposed pipeline will be able to create a parallel multilingual hate speech dataset using machine translation. |
The Contextual Variability of English Nouns: The Impact of Categorical Specificity beyond Conceptual Concreteness (2024.lrec-main)
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| Challenge: | Empirical studies on conceptual abstraction have examined differences in contextual distributions of abstract and concrete concept words. |
| Approach: | They propose to use a model to investigate the interplay between contextual variability and specificity of abstract and concrete concepts. |
| Outcome: | The proposed models show that more specific words have closer contexts than generic terms. |
The Corpus AIKIA: Using Ranking Annotation for Offensive Language Detection in Modern Greek (2024.lrec-main)
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Stella Markantonatou, Vivian Stamou, Christina Christodoulou, Georgia Apostolopoulou, Antonis Balas, George Ioannakis
| Challenge: | OLD is a less-resourced language regarding OLD. |
| Approach: | They propose to annotate OLD in Modern Greek using the lexicon of offensive terms that originates from HurtLex. |
| Outcome: | The proposed corpus is based on the lexicon of offensive terms that originates from HurtLex and can be used to detect offensive language in modern Greek. |
The Distracted Ear: How Listeners Shape Conversational Dynamics (2024.lrec-main)
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| Challenge: | a new study examines the relationship between listener feedback and narration quality in human communication . a variety of complex linguistic and cognitive processes underpin the success of conversations . |
| Approach: | They analyze listener feedback, narration quality and distraction effects in a SMYLE corpus . they find a positive correlation between frequency of specific feedback and narration quality . |
| Outcome: | The proposed method shows that feedback plays a pivotal role in shaping the dynamics of conversations. |
The Effects of Pretraining in Video-Guided Machine Translation (2024.lrec-main)
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| Challenge: | Existing approaches to improve VMT models integrate text and video modalities. |
| Approach: | They propose an approach that improves the performance of VMT models by using a new dataset which contains transcribed audio descriptions of movies. |
| Outcome: | The proposed model improves on the MAD (Movie Audio Descriptions) dataset. |
The ELCo Dataset: Bridging Emoji and Lexical Composition (2024.lrec-main)
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| Challenge: | Emoji-Lexical Composition dataset provides parallel annotations of emoji sequences corresponding to English phrases. |
| Approach: | They propose a dataset that offers parallel annotations of emoji sequences corresponding to English phrases. |
| Outcome: | The Emoji-Lexical Composition (ELCo) dataset offers parallel annotations of emoji sequences corresponding to English phrases. |
The Emergence of Semantic Units in Massively Multilingual Models (2024.lrec-main)
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| Challenge: | Massively multilingual models can process text in several languages relying on a shared set of parameters, but little is known about the encoding of multilingual information in single network units. |
| Approach: | They propose to use a shared set of parameters to encode multilingual information in single network units. |
| Outcome: | The proposed model achieves higher scores in semantic encoding in languages with more cross-lingual alignment than those with more shared cross-linguistic substrate. |
The Ethical Question – Use of Indigenous Corpora for Large Language Models (2024.lrec-main)
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Linda Wiechetek, Flammie A. Pirinen, Børre Gaup, Trond Trosterud, Maja Lisa Kappfjell, Sjur Moshagen
| Challenge: | Creating language technology based on language data is becoming more popular . indigenous language resources are not comparable in that they would encode the most recent normativised language . |
| Approach: | They describe an ethical way to work with indigenous languages based on language data . they say data driven methods make assumptions based upon majority languages they work with . authors say data-driven methods are not ethical or beneficial . |
| Outcome: | The proposed method is ethical and sustainable, and can be applied to indigenous languages in an ethical way. |
The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment (2024.lrec-main)
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Chris Chinenye Emezue, Ifeoma Okoh, Chinedu Emmanuel Mbonu, Chiamaka Chukwuneke, Daisy Monika Lal, Ignatius Ezeani, Paul Rayson, Ijemma Onwuzulike, Chukwuma Onyebuchi Okeke, Gerald Okey Nweya, Bright Ikechukwu Ogbonna, Chukwuebuka Uchenna Oraegbunam, Esther Chidinma Awo-Ndubuisi, Akudo Amarachukwu Osuagwu
| Challenge: | UNESCO projects that the Igbo language will be endangered by 2025 . primary obstacle in developing dialectal-aware language technologies is lack of comprehensive dialectal datasets. |
| Approach: | They propose to use a multi-dialectal Igbo-English dictionary dataset to enhance the representation of Igbe dialects. |
| Outcome: | The proposed dataset enables machine translation systems to handle dialect variations in sentences. |
The Impact of Stance Object Type on the Quality of Stance Detection (2024.lrec-main)
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| Challenge: | stance is defined by Biber and Finegan as the expression of an author's standpoint and judgment towards a given proposition. |
| Approach: | They analyze the implied knowledge and judgments required when deciding the stance of a text towards each possible stance object type. |
| Outcome: | The proposed models can infer the stance of a text towards any of the three stance object types, namely topics, claims, and frames of communication. |
The Influence of Automatic Speech Recognition on Linguistic Features and Automatic Alzheimer’s Disease Detection from Spontaneous Speech (2024.lrec-main)
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| Challenge: | Existing biomarkers for AD diagnosis can only be applied to relatively small sample sizes due to limited availability, excessive costs and invasive nature. |
| Approach: | They compare automatic speech recognition systems in terms of Word Error Rate (WER) using a publicly available benchmark dataset of speech recordings of AD patients and controls. |
| Outcome: | The proposed method improves classification performance by replacing manual transcriptions with ASR output. |
The Key Points: Using Feature Importance to Identify Shortcomings in Sign Language Recognition Models (2024.lrec-main)
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| Challenge: | Pose estimation keypoints are widely used in sign language recognition (SLR) but they are difficult to achieve due to the large degree of variability between occurrences of the same sign, the lack of large datasets and the imbalanced nature of the data. |
| Approach: | They propose to use pose estimation keypoints to generalise to unseen signers by identifying potentially redundant features and identifying key points that are most informative to SLR . they propose to train models with large datasets and labelled data to find key points which are redundant to differentiating between signs . |
| Outcome: | The proposed model can be trained on large datasets and has more generalised features than would be possible with a small dataset. |
The Low Saxon LSDC Dataset at Universal Dependencies (2024.lrec-main)
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| Challenge: | Low Saxon is a low-resource language that lacks a common standard . dialectal variation in morphological categories can cause problems . |
| Approach: | They extend the Low Saxon Universal Dependencies dataset to include 8 of the 9 major dialects. |
| Outcome: | The proposed dataset covers the last 200 years and 8 of the 9 major dialects. |
The Onomastic Repertoire of the Roman d’Alexandre (ORNARE). Designing an Integrated Digital Onomastic Tool for Medieval French Romance (2024.lrec-main)
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| Challenge: | The paper presents the first results of the design and implementation of a new digital tool for romance philology: the Onomastic Repertoire for the medieval French romance (12th-15th centuries). |
| Approach: | The paper presents the design and implementation of a digital romance philology tool . it uses a selection of romances from the corpus of the medieval French Roman d'Alexandre . |
| Outcome: | The proposed system was based on the corpus of the medieval French Roman d'Alexandre . it is the first integrated system for the creation of the Onomastic Repertoire of the romaN d’AlexandRE . |
The Open-World Lottery Ticket Hypothesis for OOD Intent Classification (2024.lrec-main)
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| Challenge: | Existing methods of Out-of-Domain intent classification lack confidence in In- and Out- of-domain intents. |
| Approach: | They propose to prune overparameterized models to provide better confidence . they extend the Lottery Ticket Hypothesis to open-world scenarios . |
| Outcome: | The proposed model can be calibrated to distinguish In- and Out-of-domain intents . the model can also improve on open-world scenarios . |
Theoretical and Empirical Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Open-World Scenarios (2024.lrec-main)
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| Challenge: | Existing methods for encoding intents as one-hot are not used for zero-shot learning. |
| Approach: | They show that one-hot encodings can produce much richer topologies for OOS detection . they argue that such gains are likely due to advantages of knowledge-free encoded intents . |
| Outcome: | The proposed method can produce better systems for open-world classification tasks, the authors show . they show that knowledge-free, randomly generated dense-vector encodings can produce 20% gains over one-hot encodes. |
The ParCoLab Parallel Corpus and Its Extension to Four Regional Languages of France (2024.lrec-main)
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Dejan Stosic, Saša Marjanović, Delphine Bernhard, Myriam Bras, Laurent Kevers, Stella Retali-Medori, Marianne Vergez-Couret, Carole Werner
| Challenge: | Parallel corpora are scarce for most of the world's language pairs. |
| Approach: | They propose to extend ParCoLab with a parallel corpus for Alsatian, Corsican, Occitan and Poitevin-Saintongeais. |
| Outcome: | The proposed corpus contains more than 20k tokens per regional language. |
The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings (2024.lrec-main)
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| Challenge: | The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment, which is used in a series of experiments focused on training a robust sentiment identifier for parliamentary proceedings. |
| Approach: | They propose to use a dataset of sentences manually annotated for sentiment to train a robust sentiment identifier for parliamentary proceedings. |
| Outcome: | The proposed model performs very well on languages not seen during fine-tuning and additional fine- tuning data from other languages significantly improves the target parliament’s results. |
There’s Something New about the Italian Parliament: The IPSA Corpus (2024.lrec-main)
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| Challenge: | despite their potential, the Italian parliamentary documents remain unexplored and inaccessible in their original paper-based form. |
| Approach: | They propose to transform Italian parliamentary documents into a structured corpus . the corpus includes speeches, reports of Standing Committees, and law proposals . |
| Outcome: | The proposed dataset spans 175 years of Italian history spanning from the issuing of the Statuto Albertino in 1848, up to the present day . |
The RIP Corpus of Collaborative Hypothesis-Making (2024.lrec-main)
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| Challenge: | Existing studies on hypothesis generation and collaborative problem solving combine these two fields but there is still a gap between the two. |
| Approach: | They propose to use a fictionalised murder investigation game as an environment to investigate how hypotheses are generated in group environments. |
| Outcome: | The proposed corpus shows the emergent roles individuals took on and the strategies the groups employed, showing what can be gained through a deeper exploration of this domain. |
The Role of Creaky Voice in Turn Taking and the Perception of Speaker Stance: Experiments Using Controllable TTS (2024.lrec-main)
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| Challenge: | Recent advances in spontaneous text-to-speech (TTS) have enabled the realistic generation of creaky voice, a voice quality known for its diverse pragmatic and paralinguistic functions. |
| Approach: | They used a creaky voice detection tool and a neural TTS engine to control creaky phonation in a spontaneous speech corpus to investigate the effect of creaky voices on perceived certainty, valence, sarcasm, and turn finality. |
| Outcome: | The proposed model enables the realistic synthesis of creaky voice in perceptual tests without formal training. |
The Role of Syntactic Span Preferences in Post-Hoc Explanation Disagreement (2024.lrec-main)
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| Challenge: | Existing methods for post-hoc explanations for transformer models disagree with each other . disagreement is often overlooked and the reasons for disagreement are not investigated . |
| Approach: | They propose to use a dynamic *k* approach to estimate syntactic spans to improve agreement between different methods. |
| Outcome: | The proposed method better agrees on syntactic span level, especially for the methods that agree the least with other methods. |
The SAMER Arabic Text Simplification Corpus (2024.lrec-main)
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| Challenge: | Our corpus includes 159K words selected from 15 publicly available Arabic fiction novels . text simplification aims to reduce the complexity of a text while maintaining the overall grammaticality and core content. |
| Approach: | They propose to annotate Arabic parallel corpus for text simplification targeting school-aged learners. |
| Outcome: | The SAMER Corpus includes readability level annotations at both the document and word levels, as well as two simplified parallel versions for each text targeting learners at two different readability levels. |
The Slovak Autistic and Non-Autistic Child Speech Corpus:Task-Oriented Child-Adult Interactions (2024.lrec-main)
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| Challenge: | Presented is the Slovak Autistic and Non-Autistic Child Speech Corpus . corpus contains over 15 hours of speech . |
| Approach: | They present a Slovak autistic and non-autistic child speech corpus . the corpus was primarily recorded to investigate lexical alignment . |
| Outcome: | The Slovak Autistic and Non-Autistic Child Speech Corpus contains over 15 hours of speech . the corpus can be shared with researchers and replicated in future research . |
The Swedish Parliament Corpus 1867 – 2022 (2024.lrec-main)
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Väinö Aleksi Yrjänäinen, Fredrik Mohammadi Norén, Robert Borges, Johan Jarlbrink, Lotta Åberg Brorsson, Anders P. Olsson, Pelle Snickars, Måns Magnusson
| Challenge: | The Swedish Parliament Corpus is a new research corpus for the Swedish parliament. |
| Approach: | They propose to expand the Swedish Parliament corpus by providing a database of all members of parliament over 150 years. |
| Outcome: | The new corpus facilitates detailed analysis of parliamentary speeches in several research fields. |
The Syntactic Acceptability Dataset (Preview): A Resource for Machine Learning and Linguistic Analysis of English (2024.lrec-main)
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| Challenge: | Syntactic acceptance dataset is a resource being designed for syntax and computational linguistics research. |
| Approach: | They propose to use the Syntactic Acceptability Dataset to examine the syntactical discourse. |
| Outcome: | The proposed dataset is the largest of its kind that is publicly accessible. |
The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments (2024.lrec-main)
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Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary, Maximilian Heinrich, Nicolas Handke, Xiaoni Cai, Valentin Barriere, Doratossadat Dastgheib, Omid Ghahroodi, MohammadAli SadraeiJavaheri, Ehsaneddin Asgari, Lea Kawaletz, Henning Wachsmuth, Benno Stein
| Challenge: | Cultural norms can influence the prioritization of values, leading to distinct perspectives on debatable topics. |
| Approach: | They present a Touché23-ValueEval dataset that annotates 4780 new arguments and annotated 54 human values. |
| Outcome: | The Touché23-ValueEval dataset doubles the original Webis-ArgValués-22 dataset to 9324 arguments. |
TIGER: A Unified Generative Model Framework for Multimodal Dialogue Response Generation (2024.lrec-main)
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| Challenge: | Existing research on multimodal dialogues focuses on textual response generation and visual response selection based on the dialogue context. |
| Approach: | They propose a generative model framework for multimodal dialogue response generation that ground the conversation on an image. |
| Outcome: | The proposed system provides users with an enhanced conversational experience. |
TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya (2024.lrec-main)
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| Challenge: | Existing annotated datasets for NLP tasks in languages with limited resources are limited. |
| Approach: | They propose to use machine translation to convert existing Tigrinya dataset into a Tigrina dataset in SQuAD format. |
| Outcome: | The proposed dataset is an expert-annotated Tigrinya dataset with 2,685 question-answer pairs covering 122 diverse topics. |
Time-aware COMET: A Commonsense Knowledge Model with Temporal Knowledge (2024.lrec-main)
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| Challenge: | Existing commonsense knowledge models do not consider granularity or time axes, and can't handle commonsensical knowledge, which is tacit. |
| Approach: | They propose to use ChatGPT to create a time-aware commonsense knowledge model, TaCOMET, and use it to continually fine tune existing models. |
| Outcome: | The proposed model outperforms existing models on a robotic decision-making task when proper times are input. |
Title-based Extractive Summarization via MRC Framework (2024.lrec-main)
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| Challenge: | Existing studies on extractive summarization focus on scoring and selecting summary sentences . existing models tend to select generalized sentences while overlooking the overall content of a document. |
| Approach: | They propose a machine reading comprehension framework for extractive summarization by setting a query as the title. |
| Outcome: | The proposed framework outperforms existing models on long and short summaries in Korean and English . it can consider the semantic coherence and relevance of summary sentences in relation to the overall content . |
TMFN: A Target-oriented Multi-grained Fusion Network for End-to-end Aspect-based Multimodal Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods for multimodal aspect-based sentiment analysis focus on fusing image regional information and textual words. |
| Approach: | They propose a multimodal aspect-based sentiment analysis method that integrates regional and global image information with global image data. |
| Outcome: | Experiments show that the proposed method outperforms state-of-the-art methods on two benchmark datasets. |
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese (2024.lrec-main)
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| Challenge: | Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages. |
| Approach: | They collect large-scale human annotations of whether and why a particular argument should be omitted across over 2,000 data points in Japanese, a prototypical pro-drop language. |
| Outcome: | The proposed model can explain why certain arguments are omitted in Japanese, a prototypical pro-drop language. |
To Err Is Human, How about Medical Large Language Models? Comparing Pre-trained Language Models for Medical Assessment Errors and Reliability (2024.lrec-main)
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| Challenge: | a 1999 report found that at least forty thousand deaths are a result of preventable medical errors. |
| Approach: | They test pre-trained language models to characterize their error generation and reliability in medical assessment ability. |
| Outcome: | The results show that pre-trained models can generate errors and perform better than human models. |
Token-length Bias in Minimal-pair Paradigm Datasets (2024.lrec-main)
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| Challenge: | Minimal-pair paradigm datasets are used to evaluate the linguistic knowledge of language models and provide an unsupervised method of acceptability judgment. |
| Approach: | They propose a debiased minimal pair generation method that allows MPP datasets to evaluate the linguistic knowledge of a language model correctly. |
| Outcome: | The proposed method is based on the percentage of minimal pairs in the MPP dataset where the model assigns a higher sentence log-likelihood than an unacceptable sentence. |
To Learn or Not to Learn: Replaced Token Detection for Learning the Meaning of Negation (2024.lrec-main)
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| Challenge: | State-of-the-art language models perform well on a variety of language tasks, but struggle with understanding negation cues in tasks like natural language inference (NLI). |
| Approach: | They propose a new learning strategy for negation building on ELECTRA’s replaced token detection objective. |
| Outcome: | The proposed approach leads to substantial gains on a variant of RTE with additional negation. |
ToNER: Type-oriented Named Entity Recognition with Generative Language Model (2024.lrec-main)
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| Challenge: | Input too many potential entity types would distract the model inevitably. |
| Approach: | They propose to use a generative model to exploit entity types' merit on promoting NER task by appending a type matching model to identify the entity types most likely to appear in the sentence. |
| Outcome: | The proposed framework exploits entity types' merit on promoting NER task by adding auxiliary task to the model to discover the entity types. |
ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval (2024.lrec-main)
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| Challenge: | Recent studies have proposed tool learning, which augments LLMs with external tools. |
| Approach: | They propose an adaptive and hierarchy-aware reranking method to refine retrieval results by truncating the retrieval result related to seen and unseen tools at different positions. |
| Outcome: | The proposed method improves retrieval results, leading to better execution results generated by the LLM. |
Topic Classification and Headline Generation for Maltese Using a Public News Corpus (2024.lrec-main)
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| Challenge: | Existing datasets for low-resource languages lack labelled data . public datasets only cover low-level syntactic tasks . |
| Approach: | They propose to use a news tag multi-label classification and a summary task by generating its title to generate a new semantic dataset for Maltese. |
| Outcome: | The proposed datasets show that current models lack the knowledge required to solve such tasks. |
Topic-Controllable Summarization: Topic-Aware Evaluation and Transformer Methods (2024.lrec-main)
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| Challenge: | Existing methods for topic-controllable summarization are limited by their recurrent architectures and require modifications to the model's architecture for controlling the topic. |
| Approach: | They propose a new topic-oriented evaluation measure to automatically evaluate the generated summaries based on the topic affinity between the generated summary and the desired topic. |
| Outcome: | The proposed method achieves better performance compared to more complicated embedding-based approaches while also being significantly faster. |
Topic Detection and Tracking with Time-Aware Document Embeddings (2024.lrec-main)
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| Challenge: | Topic Detection and Tracking (TDT) systems aim to cluster news articles into real-world events. |
| Approach: | They propose a neural method that fuses temporal and textual information into a single representation of news documents for event detection. |
| Outcome: | The proposed model outperforms baselines on two benchmark TDT data sets in English. |
TopicDiff: A Topic-enriched Diffusion Approach for Multimodal Conversational Emotion Detection (2024.lrec-main)
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| Challenge: | Existing studies focus on learning contextual information in conversations, neglecting acoustic and vision topic information. |
| Approach: | They propose a model-agnostic Topic-enriched Diffusion approach for capturing multimodal topic information in MCE tasks. |
| Outcome: | The proposed approach improves over the state-of-the-art MCE models and the existing models. |
Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks (2024.lrec-main)
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| Challenge: | Topic models aim to reveal latent structures within corpus of text through term-frequency statistics over bag-of-words representations. |
| Approach: | They propose to use bimodal vector representations of entities to extract latent representations from large language models and graph neural networks trained on symbolic relations to derive the most salient aspects of these conceptual units. |
| Outcome: | The proposed approach is better suited to working with entities than state-of-the-art models. |
To Share or Not to Share: What Risks Would Laypeople Accept to Give Sensitive Data to Differentially-Private NLP Systems? (2024.lrec-main)
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| Challenge: | Existing studies on differential privacy in NLP mainly focus on technical aspects, neglecting the human perception of privacy risks. |
| Approach: | They propose to use a differentially private algorithm to determine the privacy budget to determine which values are acceptable in which situations. |
| Outcome: | The proposed study aims to determine what thresholds would lead lay people to share sensitive textual data. |
Towards a Corpus of Spoken Maltese: Korpus tal-Malti Mitkellem, KMM (2024.lrec-main)
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| Challenge: | 'Corpus of Spoken Maltese' is a spoken corpus of spoken Malteser based on a gold-standard Core collection . initial results show that the ASR is robust enough to generate first-pass texts for annotators to work on, thus reducing the human effort and consequently, the cost involved. |
| Approach: | They propose to create a “dedicated” spoken corpus of Maltese based on a gold-standard Core collection and a qualitative analysis of the output of a Malteser ASR system. |
| Outcome: | The proposed corpus is based on the concept of a gold-standard Core collection and compares to human annotations. |
Towards a Danish Semantic Reasoning Benchmark - Compiled from Lexical-Semantic Resources for Assessing Selected Language Understanding Capabilities of Large Language Models (2024.lrec-main)
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| Challenge: | a semantic reasoning benchmark for Danish is compiled from human-curated lexical-semantic resources. |
| Approach: | They present a semantic reasoning benchmark for Danish compiled semi-automatically from a number of human-curated lexical-semantic resources. |
| Outcome: | The proposed datasets are compiled semi-automatically from human-curated lexical-semantic resources. |
Towards a Framework for Evaluating Explanations in Automated Fact Verification (2024.lrec-main)
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| Challenge: | A growing interest has emerged in rationalizing explanations to provide short and coherent justifications for predictions. |
| Approach: | They propose a formal framework for rationalizing explanations to support their evaluation systematically. |
| Outcome: | The proposed framework is tailored to rationalizing explanations of increasingly complex structures, from free-form explanations to argumentative explanations with the richest structure. |
Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data (2024.lrec-main)
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| Challenge: | Synthetic data generation has the potential to impact domains with scarce data, but we need to understand how different demographics are represented in it. |
| Approach: | They develop a procedure to generate depression data using GPT-3 and analyze it to uncover the types of stressors it assigns to demographic groups. |
| Outcome: | The proposed procedure produces depression data using GPT-3, and compares it to a human-generated dataset. |
Towards an Ideal Tool for Learner Error Annotation (2024.lrec-main)
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| Challenge: | 'correction annotation' is a technique that has been used for many years to correct errors in learner corpora. |
| Approach: | They propose to use SVALA to annotate and analyse corrections in learner corpora using a parallel aligned approach to visualisation and annotation. |
| Outcome: | The proposed tool supports multiple annotation systems, localisation into other languages, and the development of more complex annotation systems. |
Towards Answering Health-related Questions from Medical Videos: Datasets and Approaches (2024.lrec-main)
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| Challenge: | Existing systems that can provide visual answers from medical videos to natural language questions are limited by the availability of large datasets. |
| Approach: | They propose to use large-scale medical video datasets to provide visual answers to questions . they propose to combine multimodal and monomodal approaches to provide answers . |
| Outcome: | The proposed approach can provide visual answers from medical videos to natural language questions. |
Towards a Unified Taxonomy of Deep Syntactic Relations (2024.lrec-main)
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| Challenge: | Currently, UD is the standard for morphology and surface syntax annotations, but it is only one step towards natural language understanding. |
| Approach: | They propose to use a set of universal semantic role labels for morphology and surface syntax in four Indo-European and one Uralic languages to analyze the data. |
| Outcome: | The proposed set of universal semantic role labels is based on the data from four Indo-European and one Uralic languages. |
Towards Autonomous Tool Utilization in Language Models: A Unified, Efficient and Scalable Framework (2024.lrec-main)
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| Challenge: | Recent advances in tool learning for large language models have led to a new trend to allow LLMs to leverage external tools. |
| Approach: | They propose a framework for fine-tuning language models that categorizes queries into three different types . they also introduce an "instruct, execute, and reformat" strategy specifically designed for efficient data annotation . |
| Outcome: | The proposed framework surpasses open-source language models and GPT-3.5/4 on multiple evaluation metrics. |
Towards a Zero-Data, Controllable, Adaptive Dialog System (2024.lrec-main)
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| Challenge: | Recent approaches to controllable dialog systems require additional training data to be deployed in new domains. |
| Approach: | They propose to generate dialog tree data directly from dialog trees by using a commercial Large Language Model or a single GPU. |
| Outcome: | The proposed approach can achieve comparable dialog success to models trained on human data. |
Towards Building the LEMI Readability Platform for Children’s Literature in the Romanian Language (2024.lrec-main)
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Madalina Chitez, Mihai Dascalu, Aura Cristina Udrea, Cosmin Strilețchi, Karla Csürös, Roxana Rogobete, Alexandru Oravițan
| Challenge: | Currently, no existing platform integrates a research-based readability formula for the Romanian language, making this tool unique. |
| Approach: | They propose a new readability tool for children’s literature in the Romanian language that uses a self-compiled corpus and a text analysis interface to generate automatic readability reports for uploaded short texts. |
| Outcome: | The proposed readability tool is specifically targeted at primary school students aged 7-11 . it extracts, tests, and calibrates a readability formula for Romanian using the children’s literature corpus and the platform functionalities. |
Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue (2024.lrec-main)
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| Challenge: | Contemporary NLP has progressed from feature-based classification to fine-tuning and prompt-based techniques . many of these techniques remain understudied in the context of real-world, clinically enriched spontaneous dialogue. |
| Approach: | They investigate the efficacy and overall performance of a range of NLP techniques on transcribed speech from patients with schizophrenia and other disorders. |
| Outcome: | The proposed methods are effective in analyzing transcribed speech from patients with schizophrenia and healthy controls taking a clinically-validated language test. |
Towards Cost-effective Multi-style Conversations: A Pilot Study in Task-oriented Dialogue Generation (2024.lrec-main)
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| Challenge: | Current task-oriented dialogue systems are trained on a single conversational style and do not account for the diversity of styles encountered when interacting with different users. |
| Approach: | They propose a method for generating multi-style conversations using a multi-language dataset that is available in a conversational domain. |
| Outcome: | The proposed model can be used in the development of conversational agents . it assumes the availability of a conversational domain and leverages the generative capabilities of large language models. |
Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification (2024.lrec-main)
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| Challenge: | Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including audio signals. |
| Approach: | They propose to use self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks. |
| Outcome: | The proposed model improves dog recognition, breed identification, gender classification, and context grounding tasks. |
Towards Equitable Natural Language Understanding Systems for Dialectal Cohorts: Debiasing Training Data (2024.lrec-main)
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| Challenge: | Prior research has shown that biases exist in these models against certain languages or dialects. |
| Approach: | They propose to use a dialect identification model to obtain targeted training data augmentation for under-represented dialects to debias NLU model for dialectal cohorts in NLU systems. |
| Outcome: | The proposed framework can provide insights on dialect disparity in real-world NLU systems and targeted data argumentation can help narrow the model’s performance gap between standard language speakers and dialect speakers. |
Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset (2024.lrec-main)
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| Challenge: | Using Swiss Judgement Prediction, we evaluate the explainability of state-of-the-art monolingual and multilingual LJP models. |
| Approach: | They propose an occlusion-based approach to evaluate the explainability performance of legal judgement prediction models using Swiss Judgement Prediction, the only available multilingual LJP dataset. |
| Outcome: | The proposed framework allows us to quantify the influence of lower court information on model predictions, exposing current models’ biases. |
Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation (2024.lrec-main)
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| Challenge: | Existing methods for named entity recognition from document images are limited in few-shot settings. |
| Approach: | They propose a framework which leverages the topological adjacency relationship among tokens by learning layout information with graph neural networks. |
| Outcome: | The proposed framework outperforms baselines under different few-shot settings and shows better performance to image manipulations. |
Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have achieved significant performance in various natural language reasoning tasks, but struggle with performing first-order logic reasoning over formal logical theories expressed in natural language. |
| Approach: | They propose a framework which introduces the paradigm of resolution refutation to solve first-order logic reasoning problems by extending reasoning rules and employing the principle of proof by contradiction. |
| Outcome: | The proposed framework outperforms existing models while maintaining performance in simple scenarios. |
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database (2024.lrec-main)
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| Challenge: | Existing benchmarks for textual question answering only focus on single-chain or single-hop retrieval . Existing approaches to answer complex questions have limitations . |
| Approach: | They propose to conduct Graph-Hop, a novel multi-chains and multi-hops retrieval paradigm in complex question answering. |
| Outcome: | The proposed model provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning. |
Towards Human-aligned Evaluation for Linear Programming Word Problems (2024.lrec-main)
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Linzi Xing, Xinglu Wang, Yuxi Feng, Zhenan Fan, Jing Xiong, Zhijiang Guo, Xiaojin Fu, Rindra Ramamonjison, Mahdi Mostajabdaveh, Xiongwei Han, Zirui Zhou, Yong Zhang
| Challenge: | Existing evaluation methodologies for MWPs diverge from human judgment and face challenges in recognizing mathematically equivalent answers. |
| Approach: | They propose an evaluation metric rooted in graph edit distance that features benefits such as permutation invariance and more accurate program equivalence identification. |
| Outcome: | The proposed evaluation metric features benefits such as permutation invariance and more accurate program equivalence identification. |
Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents (2024.lrec-main)
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| Challenge: | Existing document AI approaches fail to consider key-value relations in visually-rich documents . a few-shot approach is proposed to extract key- value relation triplets in VRDs . |
| Approach: | They propose a few-shot relational learning approach targeting the extraction of key-value relation triplets in Visually-Rich Documents. |
| Outcome: | The proposed method outperforms existing methods in visually-rich documents. |
Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model (2024.lrec-main)
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Yue Wang, Zilong Zheng, Juntao Li, Zhihui Liu, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang
| Challenge: | Large Language Models (LLMs) have been gaining attention for their ability to perform a wide range of open-domain tasks . however, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios . |
| Approach: | They propose a task to evaluate the performance of Large Language Models (LLMs) they propose RCSC task to convert Chinese text into correct text . |
| Outcome: | The proposed task evaluates the performance of existing methods in Chinese text . the realistic Chinese spell checker can achieve state-of-the-art performance on the task . |
Towards Multi-modal Sarcasm Detection via Disentangled Multi-grained Multi-modal Distilling (2024.lrec-main)
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| Challenge: | Existing approaches to sarcasm detection focus on textual and intra-modal incongruity . mainstream approaches process input of each modality in a holistic manner, resulting in redundant and unrefined information. |
| Approach: | They propose a framework for multi-modal sarcasm detection that disentangles modality representations into latent spaces and conducts multi-grained knowledge distilling. |
| Outcome: | The proposed framework overpowers existing methods on a common benchmark. |
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation (2024.lrec-main)
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| Challenge: | Existing methods for few-shot relation extraction are not realistic due to the large amount of training data required. |
| Approach: | They propose a meta dataset for few-shot relation extraction based on existing supervised relation extraction datasets and a few-shot form of the TACRED dataset. |
| Outcome: | The proposed methods perform poorly on the few-shot relation extraction task. |
Towards Robust Evidence-Aware Fake News Detection via Improving Semantic Perception (2024.lrec-main)
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| Challenge: | Existing methods lack sufficient semantic perception and are easily blinded by textual expressions. |
| Approach: | They propose a model-agnostic training framework to improve the semantic perception of evidence-aware fake news detection by combining two kinds of data augmentations with synthetic data. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the extended test set while achieving competitive performance on the original one. |
Towards Robust In-Context Learning for Machine Translation with Large Language Models (2024.lrec-main)
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| Challenge: | Experimental results demonstrate the effectiveness of our method, particularly in domain adaptation. |
| Approach: | They propose a method to retrieve translation pairs as demonstrations from an additional datastore to guide translation without updating the LLMs. |
| Outcome: | The proposed method reduces noise and improves translation performance in domain adaptation. |
Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)
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Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng
| Challenge: | Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling. |
| Approach: | They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries. |
| Outcome: | The proposed method is significantly more robust to noisy training data than existing methods. |
Towards Semantic Tagging for Irish (2024.lrec-main)
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| Challenge: | well annotated corpora have been shown to have great value in linguistic and non-linguistic research . minority languages suffer from fewer available language resources than majority languages . a new method for evaluation of semantic annotation is being developed for Irish . |
| Approach: | They propose to build a tool-set for semantic annotation of Irish using semantic tags . they propose to use a lexicon built from a variety of sources to evaluate the tool . |
| Outcome: | a new method for evaluation of semantic annotation has been developed for Irish . the proposed method has 90% lexical coverage and almost 80% accuracy . |
Towards Standardized Annotation and Parsing for Korean FrameNet (2024.lrec-main)
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| Challenge: | Existing studies on Korean FrameNet have focused on English, but annotations are not optimally designed for Korean. |
| Approach: | They propose a morphologically enhanced annotation strategy for Korean FrameNet datasets and parsing by leveraging the CoNLL-U format. |
| Outcome: | The proposed method improves the annotation accuracy of Korean FrameNet datasets and their parsers. |
Towards the WhAP Corpus: A Resource for the Study of Italian on WhatsApp (2024.lrec-main)
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| Challenge: | a new initiative is aimed at building a resource featuring WhatsApp conversations in Italian . the project will allow in-depth linguistic research on the language used on WhatsApp . primary objective is to address a gap in terms of attention towards the use of the italian language . |
| Approach: | They propose to build a resource featuring WhatsApp conversations in Italian . the project will enable in-depth linguistic research on the language used on WhatsApp . primary objective is to address a gap in terms of attention towards the use of the italian language . |
| Outcome: | The project builds a resource featuring WhatsApp conversations in Italian . it will allow in-depth linguistic research on the language used on the messaging service . primary objective is to address a gap in terms of attention towards the use of the italian language . |
Towards Understanding the Relationship between In-context Learning and Compositional Generalization (2024.lrec-main)
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| Challenge: | In-context learning is an inductive bias for compositional generalization, but many deep neural architectures struggle with this ability. |
| Approach: | They propose to force a causal Transformer to in-context learn to promote compositional generalization by using earlier examples to generalize to later ones. |
| Outcome: | The proposed model can solve 'ordinary' learning problems by utilizing earlier examples to generalize to later ones, i.e., in-context learning. |
Towards Universal Dependencies for Ancash Quechua (2024.lrec-main)
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| Challenge: | a new corpus of Quechua morphosyntactic features are described for Ancash Quechuan, the majority variety of the Central Quechual language family . the morphology of the language is a feature of the universal dependency (UD) schema . a syntactical parser would be the first NLP tool for a Quechuang language of this family based on the UD schema based upon the typology of the languages . |
| Approach: | They propose to describe some morphosyntactic features of Ancash Quechua . they propose to build a corpus annotated according to the universal dependency schema . |
| Outcome: | The proposed corpus is the first bilingual and sentence-aligned digital corpus in Ancash Quechua and Spanish. |
TP-Link: Fine-grained Pre-Training for Text-to-SQL Parsing with Linking Information (2024.lrec-main)
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Ziqiang Liu, Shujie Li, Zefeng Cai, Xiangyu Li, Yunshui Li, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Existing pre-training frameworks for text-to-SQL parsing have shown inherent differences in distributions between tables and plain text. |
| Approach: | They propose a framework to improve context-dependent Text-to-SQL parsing by leveraging Linking information. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two leading downstream benchmarks. |
Training BERT Models to Carry over a Coding System Developed on One Corpus to Another (2024.lrec-main)
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| Challenge: | a pilot project aims to track trends in the perception of literary translation around the 1989 political transformation in Hungary. |
| Approach: | They train BERT models to carry over a coding system developed on a journal to another . aim is to track trends in perception of literary translation around 1989 political transformation . |
| Outcome: | The proposed system can carry over from one coding system to another, the authors show . the system can improve performance and provide better predictions from an ensemble . |
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills (2024.lrec-main)
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| Challenge: | Existing methods to fine-tune code intelligence models to individual tasks are costly and require large data sets. |
| Approach: | They propose a Transferable fine-tuning strategy for Code representation learning that uses a tunable prefix encoder to capture cross-task and cross-language transferable knowledge and apply it to downstream adaptation. |
| Outcome: | The proposed method can lead to superior performance on code-related tasks and encourage mutual reinforcement. |
TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation (2024.lrec-main)
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| Challenge: | Existing knowledge graph embedding models lack links between entities and relationships, which is a problem for knowledge graphs. |
| Approach: | They propose a translation-based knowledge geraph embedding method via efficient relation rotation that rotates the head and tail entities with their corresponding unit quaternions. |
| Outcome: | The proposed method can be used to embed knowledge graphs on 10 benchmark datasets with fewer parameters than the previous translation-based models. |
Transfer Fine-tuning for Quality Estimation of Text Simplification (2024.lrec-main)
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| Challenge: | Experimental results show that quality estimation of text simplification models can be improved on a small labeled corpus. |
| Approach: | They propose a method to train quality estimation of text simplification on a small-scale labeled corpus prior to fine-tuning pre-trained language models. |
| Outcome: | The proposed method improves quality estimation of text simplification on a small-scale labeled corpus. |
Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching (2024.lrec-main)
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| Challenge: | Pre-trained language models have revolutionized the natural language understanding landscape, but limited data hinders effective training of such models. |
| Approach: | They propose to transfer BERT capabilities from high-resource to low-resourced languages using vocabulary matching. |
| Outcome: | The proposed technique improves performance even when target language has minimal training data. |
Transformer-based Joint Modelling for Automatic Essay Scoring and Off-Topic Detection (2024.lrec-main)
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| Challenge: | Existing studies show that automated essay scoring systems assign lower grades to irrelevant responses. |
| Approach: | They propose an unsupervised technique that jointly scores essays and detects off-topic essays. |
| Outcome: | The proposed method outperforms baseline and earlier conventional methods on two essay-scoring datasets in off-topic detection and on-topic scoring. |
Transformer-based Swedish Semantic Role Labeling through Transfer Learning (2024.lrec-main)
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| Challenge: | Semantic Role Labeling (SRL) is a task in natural language understanding where the goal is to extract semantic roles for a given sentence. |
| Approach: | They propose to build a Transformer-based SRL system for Swedish by exploring multilingual and cross-lingual transfer learning methods and leveraging the Swedish FrameNet resource. |
| Outcome: | The proposed model outperforms two different cross-lingual transfer models and shows that the multilingual learning outperformed the other models. |
Transformers for Bridging Persian Dialects: Transliteration Model for Tajiki and Iranian Scripts (2024.lrec-main)
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| Challenge: | Despite its profound linguistic and cultural significance, Tajiki Persian remains a low-resource language with scant digitized datasets for computational applications. |
| Approach: | They propose to use Shahnameh, a seminal Persian epic poem, to train and assess Tajiki Persian transliteration models using two prominent sequence-to-sequence architectures: GRU with attention and transformer. |
| Outcome: | The proposed model outperforms pre-trained models with attention and transformer. |
Tree-Instruct: A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment (2024.lrec-main)
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| Challenge: | Extensive research has highlighted the importance of data complexity as a crucial metric, but the impact of complexity remains relatively unexplored. |
| Approach: | They propose to add a specified number of nodes to instructions’ semantic trees to enhance the instruction complexity in a controllable manner. |
| Outcome: | The proposed approach outperforms diverse yet complex instructions under the same token budget and can control the difficulty level of modified instructions. |
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models (2024.lrec-main)
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| Challenge: | Existing methods for incorporating external knowledge into language models do not prioritize learning embeddings for entity-related tokens. |
| Approach: | They propose a framework for incorporating external knowledge into pre-training models that utilize entity-related tokens. |
| Outcome: | The proposed framework reduces pre-training time by 50% and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks. |
Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks (2024.lrec-main)
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| Challenge: | Existing methods to jailbreak large language models have been poorly studied . a recent study showed that non-expert users can jailbreak LLMs by manipulating their prompts . |
| Approach: | They propose a formalism and a taxonomy of known (and possible) jailbreaks . they propose generating a dataset of model outputs across 3700 jailbreak prompts a 'prompt' attack is a new attack popularly categorized as "prompting injection attacks" |
| Outcome: | The proposed model exploits 3700 jailbreak prompts over 4 tasks to analyze their effectiveness . authors show that the model can learn to perform a new task on unseen examples . |
Triple-R: Automatic Reasoning for Fact Verification Using Language Models (2024.lrec-main)
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| Challenge: | Existing methods for fact-checking lack external sources and human-understandable explanations for decision-making . existing methods lack external knowledge sources and explanations . |
| Approach: | They propose a framework that uses the Web as an external knowledge source to retrieve relevant evidence for claims and generates reasons based on the retrieved evidence for datasets lacking explanations. |
| Outcome: | The proposed method improves the transparency and interpretability of fact-checking systems by providing human-understandable explanations for decision-making processes. |
Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generation (2024.lrec-main)
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| Challenge: | Existing data-to-text models are designed for the linguistic typology of English, but they are not suitable for low-resource languages. |
| Approach: | They propose a new dataset based on a subset of WebNLG that is agglutinative and low-resource data-to-text. |
| Outcome: | The proposed model outperforms existing models for isiXhosa and Finnish and fine-tunes machine translation models as the best method overall. |
Trustworthiness and Self-awareness in Large Language Models: An Exploration through the Think-Solve-Verify Framework (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) are becoming increasingly influential in reasoning tasks, but they lack trustworthiness and introspective self-awareness when subjected to complex reasoning tasks. |
| Approach: | They propose a framework to explore LLMs’ trustworthiness, introspective self-awareness, and collaborative reasoning by using the Think-Solve-Verify framework. |
| Outcome: | The proposed approach improves from 67.3% to 72.8% on the AQuA dataset and demonstrates the model’s ability to explain the given answers. |
Tug-of-War between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models (2024.lrec-main)
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| Challenge: | Existing knowledge conflicts in RALMs can ensnare them in a tug-of-war between knowledge and evidence, limiting their practical applicability. |
| Approach: | They propose a method called Conflict-Disentangle Contrastive Decoding (CD2) to better calibrate the model’s confidence. |
| Outcome: | The proposed method can resolve knowledge conflicts in large language models with the help of conflict-disentangle contrast decoding (CD2) . |
TunArTTS: Tunisian Arabic Text-To-Speech Corpus (2024.lrec-main)
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| Challenge: | Historically, TTS relied on classical methods that proved expensive in terms of data storage and often resulted in robotic-sounding output known as concatenative speech. |
| Approach: | They propose to extract a mono-speaker speech corpus from an online dictionary and use it to develop end-to-end TTS systems for the Tunisian dialect. |
| Outcome: | The proposed system is based on two approaches: training from scratch and transfer learning. |
TweetTER: A Benchmark for Target Entity Retrieval on Twitter without Knowledge Bases (2024.lrec-main)
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| Challenge: | Entity linking is a well-established task in NLP consisting of associating entity mentions with entries in a knowledge base. |
| Approach: | They propose a benchmark that reframes entity linking as a binary entity retrieval task and uses a knowledge base to evaluate model performance. |
| Outcome: | The proposed benchmark aims to bridge the challenges in entity linking in noisy domains such as social media. |
Two Counterexamples to Tokenization and the Noiseless Channel (2024.lrec-main)
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| Challenge: | Nevertheless, Rényi efficiency is not perfect and the metric is difficult to evaluate because training multiple tokenizers can be prohibitively expensive and takes days or weeks. |
| Approach: | They propose to use Rényi efficiency as an intrinsic mechanism to evaluate a tokenizer for NLP tasks without the expensive step of training multiple models with different tokenizers. |
| Outcome: | The proposed metric is better correlated to downstream model performance than a percentile frequency metric. |
Typos Correction Training against Misspellings from Text-to-Text Transformers (2024.lrec-main)
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| Challenge: | Existing dense retrieval systems suffer from typoed queries due to mistyping or phonetic typing errors. |
| Approach: | They propose a method that incorporates the spelling correction objective into the DR model and a prompt-based augmentation technique to enhance the alignment of the typoed query and its original query. |
| Outcome: | The proposed model outperforms existing typos-aware training approaches and sophisticated training advanced retrievers. |
UCxn: Typologically-Informed Annotation of Constructions Atop Universal Dependencies (2024.lrec-main)
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Leonie Weissweiler, Nina Böbel, Kirian Guiller, Santiago Herrera, Wesley Samuel Scivetti, Arthur Lorenzi, Nurit Melnik, Archna Bhatia, Hinrich Schütze, Lori Levin, Amir Zeldes, Joakim Nivre, William Croft, Nathan Schneider
| Challenge: | Grammatical constructions that convey meaning through a particular combination of several morphosyntactic elements are not labeled holistically. |
| Approach: | They propose to augment UD annotations with a ‘UCxn’ annotation layer for such meaning-bearing grammatical constructions and to approach this in a typologically informed way so that morphosyntactic strategies can be compared across languages. |
| Outcome: | The proposed annotation layer could be used to annotate meaning-bearing constructions across languages and to compare them across languages. |
UDMorph: Morphosyntactically Tagged UD Corpora (2024.lrec-main)
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| Challenge: | a range of different problems exist in using annotated corpus data and training data . linguistic annotations are only available for a limited amount of typically major languages . |
| Approach: | a new corpus creation environment provides annotated corpus data for additional languages . a range of different problems exist in using these new tools and training data . |
| Outcome: | a new tool provides an infrastructure for annotated corpus data that follows UD guidelines . a GUI interface to a growing collection taggers with a CoNLL-U output is available for 150 languages . |
UkraiNER: A New Corpus and Annotation Scheme towards Comprehensive Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition excludes nested, discontinuous, non-named entities in practice . despite attempts to broaden their coverage, the most restrictive variant of NER remains the default . |
| Approach: | They propose a new annotation scheme that offers higher comprehensiveness while preserving simplicity. |
| Outcome: | The proposed scheme offers higher comprehensiveness while preserving simplicity . it also includes an annotation tool to implement the scheme on the corpus UkraiNER . |
UMTIT: Unifying Recognition, Translation, and Generation for Multimodal Text Image Translation (2024.lrec-main)
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| Challenge: | Current Image machine translation (IMT) relies on a cascaded system that combines Optical Character Recognition (OCR) and a complex process of rendering the translated text back onto the source image. |
| Approach: | They propose a multimodal image-text translation model that generates consistent target images . they use two image-to-text conversion steps to convert images to text to recognize source text . |
| Outcome: | The proposed model outperforms existing methods and surpasses state-of-the-art methods in text recognition tasks. |
Uncertainty-Aware Cross-Modal Alignment for Hate Speech Detection (2024.lrec-main)
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| Challenge: | Existing methods for detecting hate speech ignore misalignment and uncertainty between modalities . social media platforms have become conduits for the rapid dissemination of hate speech . |
| Approach: | They propose an uncertainty-aware cross-modal alignment framework for hate speech detection that minimizes the misalignment of image and text in memes. |
| Outcome: | The proposed framework produces a competitive performance compared with existing methods. |
Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media (2024.lrec-main)
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| Challenge: | a social media analysis of online influence campaigns can reveal the sources of agenda setting . annotated data is limited or nonexistent, but there are methods to detect agenda control . |
| Approach: | They propose a method for detecting instances of agenda control through social media . they use a modest corpus of tweets centered on the 2022 french presidential election . |
| Outcome: | The proposed method overcomes the requirement for large annotated training dataset. |
Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study (2024.lrec-main)
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| Challenge: | Large language models have shown remarkable capability in many downstream tasks, yet their ability to understand discourse structures of dialogues remains less explored. |
| Approach: | They aim to systematically inspect ChatGPT’s performance in two discourse analysis tasks: topic segmentation and discourse parsing. |
| Outcome: | The proposed model can give more reasonable topic structures than human annotations but only linearly parses the hierarchical rhetorical structures. |
Understanding How Positional Encodings Work in Transformer Model (2024.lrec-main)
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| Challenge: | Existing studies have reported superiority of relative PEs in translation tasks. |
| Approach: | They analyze in which part of a transformer model PEs work and compare them using experiments . they find that relative PEs should be added only to query and key of attention mechanism . |
| Outcome: | The results show that relative and absolute PEs work in a transformer model, and should be added to the query and key of an attention mechanism, not to the value. |
Unicode Normalization and Grapheme Parsing of Indic Languages (2024.lrec-main)
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Nazmuddoha Ansary, Quazi Adibur Rahman Adib, Tahsin Reasat, Asif Shahriyar Sushmit, Ahmed Imtiaz Humayun, Sazia Mehnaz, Kanij Fatema, Mohammad Mamun Or Rashid, Farig Sadeque
| Challenge: | Indic writing systems encode words as linear sequences of Unicode characters . authors propose a grapheme parser for Abugida text to normalize inconsistencies . |
| Approach: | They propose a normalizer for normalizing inconsistencies caused by Unicode encoding schemes . grapheme parser for Abugida deconstructs words into visually distinct orthographic syllables . |
| Outcome: | The proposed library is more efficient than the previously used IndicNLP normalizer . it deconstructs words into visually distinct orthographic syllables or complex graphemes . |
Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval (2024.lrec-main)
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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for video-text retrieval capture fine-grained semantic concepts . however, they lack the ability to capture finer-grain concepts such as objects and actions. |
| Approach: | They propose a dual-encoder architecture for fast video-text retrieval that learns lexicon representations to capture fine-grained semantics. |
| Outcome: | The proposed framework outperforms existing methods with 4.8% and 8.2% improvement on MSR-VTT and DiDeMo respectively. |
UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt (2024.lrec-main)
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| Challenge: | Recent studies have shown that multi-task instruction tuning after pre-training greatly improves the model’s robustness and transfer ability, which is crucial for building a high-quality dialog system. |
| Approach: | They propose to use Task-aware Automatic Prompt generation (TAP) to automatically generate high-quality prompts from 15 dialog-related tasks. |
| Outcome: | The proposed model is robust to input prompts and capable of various dialog-related tasks. |
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding (2024.lrec-main)
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| Challenge: | Existing studies rely on shallow unsupervised data generated by token surface matching regardless of global context-aware semantics of the surrounding text tokens. |
| Approach: | They propose an Unsupervised Pseudo Semantic Data Augmentation mechanism to enrich training data without human intervention. |
| Outcome: | The proposed model improves on general zero-shot cross-lingual understanding tasks on different languages without human intervention. |
UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval (2024.lrec-main)
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| Challenge: | Existing methods for retrieving information from a large corpus of data are sub-optimal and low efficiency. |
| Approach: | They propose a multi-task framework that functions as a universal retriever for three dominant retrieval tasks during the conversation. |
| Outcome: | The proposed framework can perform persona selection, knowledge selection, and response selection tasks simultaneously. |
Universal Anaphora: The First Three Years (2024.lrec-main)
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Massimo Poesio, Maciej Ogrodniczuk, Vincent Ng, Sameer Pradhan, Juntao Yu, Nafise Sadat Moosavi, Silviu Paun, Amir Zeldes, Anna Nedoluzhko, Michal Novák, Martin Popel, Zdeněk Žabokrtský, Daniel Zeman
| Challenge: | Universal Anaphora initiative aims to push forward the state of the art in anaphora and anaphorism resolution by expanding the aspects of anaphonic interpretation which are or can be reliably annotated in an anagraphic corpora. |
| Approach: | They propose to develop a standard for anaphoric annotations and a method for evaluating models that can carry out this type of interpretation. |
| Outcome: | The Universal Anaphora initiative aims to push forward the state of the art in anaphora and anaphorism resolution by producing unified standards to annotate and encode annotations, delivering datasets encoded according to these standards, and developing methods for evaluating models that carry out this type of interpretation. |
Universal Dependencies: Extensions for Modern and Historical German (2024.lrec-main)
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| Challenge: | a new UD treebank is being developed for Middle High German annotations . the annotation scheme is inconsistent with other treebanks for this period . |
| Approach: | They propose to extend the UD scheme for modern and historical German by a range of tokens . they propose to use a treebank that is the first UD treebank for Middle High German . |
| Outcome: | The proposed extensions relate in part to differences between arguments and modifiers . the proposed treebank is the first UD treebank for Middle High German . |
Universal Dependencies for Learner Russian (2024.lrec-main)
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| Challenge: | a pilot study of Russian learner data with syntactic dependency relations is presented . a focus of recent work in the NLP community has been on grammar errors . |
| Approach: | They propose to annotate Russian learner data with syntactic dependency relations using a subset of sentences from two error-corrected Russian learners. |
| Outcome: | The proposed annotations are performed on a subset of Russian learner datasets. |
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing MMKGC methods overlook the imbalance problem of modality information among entities, resulting in inadequate modal fusion and inefficient utilization of the raw modality. |
| Approach: | They propose to integrate structural, visual, and textual information of entities into the discriminant models to predict the missing triples. |
| Outcome: | The proposed model outperforms 19 recent methods and achieves state-of-the-art results on three public MMKGC benchmarks. |
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction (2024.lrec-main)
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| Challenge: | Existing methods for relational triple extraction (RTE) are unnatural and recast RTE tasks to text-to-text prompting formats. |
| Approach: | They propose a tabular prompting for RTE which frames RTE task into a table generation task and propose an instructive in-context learning which only selects and annotates samples considering triple semantics in massive unlabeled samples. |
| Outcome: | The proposed prompting for RTE with TableIE achieves state-of-the-art performance compared to other methods . the proposed prompts are based on off-the shelf LLMs and are scalable to multiple scenarios . |
Unmasking Biases: Exploring Gender Bias in English-Catalan Machine Translation through Tokenization Analysis and Novel Dataset (2024.lrec-main)
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| Challenge: | a new dataset focuses on gender-neutral terms that necessitate gendered translations in Catalan. |
| Approach: | They propose to use a new dataset to evaluate gender bias in machine translation . they train four MT systems using different tokenization techniques . |
| Outcome: | The proposed dataset focuses on gender-neutral terms necessitating gendered translations in Catalan. |
Unpacking Bias: An Empirical Study of Bias Measurement Metrics, Mitigation Algorithms, and Their Interactions (2024.lrec-main)
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| Challenge: | Word embeddings (WE) models reflect gender, racial, and religious stereotypes from the corpus on which they are trained. |
| Approach: | They propose a method that carefully controls for word sets and vector normalization to address these factors. |
| Outcome: | The proposed method detects consistency between different mitigation methods and the evaluation words used by the mitigation methods. |
Unraveling Spontaneous Speech Dimensions for Cross-Corpus ASR System Evaluation for French (2024.lrec-main)
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| Challenge: | 'spontaneous speech' is a catch-all term used for situations like speaking with a friend, being interviewed on radio/TV or giving a lecture. |
| Approach: | They propose to use four dimensions to describe spontaneous speech variation in automatic speech recognition systems. |
| Outcome: | The proposed system can be used to predict the WER of speech recognition systems on face-to-face interactions. |
Unsupervised Grouping of Public Procurement Similar Items: Which Text Representation Should I Use? (2024.lrec-main)
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Pedro P. V. Brum, Mariana O. Silva, Gabriel P. Oliveira, Lucas G. L. Costa, Anisio Lacerda, Gisele Pappa
| Challenge: | establishing reference prices is essential to guide competitors in setting product prices . however, selecting an appropriate representation for text is challenging . |
| Approach: | They propose a framework for text cleaning, extraction, and representation based on sentence representations for public procurement item descriptions. |
| Outcome: | The proposed approach captures the most important components of item descriptions. |
Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models (2024.lrec-main)
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| Challenge: | Existing studies have explained to what extent LLMs extract conflicting knowledge from the provided text, but they neglect the necessity to reason with conflicting information. |
| Approach: | They construct a dataset for knowledge conflict resolution examination in the form of question answering that divides reasoning with conflicting knowledge into three levels. |
| Outcome: | The proposed dataset enables analysis of reasoning with conflicting knowledge in the form of question answering. |
Unveiling Project-Specific Bias in Neural Code Models (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) based neural code models struggle to generalize effectively to real-world inter-project out-of-distribution data. |
| Approach: | They propose a Cond-Idf measurement to measure the relatedness of a token with a label and its project-specificness. |
| Outcome: | The proposed framework improves both inter-project OOD generalization and adversarial robustness while not sacrificing accuracy on intra-project IID data. |
Unveiling Strengths and Weaknesses of NLP Systems Based on a Rich Evaluation Corpus: The Case of NER in French (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) is an applicative task for which annotation schemes vary . a lack of robustness of some tools towards textual variation limits evaluation . |
| Approach: | They propose a gold corpus for french annotated with a rich tagset that enables comparison with multiple annotation schemes. |
| Outcome: | The proposed framework enables a fair comparison of NER systems across textual genres and annotation schemes. |
Unveiling Vulnerability of Self-Attention (2024.lrec-main)
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| Challenge: | Existing studies focus on manipulating word inputs, but they lack generalization to versatile real-world attacks. |
| Approach: | They propose a powerful perturbation technique which perturbs the attention scores within the SA matrices via meticulously crafted attention masks. |
| Outcome: | The proposed perturbation technique achieves high attack success rate (98%) and low cost. |
UQA: Corpus for Urdu Question Answering (2024.lrec-main)
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| Challenge: | Urdu is a low-resource language with over 70 million native speakers . expanding the reach of NLP to languages other than English is crucial for advancing multilingual AI systems. |
| Approach: | They introduce a novel dataset for question answering and text comprehension in Urdu . they use a technique called EATS which preserves the answer spans in translated context paragraphs . |
| Outcome: | The proposed dataset preserves answer spans in translated context paragraphs. |
UrduMASD: A Multimodal Abstractive Summarization Dataset for Urdu (2024.lrec-main)
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| Challenge: | a surge of multimodal content on social media has transformed our methods of communication and information exchange. |
| Approach: | They propose a video-based Urdu multimodal abstractive text summarization dataset . it uses a variety of evaluation metrics to ensure the quality of the dataset amounted to a high quality one . |
| Outcome: | The proposed dataset surpasses existing datasets on key quality metrics. |
User Guide for KOTE: Korean Online That-gul Emotions Dataset (2024.lrec-main)
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| Challenge: | sentiment analysis is used to identify emotional aspects of texts but is limited by its small size and limited range of emotions. |
| Approach: | They propose a Korean sentiment analysis corpus that is limited by its small size and narrow range of emotions . they propose to fine-tune the KOTE dataset and analyze the results for social discrimination . |
| Outcome: | The proposed dataset includes 50,000 Korean online comments, each manually labeled for 43 emotions and NO EMOTION. |
Using Bibliodata LODification to Create Metadata-Enriched Literary Corpora in Line with FAIR Principles (2024.lrec-main)
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| Challenge: | Literature corpus building is relatively nascent, and standardized procedures for curating literary corpora are not yet developed. |
| Approach: | They propose a workflow for the creation and reuse of literary corpora using a metadata-enriched Polish Novel Corpus from the 19th and 20th centuries. |
| Outcome: | The proposed workflow includes a multi-stage metadata enrichment and verification process and efficient data collection and data sharing according to the FAIR principles and 5- and 7-star data standards. |
Using Persuasive Writing Strategies to Explain and Detect Health Misinformation (2024.lrec-main)
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| Challenge: | Increasing misinformation has led to a decrease in trust in news organizations and a decline in the health and medical industry. |
| Approach: | They propose a novel annotation scheme that incorporates persuasive writing tactics in textual documents to aid the automatic identification of misinformation. |
| Outcome: | The proposed scheme improves accuracy and explainability of misinformation detection models. |
Using Pre-Trained Language Models in an End-to-End Pipeline for Antithesis Detection (2024.lrec-main)
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| Challenge: | Rhetorical figures are a "departure from the normal usage" of language . features of metaphors, irony and sarcasm enhance performance of several NLP tasks. |
| Approach: | They propose a pipeline approach to detect rhetorical figures using large language models by splitting text into phrases and identifying parallel phrases with a syntactically parallel structure. |
| Outcome: | The proposed approach outperforms state-of-the-art methods by an F1 score of 65.11 %. |
Using Speech Technology to Test Theories of Phonetic and Phonological Typology (2024.lrec-main)
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| Challenge: | acoustic studies show that obstruents in European Portuguese have different voicing profiles than their Romance relatives. |
| Approach: | They propose to use speech technology to test phonetic typology in European Portuguese . they use acoustic phone models to force align different phone models for obstruents . |
| Outcome: | The proposed method supports previous accounts that European Portuguese is diverging from the traditional voicing system known for Romance languages towards a hybrid system where stops and fricatives are specified for different voicing features. |
Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification (2024.lrec-main)
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| Challenge: | Hierarchical text classification (HTC) is a challenging subtask due to its complex taxonomic structure. |
| Approach: | They propose a local hierarchy framework that can fit in nearly all HTC models and optimize them with the local hierarchy as auxiliary information. |
| Outcome: | The proposed framework is effective in all scenarios and is adept at dealing with complex taxonomic hierarchies. |
Utilizing Longer Context than Speech Bubbles in Automated Manga Translation (2024.lrec-main)
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Hiroto Kaino, Soichiro Sugihara, Tomoyuki Kajiwara, Takashi Ninomiya, Joshua B. Tanner, Shonosuke Ishiwatari
| Challenge: | Existing methods to capture contextual information for manga machine translation are difficult to perform . unofficially translated pirated copies of manga are circulating overseas in large numbers . |
| Approach: | They propose two new ways to capture broader contextual information in manga machine translation . scene-based translation considers previous scene and broader context information . detailed analysis reveals the effect of zero-anaphora resolution in translation - highlighting the usefulness of longer contextual information if manga is translated in Japanese . |
| Outcome: | The proposed methods improve translation quality for manga (Japanese-style comics) the results show that the combined methods achieve the highest quality. |
UzbekVerbDetection: Rule-based Detection of Verbs in Uzbek Texts (2024.lrec-main)
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| Challenge: | Verb detection is a fundamental task in natural language processing that involves identifying the action or state expressed by a verb in a sentence. |
| Approach: | They propose a rule-based approach for verb detection in Uzbek texts based on affixes/suffixed rules. |
| Outcome: | The proposed method outperforms existing methods on a dataset of Uzbek texts and has an F1 score of 0.97. |
Validating and Exploring Large Geographic Corpora (2024.lrec-main)
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| Challenge: | a paper examines the impact of corpus creation decisions on multi-lingual web corpora . the goal is to understand the impact on downstream corporata with a focus on under-represented languages and populations. |
| Approach: | This paper evaluates the impact of corpus creation decisions on multi-lingual web corpora . three cleaning methods are used to improve the quality of sub-corpora in the common crawl . the goal is to understand the impact on downstream corporan with a focus on under-represented languages . |
| Outcome: | The results show that the validity of sub-corpora is improved with each stage of cleaning but that this improvement is unevenly distributed across languages and populations. |
Verbing Weirds Language (Models): Evaluation of English Zero-Derivation in Five LLMs (2024.lrec-main)
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| Challenge: | Lexical-syntactic flexibility is a hallmark of English morphology . conversion involves placing a word with one part of speech in a non-prototypical context . |
| Approach: | They propose to test lexical-syntactic flexibility in the form of conversion . conversion is a process where a word with one part of speech is placed in a non-prototypical context . |
| Outcome: | The proposed task tests the ability of five language models to generalize over words with a non-prototypical part of speech. |
VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain (2024.lrec-main)
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| Challenge: | Currently, there are no publicly available speech recognition datasets in the medical domain due to privacy restrictions. |
| Approach: | They present a Vietnamese speech recognition dataset in the medical domain comprising 16h of labeled medical speech, 1000h of unlabeled medical and 1200h of general-domain speech. |
| Outcome: | The proposed model outperforms state-of-the-art models from 51.8% to 29.6% WER on test set. |
VI-OOD: A Unified Framework of Representation Learning for Textual Out-of-distribution Detection (2024.lrec-main)
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| Challenge: | Out-of-distribution (OOD) detection is a crucial part of deep neural networks. |
| Approach: | They propose a variational inference framework which maximizes the likelihood of the joint distribution p(x, y) instead of p[y|x). |
| Outcome: | The proposed framework maximizes the likelihood of the joint distribution p(x, y) instead of p[y|x). |
Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)
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| Challenge: | Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities. |
| Approach: | They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions. |
| Outcome: | The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset. |
Visual-Textual Entailment with Quantities Using Model Checking and Knowledge Injection (2024.lrec-main)
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| Challenge: | Visual-textual entailment (VTE) is a critical task in multimodal inference. |
| Approach: | They propose a visual-textual entailment system that solves VTE tasks with quantities and negation. |
| Outcome: | The proposed system solves visual-textual entailment tasks with quantities and negation more robustly than previous approaches. |
Vygotsky Distance: Measure for Benchmark Task Similarity (2024.lrec-main)
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| Challenge: | GLUE, SuperGLUE and RussianSuperGLUE benchmarks are arbitrary sets of tasks that are not generalized. |
| Approach: | They propose a theoretical instrument and an algorithm to calculate similarity between benchmark tasks . they use relative performance of the "students" on a given task to determine similarity . |
| Outcome: | The proposed model reduces the number of evaluation tasks while maintaining high validation quality. |
WaCadie: Towards an Acadian French Corpus (2024.lrec-main)
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| Challenge: | Existing corpora do not exist for many languages and language varieties, such as Acadian French. |
| Approach: | They propose to build a corpus of Acadian French using web-as-corpus methodologies . they use domain crawling, social media scraping, and search engines to create corpus . |
| Outcome: | The proposed corpus includes some traces of Acadian French, but it is not available for many languages and language varieties, such as Acadinian French. |
Well Begun Is Half Done: An Implicitly Augmented Generative Framework with Distribution Modification for Hierarchical Text Classification (2024.lrec-main)
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| Challenge: | Hierarchical Text Classification (HTC) is a challenging task which aims to extract the labels in a tree structure corresponding to a given text. |
| Approach: | They propose an explicit-agmented-generativ-e framework with distribution modification for hierarchical text classification. |
| Outcome: | The proposed framework improves on the initial distributions of tail classes and avoids misinterpreting predictions on unbalanced data. |
What Are the Implications of Your Question? Non-Information Seeking Question-Type Identification in CNN Transcripts (2024.lrec-main)
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| Challenge: | Non-information seeking questions capture subtle dynamics of human discourse . authors use dataset of over 1,500 information-seeking questions and NISQs as benchmark . |
| Approach: | They use a dataset of over 1,500 information-seeking question(ISQ) and NISQ to evaluate human and machine performance on classifying fine-grained NISq types. |
| Outcome: | The proposed corpus is the first publicly available for annotation of non-information seeking questions . it evaluates human and machine performance on classifying fine-grained questions based on models . |
What Can Diachronic Contexts and Topics Tell Us about the Present-Day Compositionality of English Noun Compounds? (2024.lrec-main)
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| Challenge: | Existing methods to determine the semantic relatedness between compounds and constituents have applied a synchronic perspective, but this study examines what diachronic changes in contexts and semantic topics reveal about the compounds’ present-day compositionality. |
| Approach: | They propose to use two diachronic vector spaces to model compositional patterns between compounds with low and high present-day compositionality. |
| Outcome: | The proposed model performs on par with co-occurrence space and captures similar information. |
What Do Transformers Know about Government? (2024.lrec-main)
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Jue Hou, Anisia Katinskaia, Lari Kotilainen, Sathianpong Trangcasanchai, Anh-Duc Vu, Roman Yangarber
| Challenge: | Currently, data is lacking for the research community working on grammatical constructions, and government in particular. |
| Approach: | They use transformer language models to study how government relations are encoded . they use morphologically rich languages to train a classifier capable of discovering new types of government . |
| Outcome: | The proposed classifiers can learn new types of government, the authors show . they find that the classifier can learn government relations in two languages . |
What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)
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Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji
| Challenge: | Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields. |
| Approach: | They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies. |
| Outcome: | The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges. |
What Happens to a Dataset Transformed by a Projection-based Concept Removal Method? (2024.lrec-main)
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| Challenge: | A number of recent methods have used linear projections to remove information about a concept from a language representation. |
| Approach: | They propose to use linear projections to remove a concept from a language representation to create a transformed set of word embeddings. |
| Outcome: | The proposed methods inject strong statistical dependencies into the transformed datasets. |
What Has LeBenchmark Learnt about French Syntax? (2024.lrec-main)
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| Challenge: | Pretrained acoustic models are increasingly used for downstream speech tasks such as automatic speech recognition, speech translation, spoken language understanding or speech parsing. |
| Approach: | They propose to probing a pretrained acoustic model for French for syntactic information using the Orféo treebank. |
| Outcome: | The proposed model is trained on 7k hours of spoken French and obtained reasonable results on tasks that require higher level linguistic knowledge. |
What Is Needed for Intra-document Disambiguation of Math Identifiers? (2024.lrec-main)
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| Challenge: | Ambiguity in math identifiers within a document poses significant challenges to understanding formulae . ambiguity in mathematical expressions can be difficult to disambiguate, requiring intra-document disambiguation . |
| Approach: | They propose to use position data and local formula structure to disambiguate math identifiers . they train a model that performs similarly to humans with an 85% accuracy . |
| Outcome: | The proposed model outperforms rule-based models in natural language processing. |
When Argumentation Meets Cohesion: Enhancing Automatic Feedback in Student Writing (2024.lrec-main)
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| Challenge: | Argumentative essays require a high degree of cohesion, defined as a network of semantic relationships that link together. |
| Approach: | They investigate the role of arguments in the automatic scoring of cohesion in argumentative essays. |
| Outcome: | The proposed model improves on a multi-task learning process by adding argumentative elements as an auxiliary task. |
When Cohesion Lies in the Embedding Space: Embedding-Based Reference-Free Metrics for Topic Segmentation (2024.lrec-main)
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| Challenge: | Recent advances in topic segmentation have led to a surge in interest in reference-free metrics, designed to score a hypothesised segmentation of a document without the need to refer to any expert annotation. |
| Approach: | They propose a common framework for reference-free topic segmentation metrics and a new method for the embedding space. |
| Outcome: | The proposed framework outperforms existing metrics based on human annotations while allowing for conversational data to outperformed other metrics. |
When Do “More Contexts” Help with Sarcasm Recognition? (2024.lrec-main)
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| Challenge: | Prior work has focused on integrating more contexts into sarcasm recognition models, but no systematic evaluation of their collective effectiveness has been conducted. |
| Approach: | They propose a framework to integrate multiple contextual cues into a model and test different approaches. |
| Outcome: | The proposed framework achieves state-of-the-art performance and also shows the benefits of sequentially adding more contexts. |
When Your Cousin Has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages (2024.lrec-main)
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| Challenge: | Existing methods for unsupervised bilingual lexicon induction depend on good quality static or contextual embeddings for both languages. |
| Approach: | They propose a method for unsupervised bilingual lexicon induction between a related LRL and a high-resource language that only requires inference on a masked language model of the HRL. |
| Outcome: | The proposed method performs well on low-resource languages with 5M tokens against Hindi . it is compared with existing methods on (mid-resourced) Marathi and Nepali . |
Which Sense Dominates Multisensory Semantic Understanding? A Brain Decoding Study (2024.lrec-main)
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| Challenge: | Decoding semantic meanings from brain activity is open to multisensory stimulation, as word meanings can be delivered by both auditory and visual inputs. |
| Approach: | They aim to develop a computational model to probing what information from the act of language understanding is represented in human brain. |
| Outcome: | The proposed model dissociates multisensory integration of word understanding into written text, spoken text and image perception respectively, exploring the decoding efficiency and reliability of unisensory information in the brain representation. |
Who Did You Blame When Your Project Failed? Designing a Corpus for Presupposition Generation in Cross-Examination Dialogues (2024.lrec-main)
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| Challenge: | Existing models for presupposition generation fail to generate complete lists of presuffpositions. |
| Approach: | They propose to fine-tune existing BERT and T5 models for a task where a model produces a list of presuppositions carried by the given input sentence. |
| Outcome: | The proposed models outperform BERT and T5 models on the novel task of presupposition as natural language inference (PNLI) despite limited data, they achieved an emerging proficiency in generation of presumptions reaching ROUGE scores of 43.47, adhering to systematic patterns that mirror valid strategies for pres upposition generation, although failed to generate the complete lists. |
Who Is Bragging More Online? A Large Scale Analysis of Bragging in Social Media (2024.lrec-main)
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| Challenge: | Social media is a natural platform for users to use bragging to gain admiration, respect, attention and followers from their audiences. |
| Approach: | They employ computational sociolinguistics methods to conduct the first large scale study of bragging behavior on Twitter by focusing on its overall prevalence, temporal dynamics and impact of demographic factors. |
| Outcome: | The proposed study shows that the prevalence of bragging decreases over time within the same population of users and younger, more educated and popular users in the U.S. are more likely to brag. |
Who Said What: Formalization and Benchmarks for the Task of Quote Attribution (2024.lrec-main)
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| Challenge: | Existing methods for quote attribution are poorly understood, despite advances in research . previous approaches have used hand-crafted features to identify speaker names . |
| Approach: | They formalize the task of quote attribution and establish a basis for comparison . they compare CEQA and ChatGPT models on available datasets in both English and Chinese . |
| Outcome: | The proposed model outperforms all supervised methods on English and Chinese datasets. |
Why Voice Biomarkers of Psychiatric Disorders Are Not Used in Clinical Practice? Deconstructing the Myth of the Need for Objective Diagnosis (2024.lrec-main)
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| Challenge: | Anxiety and depression are the most prevalent mental disorders, affecting 3.9% and 3.6% of the world's population . |
| Approach: | They propose to shift the estimation of diagnoses towards estimation of clinical symptoms and signs, which address the limitations raised against diagnosis estimation. |
| Outcome: | The proposed paradigm shift will empower the use of vocal biomarkers in clinical practice. |
WikiFactDiff: A Large, Realistic, and Temporally Adaptable Dataset for Atomic Factual Knowledge Update in Causal Language Models (2024.lrec-main)
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| Challenge: | Factual update is a task of inserting, replacing, or removing facts in large language models. |
| Approach: | They present a dataset that describes the evolution of factual knowledge between two dates as a collection of simple facts divided into three categories: new, obsolete, and static. |
| Outcome: | The proposed dataset compares the state of the Wikidata knowledge base at 4 January 2021 and 27 February 2023. |
WikiSplit++: Easy Data Refinement for Split and Rephrase (2024.lrec-main)
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| Challenge: | Existing text simplification methods rely on encoder-decoder models to achieve this task. |
| Approach: | They propose a text-to-text generation approach that applies encoder-decoder models to a large-scale dataset to improve Split and Rephrase. |
| Outcome: | The proposed approach improves Split and Rephrase readability and performance on large datasets, but still suffers from hallucinations and under-splitting. |
Willkommens-Merkel, Chaos-Johnson, and Tore-Klose: Modeling the Evaluative Meaning of German Personal Name Compounds (2024.lrec-main)
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Annerose Eichel, Tana Deeg, Andre Blessing, Milena Belosevic, Sabine Arndt-Lappe, Sabine Schulte im Walde
| Challenge: | Personal name compounds (PNCs) are compositions that refer to a person, such as Willkommens-Merkel ('Welcome-Meerkel') and a personal name such as Merkel. |
| Approach: | They propose to model 321 personal name compounds and their corresponding full names at discourse level and compare two approaches to assess whether a PNC is more positively or negatively evaluative . they further enrich data with personal, domain-specific, and extra-linguistic information and perform regression analyses revealing that factors including compound and modifier valence, domain, and political party membership influence how a pnc is evaluated. |
| Outcome: | The proposed model shows that the PNCs are perceived as more positively or negatively than their full name and that they are perceived to be more positive or negative. |
WkNER: Enhancing Named Entity Recognition with Word Segmentation Constraints and kNN Retrieval (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) tasks require detecting the span and category of the entity from the text block. |
| Approach: | They propose a kNN retrieval enhancement algorithm that incorporates word segmentation information to enhance the model’s generalization ability and alleviate the problem of missing entity tokens in prediction. |
| Outcome: | The proposed method improves the performance of baseline models and achieves better or compared recognition accuracy than previous state-of-the-art models in multiple public Chinese and English datasets. |
Word-Aware Modality Stimulation for Multimodal Fusion (2024.lrec-main)
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| Challenge: | Multimodal learning is expected to make more accurate predictions than text-only analysis. |
| Approach: | They propose a method for fusing multimodal inputs with text-based fusion methods . they propose fusion that integrates non-verbal modalities with text . |
| Outcome: | The proposed method improves sentiment prediction by using non-verbal modalities with text . the proposed method is unsuitable for applying attention to text modality in the fusion phase . |
Word-level Commonsense Knowledge Selection for Event Detection (2024.lrec-main)
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| Challenge: | Event Detection (ED) is a task of automatically extracting multi-class trigger words . Xie and Tu, 2022, use a Context-specific Knowledge Selector to select commonsense knowledge of words based on living contexts . |
| Approach: | They use a Context-specific Knowledge Selector to select the exact commonsense knowledge of words from a large knowledge base. |
| Outcome: | The proposed approach achieves the F1-score of about 78.3% on the ACE-2005 dataset. |
WordNet under Scrutiny: Dictionary Examples in the Era of Large Language Models (2024.lrec-main)
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| Challenge: | Lexical resources are a repository of knowledge and are used for many tasks, including word sense disambiguation and etymology. |
| Approach: | They compare WordNet, the most commonly used lexical resource in NLP, with a variety of dictionaries and examples that were generated by ChatGPT. |
| Outcome: | The most commonly used lexical resource in NLP, with a variety of dictionaries and examples that were generated by ChatGPT. |
WorldValuesBench: A Large-Scale Benchmark Dataset for Multi-Cultural Value Awareness of Language Models (2024.lrec-main)
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| Challenge: | a global dataset for multi-cultural value prediction task is lacking in the computer science community . a multi-culture awareness of LMs is critical to generating safe and personalized responses . |
| Approach: | They present a global multi-cultural value prediction task using a world value survey dataset . they construct more than 20 million examples of the type "(demographic attributes, value question) answer" they show that the task is challenging for strong open and closed-source models . |
| Outcome: | The proposed model can generate a rating response to a value question based on demographic contexts on 11.1%, 25.0%, 72.2%, and 75.0% of the questions. |
Would You Like to Make a Donation? A Dialogue System to Persuade You to Donate (2024.lrec-main)
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| Challenge: | Persuasive automated dialogue systems are a popular way to influence people's behavior and decision making. |
| Approach: | They propose to use a context-aware persuasion strategy selection module to persult users . they also propose a persuasiveness prediction model to automatically evaluate the persuasiveness of generated text. |
| Outcome: | The proposed system can achieve better performance on several automated evaluation metrics than baseline models. |
WW-CSL: A New Dataset for Word-Based Wearable Chinese Sign Language Detection (2024.lrec-main)
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| Challenge: | Sign language is an effective non-verbal communication mode for the hearingimpaired people. |
| Approach: | They propose a three-form scheme to represent dynamic CSL gestures using a word-based dataset. |
| Outcome: | The proposed framework integrates the local sequential sensor data derived from the wearable-based CSL gestures with the global, fine-grained skeleton representations captured from video-based gestures simultaneously. |
XAI-Attack: Utilizing Explainable AI to Find Incorrectly Learned Patterns for Black-Box Adversarial Example Creation (2024.lrec-main)
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| Challenge: | Adversarial examples can be used to trick machine learning models into making erroneous predictions, causing poorer insights and lower confidence in the information gathered. |
| Approach: | They propose a textual adversarial example method that identifies falsely learned word indicators by leveraging explainable AI methods as importance functions on incorrectly predicted instances. |
| Outcome: | The proposed method outperforms existing examples and training methods and shows baseline improvements of up to 23 percentage points on adversarial tasks. |
XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates (2024.lrec-main)
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| Challenge: | Existing text editing benchmark datasets contain coarse-grained instructions and lack explainability, resulting in outputs that deviate from intended changes. |
| Approach: | They propose a benchmark specifically designed for fine-grained instruction-based explainable text editing. |
| Outcome: | The proposed benchmark incorporates fine-grained instructions and gold-standard edit explanations. |
XVD: Cross-Vocabulary Differentiable Training for Generative Adversarial Attacks (2024.lrec-main)
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| Challenge: | Existing approaches to create adversarial examples using tokens are not sufficient to ensure other desirable properties such as similarity to non-adversarial examples, linguistic fluency, and so forth. |
| Approach: | They propose a method which leverages a set of pretrained language models to promote similarity to non-adversarial examples, linguistic fluency, and so forth. |
| Outcome: | The proposed approach outperforms existing methods and is competitive with token-based approaches. |
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)
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Karen Fort, Laura Alonso Alemany, Luciana Benotti, Julien Bezançon, Claudia Borg, Marthese Borg, Yongjian Chen, Fanny Ducel, Yoann Dupont, Guido Ivetta, Zhijian Li, Margot Mieskes, Marco Naguib, Yuyan Qian, Matteo Radaelli, Wolfgang S. Schmeisser-Nieto, Emma Raimundo Schulz, Thiziri Saci, Sarah Saidi, Javier Torroba Marchante, Shilin Xie, Sergio E. Zanotto, Aurélie Névéol
| Challenge: | Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America. |
| Approach: | They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts. |
| Outcome: | The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories . |
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus (2024.lrec-main)
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| Challenge: | a corpus of multilingual Arabic-English speech is presented in a new paper . a major bottleneck is the lack of data needed for training NLP models . |
| Approach: | They propose a multilingual multidialectal Arabic-English speech corpus with a set of guidelines for automatic speech recognition. |
| Outcome: | The proposed corpus includes two languages with Arabic and English spoken in multiple variants and Arabic and Arabic with various accents. |
ZeLa: Advancing Zero-Shot Multilingual Semantic Parsing with Large Language Models and Chain-of-Thought Strategies (2024.lrec-main)
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| Challenge: | Existing approaches to augment multilingual datasets with labeled English data are lacking in annotated data. |
| Approach: | They propose a framework to augment English data and then use it to train parsers . they propose to use multilingual chain-of-thought prompting techniques to augment other languages' data . |
| Outcome: | The proposed framework augments English data in other languages and trains them with no demonstration samples in target languages. |
ZenPropaganda: A Comprehensive Study on Identifying Propaganda Techniques in Russian Coronavirus-Related Media (2024.lrec-main)
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| Challenge: | a new classification scheme for automatic detection of propaganda techniques is proposed . the capabilities of algorithms increase the risks of propaganda impact on the audience . |
| Approach: | They propose a novel multi-level classification scheme for automatic detection of propaganda techniques. |
| Outcome: | The proposed classification scheme outperforms existing methods in a Russian dataset and provides a valuable resource for future research. |
Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis (2024.lrec-main)
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Md. Arid Hasan, Shudipta Das, Afiyat Anjum, Firoj Alam, Anika Anjum, Avijit Sarker, Sheak Rashed Haider Noori
| Challenge: | Recent performance of Large Language Models (LLMs) in low-resource languages is under-researched due to resource constraints. |
| Approach: | They present a manually annotated dataset encompassing 33,606 Bangla tweets and Facebook comments. |
| Outcome: | The proposed model outperforms other models even in zero and few-shot scenarios. |
Zero-shot Cross-lingual Automated Essay Scoring (2024.lrec-main)
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| Challenge: | Existing approaches to automate essay scoring (AES) use pre-trained multilingual representations and writing quality alignment to score essays in unseen languages. |
| Approach: | They propose a novel cross-lingual scoring method using pretrained multilingual representation and writing quality alignment to represent multilingual essays. |
| Outcome: | The proposed method achieves state-of-the-art cross-lingual scoring performance. |
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning (2024.lrec-main)
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| Challenge: | Existing studies focus on sentence-level ECI with high-resource languages, leaving document-level DECI with low-resourced languages under-explored. |
| Approach: | They propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning for zero-shot cross-lingual ECI. |
| Outcome: | The proposed model outperforms the state-of-the-art model on monolingual and multilingual scenarios by 9.4% and 8.2% of average F1 score. |
Zero-shot Event Detection Using a Textual Entailment Model as an Enhanced Annotator (2024.lrec-main)
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| Challenge: | Recent work proposed to use a pre-trained textual entailment model for event detection . but, those methods treated the TE model as a frozen annotator . |
| Approach: | They propose to use TE models to annotate large-scale unlabeled text and annotated data to fine-tune the TE model. |
| Outcome: | The proposed method outperforms baseline methods by 15% on the ACE05 dataset. |
Zero-shot Learning for Multilingual Discourse Relation Classification (2024.lrec-main)
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| Challenge: | Discourse analysis is a hard task, but data is limited for other languages. |
| Approach: | They propose to use zero-shot learning to combine discourse relation data . they compare two versions of the same text with different labels . |
| Outcome: | The proposed method can be applied to languages, frameworks, or similarity measures. |
Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods. |
| Approach: | They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs. |
| Outcome: | The proposed framework can be built by directly prompting LLMs to understand user needs without training data. |