Proceedings of the Thirteenth Language Resources and Evaluation Conference
Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet (2022.lrec-1)
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| Challenge: | Neural models have been advancing in a myriad of tasks, but there is a lack of large training data. |
| Approach: | They propose a method for domain adaptation of Neural Machine Translation systems using a multilingual FrameNet enriched with qualia relations as an external knowledge base. |
| Outcome: | The proposed system outperforms the state-of-the-art commercial system in an experiment . the proposed system substitutes domain-specific terms in the source language by their adequate translation in the target language. |
HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation (2022.lrec-1)
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| Challenge: | Existing automated evaluation metrics for machine translation are expensive and lack inter-rater reliability. |
| Approach: | They propose a task-oriented and human-centric evaluation framework for machine translation output based on professional post-e diting annotations. |
| Outcome: | The proposed framework improves translation quality and system performance and transparency . it is cost-effective, easy to use and faster to implement . |
Priming Ancient Korean Neural Machine Translation (2022.lrec-1)
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| Challenge: | Recent studies have focused on the restoration and translation of historical languages. |
| Approach: | They propose to use two different stimuli to priming ancient-Korean NMT . they confirm the possibility of developing a human-centric model based on cognitive science . |
| Outcome: | The proposed model can be used to translate historical Korean documents using neural machine translation. |
GECO-MT: The Ghent Eye-tracking Corpus of Machine Translation (2022.lrec-1)
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| Challenge: | Despite improvements in machine translation output, remarkable differences can be observed when comparing machine translations (MT) and human translations. |
| Approach: | They describe a corpus of eye movement data collected during natural reading of a human translation and a machine translation of . they use this corpus to investigate the effect of machine translation on the reading process and the effects of various error types on reading. |
| Outcome: | The proposed corpus will be used in future research to investigate the effect of machine translation on the reading process and the effects of various error types on reading. |
Introducing Frege to Fillmore: A FrameNet Dataset that Captures both Sense and Reference (2022.lrec-1)
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| Challenge: | a widely supported claim in the fields of semantics and philosophy is that meaning arises from the combination of sense and reference. |
| Approach: | They propose a tool that facilitates both referential- and frame annotations of language-independent corpora. |
| Outcome: | The Dutch FrameNet annotation tool facilitates both referential- and frame annotations of language-independent corpora. |
Compiling a Suitable Level of Sense Granularity in a Lexicon for AI Purposes: The Open Source COR Lexicon (2022.lrec-1)
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Bolette Pedersen, Nathalie Carmen Hau Sørensen, Sanni Nimb, Ida Flørke, Sussi Olsen, Thomas Troelsgård
| Challenge: | The central word register for Danish is an open source lexicon project for general AI purposes funded and initiated by the Danish Agency for Digitisation in 2020. |
| Approach: | They propose to use existing fine-grained sense inventory to compile a more AI-appropriate sense granularity level of the vocabulary. |
| Outcome: | The proposed lexical resource is based on the fine-grained sense inventory from Den Danske Ordbog (DDO) it is designed to be more practical and suitable for AI, omitting outdated language and slang, merging subtle and rare sub-senses with their main sense, disregarding sub-domains, etc. |
Sense and Sentiment (2022.lrec-1)
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| Challenge: | Existing sentiment lexicons and concept-based sentiment-tagged corpora are not accurate, and it is difficult to map sentiment scores accurately to different languages. |
| Approach: | They examine existing sentiment lexicons and sense-based sentiment-tagged corpora to find out how sense and concept-based semantic relations effect sentiment scores. |
| Outcome: | The proposed lexicon can be used to generate sentiment lexicos for English using the Open Multilingual Wordnet. |
Enriching Linguistic Representation in the Cantonese Wordnet and Building the New Cantonese Wordnet Corpus (2022.lrec-1)
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| Challenge: | Currently, our wordnet includes a little over 5,200 concepts and 16,300 senses . |
| Approach: | They propose to improve the Cantonese Wordnet by increasing the general coverage, adding functional categories, enriching verbal representations and creating the Cannese WordNet Corpus . |
| Outcome: | The new version includes a little over 5,200 concepts and 16,300 senses . |
ZAEBUC: An Annotated Arabic-English Bilingual Writer Corpus (2022.lrec-1)
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| Challenge: | ZAEBUC is an annotated Arabic-English bilingual writer corpus . it is a corpus of short essays written by first-year university students . |
| Approach: | They propose to use a standard Arabic-English bilingual writer corpus to match comparable texts written by the same writer on different occasions. |
| Outcome: | The ZAEBUC corpus is an annotated Arabic-English bilingual writer corpus by first-year university students at Zayed University in the United Arab Emirates. |
Turkish Universal Conceptual Cognitive Annotation (2022.lrec-1)
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| Challenge: | UCCA-annotated datasets have been released in English, French, and German . a semi-automatic annotation approach is used to annotate the datasets . |
| Approach: | They propose to use an external semantic parser to annotate Turkish sentences . they use the same parsers for evaluation purposes and conducted experiments . |
| Outcome: | The proposed dataset is the first UCCA-annotated Turkish dataset . the results show that the parser can improve on the initial annotations . |
Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources (2022.lrec-1)
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Tamás Váradi, Bence Nyéki, Svetla Koeva, Marko Tadić, Vanja Štefanec, Maciej Ogrodniczuk, Bartłomiej Nitoń, Piotr Pęzik, Verginica Barbu Mititelu, Elena Irimia, Maria Mitrofan, Dan Tufiș, Radovan Garabík, Simon Krek, Andraž Repar
| Challenge: | The CURLICAT CEF Telecom project aims to collect and deeply annotate a set of large corpora from selected domains. |
| Approach: | They present the results of the CURLICAT CEF Telecom project . they propose to collect and deeply annotate a set of large corpora from selected domains . |
| Outcome: | The CURLICAT CEF Telecom project provides a set of large corpora from selected domains . the corporatized corporates are tokenized, lemmatized and morphologically analysed . |
RU-ADEPT: Russian Anonymized Dataset with Eight Personality Traits (2022.lrec-1)
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C. Anton Rytting, Valerie Novak, James R. Hull, Victor M. Frank, Paul Rodrigues, Jarrett G. W. Lee, Laurel Miller-Sims
| Challenge: | Social media has provided a platform for many individuals to express themselves naturally and publicly, but most of the work in this area has focused on English and other Western European languages. |
| Approach: | They propose to use a Russian dataset to combine author trait data with social media content to find out how personality traits are manifested. |
| Outcome: | The proposed dataset is the first to associate demographic and personality trait data with Russian-language social media content and to a limited extent, the first publicly-available dataset of personality traits to author content across multiple social media platforms. |
CoQAR: Question Rewriting on CoQA (2022.lrec-1)
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| Challenge: | Existing systems that ask questions in a conversational context may have contextual dependencies that make the understanding difficult. |
| Approach: | They propose to rewrite questions into an out-of-context form to facilitate understanding . they propose to use this form to train and evaluate conversational question answering models . |
| Outcome: | The proposed model can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering. |
User Interest Modelling in Argumentative Dialogue Systems (2022.lrec-1)
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| Challenge: | Existing studies on user interest in dialogue systems depend on explicit user feedback. |
| Approach: | They propose a model to implicitly estimate user interest during argumentative dialogues based on semantically clustered data. |
| Outcome: | The proposed model achieves a classification accuracy of 74.9% and tested with different Artificial Neural Networks (ANN) which new argument would fit the user interest best. |
Every time I fire a conversational designer, the performance of the dialogue system goes down (2022.lrec-1)
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Giancarlo Xompero, Michele Mastromattei, Samir Salman, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto
| Challenge: | Incorporating handwritten domain scripts into neural-based task-oriented dialogue systems may be an effective way to reduce the need for large sets of annotated dialogues. |
| Approach: | They propose a system where domain scripts are coded in semi-logical rules and evaluated semi-logic rules produced by differently-skilled conversational designers. |
| Outcome: | The proposed system outperforms state-of-the-art systems when trained with smaller sets of annotated dialogues. |
An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasets (2022.lrec-1)
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| Challenge: | Existing benchmark datasets for open-domain dialogue generation are advancing the field . overlapping between training and test sets can cause fake performance . |
| Approach: | They analyze dailyDialog and OpenSubtitles to find out how overlapping can be exploited to obtain fake state-of-the-art performance. |
| Outcome: | The proposed datasets are cleaned and set up for future research. |
Language Technologies for the Creation of Multilingual Terminologies. Lessons Learned from the SSHOC Project (2022.lrec-1)
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| Challenge: | Language Technologies can help in promoting and facilitating multilingualism in the Social Sciences and Humanities domain. |
| Approach: | They propose to use Natural Language Processing and Machine Translation to provide tools to foster multilingual access and discovery to SSH content across different languages. |
| Outcome: | The proposed tools prove to be a valid asset to translation tasks . validation of results by domain experts proficient in the language is an unavoidable phase of the whole workflow. |
How to be FAIR when you CARE: The DGS Corpus as a Case Study of Open Science Resources for Minority Languages (2022.lrec-1)
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| Challenge: | a corpus of recordings of members of the deaf community in germany is published under the FAIR principles . CARE principles are used to ensure data is open and respects indigenous and minority group stakeholders. |
| Approach: | This article describes how the DGS Corpus implemented the CARE principles . the principles were introduced as a guide to good open data practices . they also help identify how openness of data should be limited or adjusted . |
| Outcome: | The DGS Corpus is a large collection of recordings of members of the deaf community in germany . the CARE principles have been used to help the corpus achieve its goals . |
Italian NLP for Everyone: Resources and Models from EVALITA to the European Language Grid (2022.lrec-1)
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| Challenge: | European Language Grid enables researchers and practitioners to easily distribute and use NLP resources and models. |
| Approach: | They propose to integrate Italian NLP resources into the European Language Grid . they show how easy it is to use the integrated systems and demonstrate how seamless it is . |
| Outcome: | The European Language Grid enables researchers and practitioners to easily distribute and use NLP resources and models. |
Cross-Lingual Link Discovery for Under-Resourced Languages (2022.lrec-1)
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Michael Rosner, Sina Ahmadi, Elena-Simona Apostol, Julia Bosque-Gil, Christian Chiarcos, Milan Dojchinovski, Katerina Gkirtzou, Jorge Gracia, Dagmar Gromann, Chaya Liebeskind, Giedrė Valūnaitė Oleškevičienė, Gilles Sérasset, Ciprian-Octavian Truică
| Challenge: | Linked data paradigms can be used to solve under-resourced languages' problem of under-utilization of resources. |
| Approach: | They propose a paradigm for cross-lingual link discovery that can be applied to under-resourced languages . they argue that techniques for cross language linking can be readily applied . |
| Outcome: | The proposed technologies can be applied to under-resourced languages, the authors argue . the authors show that the Linked Data paradigm can be used to solve the problem . |
Angry or Sad ? Emotion Annotation for Extremist Content Characterisation (2022.lrec-1)
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| Challenge: | Social platforms play an increasingly important role in the propagation of extremist ideas. |
| Approach: | They propose to use a linguistic annotation scheme to characterize extremist content in French . they validate the scheme and test its ability to capture various aspects of emotions . |
| Outcome: | The proposed method combines sociological and linguistic knowledge to characterize extremist content in French. |
Identification of Multiword Expressions in Tweets for Hate Speech Detection (2022.lrec-1)
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| Challenge: | Multiword expression (MWE) identification in tweets is a complex task due to the complex linguistic nature of MWEs combined with the non-standard language use in social networks. |
| Approach: | They propose a new architecture for incorporating multiword expression features into tweets to improve their accuracy. |
| Outcome: | The proposed system outperforms existing systems on the hate speech detection task on English Twitter. |
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text (2022.lrec-1)
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| Challenge: | Social distancing orders are the most effective strategy to reduce the spread of COVID-19 in 2020 . |
| Approach: | They propose to use NLP methods in a causal mediation scenario to emphasize the use of NLP and economics to decouple the effect of government restrictions on mobility from the effect that occurs due to public perception of the COVID-19 strategy. |
| Outcome: | The proposed model decouples the effect of government restrictions on mobility behavior from the effect that occurs due to public perception of the COVID-19 strategy in a country. |
Misspelling Semantics in Thai (2022.lrec-1)
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| Challenge: | In English, more than 70% of documents on the internet contain some form of misspelling . misspellers can be used as prosody to provide additional clues about the writer's attitude . |
| Approach: | They propose two ways to incorporate misspelling semantics into user-generated content . they propose a method to boost micro F1 score by 0.4-2% . |
| Outcome: | The proposed methods can boost the micro F1 score up to 0.4-2% while normalising misspelling is harmful and suboptimal. |
Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter (2022.lrec-1)
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| Challenge: | Psychiatry and people suffering from mental disorders have often been given a pejorative label that induces social rejection. |
| Approach: | They propose to use deep learning to detect polarity and type of use in tweets . they propose to combine polarization detection with typeof use detection to improve polarities . |
| Outcome: | The proposed models can detect the polarity of a tweet and the types of use on a dataset that is not yet available. |
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets (2022.lrec-1)
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| Challenge: | Existing fact-checking resources cover COVID-19 related information in news, but there is no dataset providing fact- checked COVId-19 related tweets with detailed annotations for biomedical entities, relations and relevant evidence. |
| Approach: | They propose a fact-checked corpus of tweets with annotations for biomedical entities, relations and relevant evidence for COVID-19 related tweets. |
| Outcome: | The proposed dataset provides fact-checked COVID-19 related tweets with detailed annotations for biomedical entities, relations and relevant evidence. |
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond (2022.lrec-1)
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| Challenge: | Language models are ubiquitous in NLP, but current analyses focus on (multilingual variants of) standard benchmarks and task-specific corpora as multilingual signals. |
| Approach: | They propose a model to train and evaluate multilingual language models in Twitter using a set of Twitter datasets in eight different languages and a XLM-T model. |
| Outcome: | The proposed model trains and evaluates multilingual models on Twitter. |
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models (2022.lrec-1)
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| Challenge: | Existing studies on NLP touch upon moral contexts in text. |
| Approach: | They construct a dataset that can be used for moral judgement tasks on a popular reddit subreddit. |
| Outcome: | The proposed model passes moral judgements on posts from a popular reddit subreddit . it shows that the model can be fine tuned and improves across the datasets . |
Generating Questions from Wikidata Triples (2022.lrec-1)
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| Challenge: | Existing methods for question generation from knowledge bases rely on extensive pre- and post-processing of the input triple. |
| Approach: | They revisit KBQG using pre training, a new (triple, question) dataset and taking question type into account and provide a more extended KBqg dataset. |
| Outcome: | The proposed approach outperforms existing methods in a standard and in 'zero-shot' setting. |
Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition (2022.lrec-1)
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| Challenge: | Large Language Models such as GPT-3 have demonstrated on-the-fly reasoning capabilities in NLP tasks, but they struggle with arithmetic operations. |
| Approach: | They propose a Transformer Language Model that decomposes numbers in units, tens, and so on . they introduce a pipeline that allows them to perform arithmetic operations between decomposed numbers . |
| Outcome: | The proposed model improves accuracy in addition, subtractions and multiplication tasks by 63% . the model is fine-tuned to perform arithmetic operations between decomposed numbers . |
Evaluating the Effects of Embedding with Speaker Identity Information in Dialogue Summarization (2022.lrec-1)
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| Challenge: | Existing methods for automatic dialogue summarization do not take into account speaker identity information, but instead use sinusoidal functions to embed speaker information at the less informative part of the position embedding. |
| Approach: | They propose to embed speaker identity information into a dialogue transcript encoder to address this issue and reduce the "who said what"-related errors. |
| Outcome: | The proposed method improves the convergence of the model in training and increases the average ROUGE scores of the generated summaries in comparison to existing methods. |
Perceived Text Quality and Readability in Extractive and Abstractive Summaries (2022.lrec-1)
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| Challenge: | Summarisation systems are limited and reflect human judgements poorly, resulting in expensive and inconsistent evaluation methods. |
| Approach: | They conducted an online survey on extractive and abstractive summaries using Swedish news data and used them to produce summary. |
| Outcome: | The summarisation models were trained on Swedish news data and tested on extractive and abstractive summaries. |
Learning to Prioritize: Precision-Driven Sentence Filtering for Long Text Summarization (2022.lrec-1)
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| Challenge: | Neural text summarization models are limited by their maximum input length, posing a challenge to summarizing longer texts comprehensively. |
| Approach: | They propose a pre-processing layer that removes low-quality sentences in articles to improve existing summarization models. |
| Outcome: | The proposed approach improves state-of-the-art summarization models on WikiHow and Reddit TIFU datasets by 3.84 and 8.57 points on the full test set and the long article subset. |
Automating Horizon Scanning in Future Studies (2022.lrec-1)
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Tatsuya Ishigaki, Suzuko Nishino, Sohei Washino, Hiroki Igarashi, Yukari Nagai, Yuichi Washida, Akihiko Murai
| Challenge: | Existing studies collect enough information to predict drastic social changes in the mid- or long-term future. |
| Approach: | They propose document retrieval and comment generation tasks for automating horizon scanning by analyzing a dataset that contains 2,266 manually collected news articles with comments written by experts. |
| Outcome: | The proposed tasks are more efficient than previous methods and the proposed models are more accurate. |
ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining (2022.lrec-1)
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| Challenge: | Recent large-scale language models show remarkable achievements in key NLP tasks such as Question Answering and Text Summarization. |
| Approach: | They propose a domain-specific pre-trained Vietnamese language model that outperforms the general domain language models. |
| Outcome: | The proposed model outperforms the general domain language models in Vietnamese datasets while outperforming the general-domain language models. |
Privacy-Preserving Graph Convolutional Networks for Text Classification (2022.lrec-1)
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| Challenge: | Graph convolutional networks (GCNs) are powerful for representation learning on documents that naturally occur as graphs, but sensitive personal information is prone to privacy leaks. |
| Approach: | They propose a method that adapts differentially-private gradient-based training to GCNs and conduct experiments using two optimizers on five NLP datasets in two languages. |
| Outcome: | The proposed method improves baseline privacy bounds by 2.7 while retaining competitive F1 scores while providing strong privacy guarantees. |
ArMATH: a Dataset for Solving Arabic Math Word Problems (2022.lrec-1)
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| Challenge: | This paper is the first to use deep learning methods to solve Arabic MWPs . it is also the first study to use transfer learning to solve MWp across different languages . |
| Approach: | They contribute to the first large-scale dataset for Arabic Math Word Problems . they use deep learning methods to solve Arabic MWPs and a transfer learning model to promote performance . |
| Outcome: | The proposed model improves Arabic MWP solvers by 3% over the existing model. |
KIMERA: Injecting Domain Knowledge into Vacant Transformer Heads (2022.lrec-1)
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| Challenge: | Recent studies show that transformer models lack specific domain knowledge and are under-performing in broad domains like the medical domain. |
| Approach: | They propose a method for retraining and instilling attention heads with structured domain knowledge by masking redundant attention heads. |
| Outcome: | The proposed method improves on seven datasets in the medical domain in information retrieval and clinical outcome prediction settings. |
Distilling the Knowledge of Romanian BERTs Using Multiple Teachers (2022.lrec-1)
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Andrei-Marius Avram, Darius Catrina, Dumitru-Clementin Cercel, Mihai Dascalu, Traian Rebedea, Vasile Pais, Dan Tufis
| Challenge: | Existing approaches to train pre-trained language models focus on the English language, thus widening the gap when considering low-resource languages. |
| Approach: | They propose three versions of distilled BERT models for the Romanian language . they argue that the models offer performance comparable to their teachers . |
| Outcome: | The proposed models perform comparable to their teachers, while being twice as fast on a GPU and 35% smaller. |
Personalized Filled-pause Generation with Group-wise Prediction Models (2022.lrec-1)
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| Challenge: | Disfluency generation is a method to generate personalized filled pauses (FPs) compared with fluent text generation, it is difficult to predict them because of the sparsity of position and frequency difference between more and less frequently used FPs. |
| Approach: | They propose a method to generate personalized filled pauses (FPs) by group-wise prediction models. |
| Outcome: | The proposed method generates personalized filled pauses (FPs) with group-wise prediction models. |
Transformer versus LSTM Language Models trained on Uncertain ASR Hypotheses in Limited Data Scenarios (2022.lrec-1)
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| Challenge: | Existing studies show that domain-specific LMs can only rely on limited in-domain speech data . a qualitative analysis reveals that Transformer LM can predict less frequent words . |
| Approach: | They propose a method to train Transformer LMs on ASR confusion networks . they find they are better at exploiting alternate uncertain ASR hypotheses . |
| Outcome: | The proposed method reduces perplexity by 3-6% on AMI scenarios but performs similar to LSTM LMs on Verbmobil conversational corpus. |
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised? (2022.lrec-1)
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| Challenge: | Keyword extraction is the task of retrieving words that are essential to the content of a document. |
| Approach: | They propose to use pretrained multilingual language models for zero-shot cross-lingual keyword extraction on low-resource languages with limited or no available labeled training data. |
| Outcome: | The proposed models outperform state-of-the-art unsupervised methods on low-resource languages with limited or no training data. |
Evaluating Pretraining Strategies for Clinical BERT Models (2022.lrec-1)
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| Challenge: | Existing generic language models in specialized domains may be sub-optimal due to domain differences. |
| Approach: | They propose various strategies for adapting a generic language model to the target domain and various forms of vocabulary modifications to fine-tune it. |
| Outcome: | The proposed strategies outperform a general-domain language model but little difference in performance between the models. |
KazNERD: Kazakh Named Entity Recognition Dataset (2022.lrec-1)
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| Challenge: | Named entity recognition (NER) is a subtask of information extraction aimed at identifying named entities (NEs) in semi-or unstructured text and classifying them into pre-specified types. |
| Approach: | They present a dataset for Kazakh named entity recognition using an annotation scheme and guidelines for annotation. |
| Outcome: | The dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. |
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization (2022.lrec-1)
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| Challenge: | In recent years, natural language inference has been an emerging research area . a new data augmentation technique is used to augment pre-trained language models . |
| Approach: | They propose to combine automatic contextual data augmentation with a learning procedure for natural language inference. |
| Outcome: | The proposed method outperforms baseline pre-trained language models on benchmark datasets and adversarial examples. |
Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning (2022.lrec-1)
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| Challenge: | Several studies focus on Skill Identification, but there is little work in further categorizing the identified skills. |
| Approach: | They propose a Danish job posting dataset annotated for nested spans of competences . they use the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy API to obtain fine-grained labels via distant supervision. |
| Outcome: | The proposed dataset outperforms existing models in the Danish job postings. |
Semantic Role Labelling for Dutch Law Texts (2022.lrec-1)
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| Challenge: | Existing methods to extract formalised representations of legal texts are labor-intensive . a Flint language is a knowledge representation language that allows us to express meaning of sources of norms . |
| Approach: | They propose a method to extract structured representations in the Flint language from natural language. |
| Outcome: | The proposed method outperforms the rule-based method on the Dutch Aliens Act. |
English Language Spelling Correction as an Information Retrieval Task Using Wikipedia Search Statistics (2022.lrec-1)
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| Challenge: | Existing spelling correction tools lack training or annotated data sets to perform . many spelling correction utilities suffer due to the size and quality of dictionaries available to aid correction. |
| Approach: | They propose a dynamic spelling correction tool that uses the Wikipedia dataset search API to aid misspelled term identification and automatic replacement. |
| Outcome: | The proposed spelling correction tool performs comparable to the industry-standard spelling correction algorithm, Hunspell. |
CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction (2022.lrec-1)
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| Challenge: | a corpus of English crude oil news for event extraction is presented . the corpus contains 425 news articles with approximately 11k events annotated . |
| Approach: | They present a corpus of English Crude Oil news for event extraction . it is the first of its kind for Commodity News and contributes to text mining . |
| Outcome: | The proposed corpus of English crude oil news is the first of its kind for Commodity News . the annotated news articles are compared with the standard news articles . |
Claim Extraction and Law Matching for COVID-19-related Legislation (2022.lrec-1)
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| Challenge: | Existing approaches to extract legal claims from news articles and match them with applicable laws are difficult for laypersons to learn since news articles do not refer to underlying laws. |
| Approach: | They propose an automated approach to extract legal claims from news articles and match the claims with applicable laws. |
| Outcome: | The proposed model achieves 46.7 F1 for claim extraction and 91.4 F1 law matching, despite conceptual limitations. |
Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules (2022.lrec-1)
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| Challenge: | Argumentation mining is a growing area of research with several interesting practical applications. |
| Approach: | They propose three sets of rules based on linguistic knowledge and distant supervision to identify such relations from Indian Supreme Court judgments. |
| Outcome: | The proposed rules are based on linguistic knowledge and distant supervision and use the source of the argument to build a dataset of Support and Attack relations between sentences in a court judgement with reasonable accuracy. |
KIND: an Italian Multi-Domain Dataset for Named Entity Recognition (2022.lrec-1)
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| Challenge: | Named-entity recognition is a task that uses named entities to classify texts . annotated data are time and money consuming, since they need to be created by experts of the domain of the annotation that is going to be done . |
| Approach: | They present an Italian dataset for Named-entity recognition with manual annotations and a semi-automatically annotated part. |
| Outcome: | The proposed dataset covers different styles and language uses, and is the largest in Italy. |
Russian Jeopardy! Data Set for Question-Answering Systems (2022.lrec-1)
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| Challenge: | Question answering is one of the most common tasks in natural language processing . open-domain questions cover a wide range of topics and do not necessarily come in form of an actual question. |
| Approach: | They describe a Russian question-like question set collected from the Russian analogue of Jeopardy! They observe its linguistic features and the related QA-task. |
| Outcome: | The proposed data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy! |
Know Better – A Clickbait Resolving Challenge (2022.lrec-1)
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| Challenge: | a clickbait headline or teaser is used to "bait" the reader into clicking a link to an article . clickbaiting is annoying but effective, and can be countered with specialized models . |
| Approach: | They propose to construct approaches that can automatically extract relevant information from clickbait articles . they argue that clickbaiting can probably not be defeated with clickbaitting detection alone . |
| Outcome: | The proposed methods outperform question answering models on clickbait resolving task . the data will be used to give users tools to counter clickbaiting in the future . |
Valet: Rule-Based Information Extraction for Rapid Deployment (2022.lrec-1)
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| Challenge: | a number of machine learning models can be trained to perform sentence-level information extraction at accuracies ranging from strong to adequate. |
| Approach: | They propose a Python framework for rule-based information extraction that allows for complex matching. |
| Outcome: | The proposed framework can be used to perform rule-based information extraction on examples. |
Negation Detection in Dutch Spoken Human-Computer Conversations (2022.lrec-1)
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| Challenge: | Existing negation detection methods in English are not available. |
| Approach: | They propose to annotate a Dutch dialogue corpus with negation cues and their scopes. |
| Outcome: | The proposed method can detect negation cues and scope in Dutch dialogues with high precision and recall. |
Reflections on 30 Years of Language Resource Development and Sharing (2022.lrec-1)
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| Challenge: | Linguistic Data Consortium was founded in 1992 to solve the problem that limitations in access to shareable data was impeding progress in Human Language Technology research and development. |
| Approach: | They review the roles of the Linguistic Data Consortium over the past 30 years after describing the conditions that lead to an HLT winter followed by a reawakening and an insatiable hunger for LRs. |
| Outcome: | The authors review the roles of the Linguistic Data Consortium over the past 30 years and provide a preview into future plans. |
Language Resources to Support Language Diversity – the ELRA Achievements (2022.lrec-1)
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| Challenge: | ELRA and its operational agency ELDA have continued to increase their catalogue of Language Resources (LRs) over the past few years, ELLA and ELTA have contributed to improve the access to multilingual information in the context of the pandemic . |
| Approach: | ELRA and its operational agency ELDA have increased their catalogue of Language Resources (LRs) over the past few years, they have established partnerships for the production of various types of LRs. |
| Outcome: | ELRA and its operational agency ELDA have contributed to improve the access to multilingual information in the context of the pandemic, develop tools for the de-identification of texts in the legal and medical domains, and support the EU eTranslation Machine Translation system. |
Ethical Issues in Language Resources and Language Technology – Tentative Categorisation (2022.lrec-1)
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| Challenge: | Ethical issues are often invoked, but rarely discussed in the fields of Language Resources and Language Technology. |
| Approach: | They propose a tentative taxonomy of ethical issues in Language Resources and Language Technology, built around five principles: Privacy, Property, Equality, Transparency and Freedom. |
| Outcome: | The proposed taxonomy will facilitate ethical assessment of projects in the field of Language Resources and Language Technology and structure discussion on ethical issues in this domain. |
Do we Name the Languages we Study? The #BenderRule in LREC and ACL articles (2022.lrec-1)
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| Challenge: | Using the #BenderRule, we examine the number and which languages are studied in two NLP conferences. |
| Approach: | They examine the application of the #BenderRule in NLP articles by inspecting 14,000 articles over a period of time ranging from 2000 to 2020 for LREC and 1979 to 2020 respectively. |
| Outcome: | The authors examine the application of the #BenderRule in natural language processing articles over a period of time ranging from 2000 to 2020 for LREC and ACL. |
Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts (2022.lrec-1)
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| Challenge: | Aspect-based sentiment analysis of user-generated content has been relatively unexplored in recent years. |
| Approach: | They present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA) they take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework. |
| Outcome: | The proposed dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. |
Multi-source Multi-domain Sentiment Analysis with BERT-based Models (2022.lrec-1)
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| Challenge: | Sentiment analysis is a widely studied task in natural language processing. |
| Approach: | They propose to improve BERT-based models for sentiment analysis on italian corpora and evaluate their performance on the basis of eight corpors. |
| Outcome: | The proposed model is evaluated over eight sentiment analysis corpora from different domains and sources on the prediction of positive, negative and neutral classes. |
NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis (2022.lrec-1)
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Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, Ibrahim Sa’id Ahmad, Idris Abdulmumin, Bello Shehu Bello, Monojit Choudhury, Chris Chinenye Emezue, Saheed Salahudeen Abdullahi, Anuoluwapo Aremu, Alípio Jorge, Pavel Brazdil
| Challenge: | Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. |
| Approach: | They propose a large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria. |
| Outcome: | The proposed dataset includes 30,000 tweets and a significant fraction of code-mixed tweets. |
A (Psycho-)Linguistically Motivated Scheme for Annotating and Exploring Emotions in a Genre-Diverse Corpus (2022.lrec-1)
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| Challenge: | Using a linguistic perspective, emotion annotation is considered a difficult task because of the lack of consensus on emotional categories, the fuzziness of boundaries between them or the great variability of emotion expressions types. |
| Approach: | They propose a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in french. |
| Outcome: | The proposed method clarifies the main concepts implied by the analysis of emotions as they are expressed in texts and performs a manual annotation campaign on a corpus of 1,594 texts (ca. 515K tokens) of different genres. |
Integrating a Phrase Structure Corpus Grammar and a Lexical-Semantic Network: the HOLINET Knowledge Graph (2022.lrec-1)
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| Challenge: | a graph modelling of grammar knowledge enables its merging with a lexical-semantic network . knowledge graphs provide a convenient means for heterogeneous knowledge to interact rather seamlessly. |
| Approach: | They propose a graph modelling of grammar knowledge which enables its merging with a lexical-semantic network. |
| Outcome: | The proposed model integrates grammar and lexical-semantic knowledge within a single and homogeneous knowledge graph. |
On the Impact of Temporal Representations on Metaphor Detection (2022.lrec-1)
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| Challenge: | State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using neural networks. |
| Approach: | They propose to use temporal and static word embeddings to account for different representations of literal meanings to examine metaphor detection tasks. |
| Outcome: | The proposed method outperforms static methods but may provide representations of the core meaning of the metaphor too close to their contextual meaning, causing confusion. |
Analysis and Prediction of NLP Models via Task Embeddings (2022.lrec-1)
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| Challenge: | Pretrained transformer-based encoders can be used to acquire rich text representations but need additional task supervision to be useful for downstream tasks. |
| Approach: | They propose a transformer to each MetaEval task and a neural network with a weighted encoder to perform the embeddings. |
| Outcome: | The proposed model outperforms baselines on GLUE tasks and can be used as a benchmark for future transfer learning research. |
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data (2022.lrec-1)
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| Challenge: | Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and can be used for further NLP applications. |
| Approach: | They propose to fine-tune pre-trained BERT models for automatic Term Extraction (ATE) using cross-lingual and cross-domain transfer learning to extract single and multi-word terms. |
| Outcome: | The proposed models can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE. |
Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate (2022.lrec-1)
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| Challenge: | Existing methods to classify aspects of arguments are expensive and require training data for further aspects and topics. |
| Approach: | They propose a supervised aspect-based argument mining task to classify arguments into semantically coherent groups referring to the same defined aspect categories. |
| Outcome: | The proposed method is able to predict share of arguments in a British newspaper corpus with 50 to 100 examples per aspect. |
MuLVE, A Multi-Language Vocabulary Evaluation Data Set (2022.lrec-1)
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| Challenge: | Existing systems for vocabulary evaluation are based on simple rules and do not account for real-life user learning data. |
| Approach: | They propose to use real-life user vocabulary learning data to evaluate vocabulary . they use language learning data from a phase6 vocabulary trainer to generate a multilingual data set for vocabulary evaluation. |
| Outcome: | The proposed data set provides outstanding results with 95.5 accuracy and F2-score. |
PLOD: An Abbreviation Detection Dataset for Scientific Documents (2022.lrec-1)
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| Challenge: | Existing datasets for abbreviation detection and extraction are limited. |
| Approach: | They propose to use a large-scale dataset for abbreviation detection and extraction that contains 160k+ segments automatically annotated with abbrevian and long forms. |
| Outcome: | The proposed dataset has an F1 score of 0.92 for abbreviations and 0.89 for detecting their corresponding long forms. |
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms (2022.lrec-1)
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Tosin Adewumi, Roshanak Vadoodi, Aparajita Tripathy, Konstantina Nikolaido, Foteini Liwicki, Marcus Liwicki
| Challenge: | Potential Idiomatic Expression (PIE) dataset for NLP in English contains over 20,100 samples with almost 1,200 cases of idioms from 10 classes (or senses). |
| Approach: | They present a large Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. |
| Outcome: | The proposed dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). |
LeSpell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner Language (2022.lrec-1)
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| Challenge: | Existing spellcheckers do not work well with learner data. |
| Approach: | They propose a multi-lingual evaluation data set of spelling mistakes in context that is highly customizable for the DKPro architecture. |
| Outcome: | The proposed spellchecker improves performance in many settings and can be customized to meet learners' needs. |
Subjective Text Complexity Assessment for German (2022.lrec-1)
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| Challenge: | Often, readability is defined as how easily a written text is to read. |
| Approach: | They propose to use a corpus of sentences provided by a German IT service provider to assess the readability of German text. |
| Outcome: | The proposed model can predict complexity of German text by using linguistically motivated features. |
Querying Interaction Structure: Approaches to Overlap in Spoken Language Corpora (2022.lrec-1)
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| Challenge: | In this paper, we address two specific problems arising when indexing and searching interaction corpora with overlapping speaker contributions. |
| Approach: | They propose and experiment with a speaker-based search mode that enables any speaker’s transcription tier to be the basic tokenization layer whereby contributions of other speakers are mapped to this given tier. |
| Outcome: | The proposed method enables any speaker’s transcription tier to be the basic tokenization layer whereby contributions of other speakers are mapped to this given tier. |
DiaBiz – an Annotated Corpus of Polish Call Center Dialogs (2022.lrec-1)
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Piotr Pęzik, Gosia Krawentek, Sylwia Karasińska, Paweł Wilk, Paulina Rybińska, Anna Cichosz, Angelika Peljak-Łapińska, Mikołaj Deckert, Michał Adamczyk
| Challenge: | DiaBiz is a large corpus of phone conversations from different business domains . it contains nearly 410 hours of recordings and over 3 million words of transcribed speech. |
| Approach: | They introduce DiaBiz, a large, annotated, multimodal corpus of Polish telephone conversations . it is a multimodal, multi-modal corpor of 4036 phone conversations from nine different domains . |
| Outcome: | The corpus of 4036 phone conversations in Poland is 410 hours long and contains over 3 million words of transcribed speech. |
LaVA – Latvian Language Learner corpus (2022.lrec-1)
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| Challenge: | a corpus of 1015 essays from foreigners learning Latvian as a foreign language is available at http://www.korpuss.lv/id/LaVA. |
| Approach: | They propose to create a Latvian Language Learner Corpus (LaVA) which contains 1015 essays from Latvian students with different language backgrounds. |
| Outcome: | The LaVA corpus contains 1015 essays from foreigners studying at Latvian higher education institutions and reaching the A1 (possibly A2) Latvian language proficiency level. |
The EuroPat Corpus: A Parallel Corpus of European Patent Data (2022.lrec-1)
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| Challenge: | a new corpus of patent-specific parallel data is available for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. |
| Approach: | They present a patent-specific corpus of parallel data for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. |
| Outcome: | The filtered corpus ranges in size from 51 million sentences (Spanish-English) to 154k sentences (Croatian-English), with the unfiltered (raw) corpus being up to 2 times larger. |
“Beste Grüße, Maria Meyer” — Pseudonymization of Privacy-Sensitive Information in Emails (2022.lrec-1)
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| Challenge: | exploding amount of user-generated content has spurred research to deal with documents from various digital communication formats. |
| Approach: | They propose to identify text spans that carry information revealing an individual’s identity and substitute them with synthetically generated surrogates. |
| Outcome: | The proposed model is based on a German-language email corpus and evaluates its training data on pseudonymized data. |
Criteria for the Annotation of Implicit Stereotypes (2022.lrec-1)
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| Challenge: | social media has brought with it a massive channel for spreading and reinforcing stereotypes . most stereotypes are expressed implicitly and identifying them automatically remains a challenge . |
| Approach: | They propose criteria to facilitate the subjective task of identifying the presence of stereotypes . they propose a newsCom-Implicitness corpus of 1,911 sentences, of which 426 are explicit and implicit racial stereotypes. |
| Outcome: | The proposed criteria show that they obtain different inter-annotator agreement values . the criteria are applied to a corpus of 1,911 sentences, of which 426 are explicit and implicit racial stereotypes . |
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling (2022.lrec-1)
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| Challenge: | Current state-of-the-art acoustic models can easily comprise more than 100 million parameters. |
| Approach: | They propose to train a gender-balanced, multilingual corpus from 76.000 contributors via Mozilla’s Common Voice project to perform phonetic symbol recognition and validate the quality of the generated phonetic annotation. |
| Outcome: | The proposed model can perform phonetic symbol recognition and validate the quality of the generated phonetic annotation. |
Curras + Baladi: Towards a Levantine Corpus (2022.lrec-1)
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| Challenge: | The processing of the Arabic language is a complex field of research due to the complex and rich morphology of Arabic, its high degree of ambiguity, and the presence of several regional varieties that need to be processed while taking into account their unique characteristics. |
| Approach: | They propose to revise the Palestinian morphologically annotated corpus and a Lebanese corpus to bridge nuanced linguistic gaps between the two highly mutually intelligible dialects. |
| Outcome: | The revised corpus can be used as a more general Levantine corpus. |
Annotation Study of Japanese Judgments on Tort for Legal Judgment Prediction with Rationales (2022.lrec-1)
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| Challenge: | An annotation scheme for Japanese judgment documents is proposed to provide a reliable dataset for Legal Judgment Prediction (LJP) the anticipated cost of LJP will be much lower than that of human legal professionals. |
| Approach: | They propose to build an annotation scheme for legal judgment prediction, especially for torts, which extracts decisions and rationales at character-level. |
| Outcome: | The proposed annotation scheme can produce a dataset of Japanese LJP at reasonable reliability. |
Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online (2022.lrec-1)
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Dana Ruiter, Liane Reiners, Ashwin Geet D’Sa, Thomas Kleinbauer, Dominique Fohr, Irina Illina, Dietrich Klakow, Christian Schemer, Angeliki Monnier
| Challenge: | HS-related corpora over-simplify the phenomenon of hate by labelling user content with binary classes, e.g., hate/neutral . this ignores the complex and subjective nature of HS, which limits the real-life applicability of classifiers trained on these corporales. |
| Approach: | They present a corpus of 9k German and french user comments from migration-related news articles. |
| Outcome: | The proposed corpus is annotated with 23 features that become descriptors of various types of speech, ranging from critical comments to implicit and explicit expressions of hate. |
ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference (2022.lrec-1)
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| Challenge: | Existing corpus ParCorFull contains parallel texts for English-German, French and Portuguese . translation of coreference across languages is challenging for MT and other NLP applications . |
| Approach: | They describe a parallel corpus annotated with full coreference chains for multiple languages . they use the existing corpus ParCorFull to study translation of coreference across languages - a challenge for machine translation and NLP . |
| Outcome: | The proposed corpus addresses translation of coreference across languages, a problem still challenging for machine translation and other multilingual natural language processing applications. |
A Multi-Party Dialogue Ressource in French (2022.lrec-1)
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| Challenge: | a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues is available for French-speaking players of the board game Catan. |
| Approach: | They propose to make available a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between french-speaking players of the board game Catan. |
| Outcome: | The proposed corpus is composed of long human-human interactions and can be used for dialogue studies in many fields. |
Bicleaner AI: Bicleaner Goes Neural (2022.lrec-1)
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| Challenge: | a new version of Bicleaner detects noisy sentences in parallel corpora . the tool is based on pre-trained transformer-based language models fine-tuned on a binary classification task. |
| Approach: | They propose to use Bicleaner AI to detect noisy sentences in parallel corpora . they use pre-trained transformer-based language models fine-tuned on a binary classification task . |
| Outcome: | The proposed tool improves translation quality and reduces manual cleaning steps. |
Semi-automatically Annotated Learner Corpus for Russian (2022.lrec-1)
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| Challenge: | Revita Learner Corpus is a semi-automatically annotated learner corpus for Russian . it is used for research in second language acquisition and foreign language teaching . |
| Approach: | They propose a semi-automatically annotated learner corpus for Russian that detects errors automatically and annotates errors by type. |
| Outcome: | The proposed corpus detects errors automatically and is annotated by type . the data is made public and the process is much cheaper and faster . |
UniMorph 4.0: Universal Morphology (2022.lrec-1)
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Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Abbott Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud’hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova
| Challenge: | The Universal Morphology project provides broad-coverage instantiated morphological inflection tables for hundreds of diverse languages. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. |
| Outcome: | The proposed schema has added 66 new languages, including 24 endangered languages. |
Textinator: an Internationalized Tool for Annotation and Human Evaluation in Natural Language Processing and Generation (2022.lrec-1)
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| Challenge: | Large-scale pretrained language models have brought substantial advances to the natural language processing field. |
| Approach: | They present an internationalized annotation and human evaluation bundle, called Textinator, along with documentation and video tutorials. |
| Outcome: | The proposed tool is compared to other tools along 9 different axes and is available in multiple languages. |
CyberAgressionAdo-v1: a Dataset of Annotated Online Aggressions in French Collected through a Role-playing Game (2022.lrec-1)
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| Challenge: | Recent studies have highlighted that private instant messaging platforms are major mediums of cyber aggression among teens. |
| Approach: | They present a dataset of aggressive chats in French collected through a role-playing game in high-schools . they provide insights on the different types of aggression and verbal abuse depending on the targeted victims . |
| Outcome: | The proposed dataset analyzes aggressive conversations in French on a role-playing game in high schools . it provides insights on the different types of aggression and verbal abuse depending on the targeted victims . |
Finnish Hate-Speech Detection on Social Media Using CNN and FinBERT (2022.lrec-1)
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| Challenge: | Existing tools to identify hate posts from social media are limited in the field of online hate speech detection. |
| Approach: | They propose to use finBERT to generate a Finnish hate speech dataset . finBERt has a 91.7% accuracy and 90.8% F1 score value, they say . |
| Outcome: | The proposed model outperforms state-of-the-art models in Finnish and other languages. |
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing (2022.lrec-1)
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| Challenge: | Automatic post-editing (APE) is a research field that aims to correct errors in translated sentences regardless of the utilized machine translation system. |
| Approach: | They propose a method for automatically generating APE data based on a noising scheme from a parallel corpus. |
| Outcome: | The proposed method shows that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance. |
Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning (2022.lrec-1)
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| Challenge: | Recent studies have reported that domain mismatch prevents cross-lingual transfer . UBLI and UNMT do not work well when underlying monolingual corpora come from different domains . |
| Approach: | They show that a simple initialization regimen can overcome domain mismatch in cross-lingual transfer . they pre-train word embeddings on concatenated domain-mismatched corpora and use them as initializations . |
| Outcome: | The initialization regimen can overcome the domain mismatch effect in cross-lingual transfer learning . the initializations were used for MUSE UBLI, UN Parallel UNMT, and the SemEval 2017 task . |
Cross-Lingual Knowledge Transfer for Clinical Phenotyping (2022.lrec-1)
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Jens-Michalis Papaioannou, Paul Grundmann, Betty van Aken, Athanasios Samaras, Ilias Kyparissidis, George Giannakoulas, Felix Gers, Alexander Loeser
| Challenge: | Current models for clinical phenotyping are limited to clinical notes written in English due to the large amount of labeled and unlabeled clinical text resources. |
| Approach: | They propose to use translation-based methods with domain-specific encoders and cross-lingual encoder plus adapters to perform this task for clinics that do not use the English language. |
| Outcome: | The proposed strategies outperform the state-of-the-art models for clinics that do not use the English language and have a small amount of in-domain data available. |
The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text (2022.lrec-1)
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| Challenge: | a corpus of over 200,000 microblog translations supports translation of thirteen languages into English . large collections of parallel text, or bitext, are increasingly available in many languages . |
| Approach: | They propose a corpus of over 200,000 microblog posts that supports translation of thirteen languages into English. |
| Outcome: | The proposed corpus contains over 200,000 translations of microblog posts in 13 languages . fine-tuning showed significant improvements in translation quality . |
Multilingual and Multimodal Learning for Brazilian Portuguese (2022.lrec-1)
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| Challenge: | Existing models that learn multimodal and multilingual representations perform better in many natural language tasks. |
| Approach: | They use a multimodal and multilingual corpus to test its generalization ability for other languages . they achieve a BLEU score of 51.8 and a METEOR score of 78.0 on the test set . |
| Outcome: | The proposed model outperforms the existing model on a Portuguese-English multimodal translation task. |
LibriS2S: A German-English Speech-to-Speech Translation Corpus (2022.lrec-1)
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| Challenge: | Recent advances in speech-to-text translation have led to significant improvements, but the availability of appropriate training data is limiting. |
| Approach: | They propose a new text-to-speech and speech-tospech translation model that directly learns to generate the speech signal based on the pronunciation of the source language. |
| Outcome: | The proposed model learns to generate speech signal based on pronunciation of source language. |
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output (2022.lrec-1)
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Vivien Macketanz, Eleftherios Avramidis, Aljoscha Burchardt, He Wang, Renlong Ai, Shushen Manakhimova, Ursula Strohriegel, Sebastian Möller, Hans Uszkoreit
| Challenge: | Using fine-grained evaluation techniques, translation outputs have become better and more fluent. |
| Approach: | They propose a fine-grained test suite for the language pair German–English . they describe the creation and implementation of the test suite in detail . |
| Outcome: | The proposed test suite is based on linguistically motivated categories and phenomena and semi-automatic evaluation is carried out with regular expressions. |
Cross-lingual Transfer of Monolingual Models (2022.lrec-1)
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| Challenge: | Existing studies on cross-lingual learning using multilingual models cast doubt on shared vocabulary and joint pre-training . et al. (2005) show that model knowledge learned in the source language enhances the learning of the target language independently of language proximity. |
| Approach: | They propose a method for transferring monolingual models to other languages through continuous pre-training and investigate their results in English. |
| Outcome: | The proposed method outperforms a model trained from scratch in the GLUE benchmark for English . it shows that model knowledge from the source language enhances the learning of syntactic and semantic knowledge in english. |
Dataset of Student Solutions to Algorithm and Data Structure Programming Assignments (2022.lrec-1)
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| Challenge: | a dataset containing source code solutions to algorithmic programming exercises solved by students at the University of Hamburg is available under the permissive CC BY-NC 4.0 license. |
| Approach: | They present a dataset containing source code solutions to algorithmic programming exercises solved by students at the University of Hamburg. |
| Outcome: | The proposed dataset contains solutions to 21 programming tasks written in Java and Python and over 1500 individual solutions. |
Language Patterns and Behaviour of the Peer Supporters in Multilingual Healthcare Conversational Forums (2022.lrec-1)
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Ishani Mondal, Kalika Bali, Mohit Jain, Monojit Choudhury, Jacki O’Neill, Millicent Ochieng, Kagnoya Awori, Keshet Ronen
| Challenge: | a quantitative linguistic analysis of multilingual peer supporters in health-focused WhatsApp forums in Kenya is needed. |
| Approach: | They conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp forums in Kenya. |
| Outcome: | The proposed language analyzer can be used to analyze language usage patterns in two health-focused WhatsApp forums in Kenya. |
Frame Shift Prediction (2022.lrec-1)
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| Challenge: | Frame shift is a cross-linguistic phenomenon in translation which results in corresponding pairs of linguistic material evoking different frames. |
| Approach: | They propose a task to predict cross-linguistic frame-to-frame correspondence and propose auxiliary training to learn cross-lingual frame-by-frame correlation. |
| Outcome: | The proposed task can learn cross-linguistic frame-to-frame correspondence and predict frame shifts in a Berkeley FrameNet-like configuration. |
CLeLfPC: a Large Open Multi-Speaker Corpus of French Cued Speech (2022.lrec-1)
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| Challenge: | Cued Speech is a visual communication system developed for deaf people to complement speechreading at the phonetic level with hands. |
| Approach: | They describe a visual communication mode that uses handshapes in different placements near the face and mouth movements to make the phonemes of spoken language look different from each other. |
| Outcome: | The proposed system is based on 4 hours of audio and video recordings of 23 participants. |
Samrómur Children: An Icelandic Speech Corpus (2022.lrec-1)
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| Challenge: | Samrómur Children contains 131 hours of read speech from Icelandic children aged between 4 to 17 years. |
| Approach: | They propose to build a large-scale speech corpus for automatic speech recognition for Icelandic. |
| Outcome: | The corpus contains 131 hours of read speech from Icelandic children aged 4 to 17 years . the goal of the project is to make Icelandic available in language-technology applications . |
The Norwegian Parliamentary Speech Corpus (2022.lrec-1)
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| Challenge: | the dataset contains recordings of meetings at the Norwegian parliament . it is the first publicly available dataset containing unscripted, Norwegian speech . |
| Approach: | the Norwegian Parliamentary Speech Corpus is a publicly available speech dataset . it contains recordings of meetings from the Norwegian parliament with orthographic transcriptions . the dataset is intended to fill a gap in the available unscripted speech data . |
| Outcome: | the dataset contains recordings of meetings at the Norwegian parliament with orthographic transcriptions in Norwegian Bokml and Norwegian Nynorsk. |
A Speech Recognizer for Frisian/Dutch Council Meetings (2022.lrec-1)
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Martijn Bentum, Louis ten Bosch, Henk van den Heuvel, Simone Wills, Domenique van der Niet, Jelske Dijkstra, Hans Van de Velde
| Challenge: | During council meetings both Frisian and Dutch are spoken, and code switching between both languages shows up frequently. |
| Approach: | They develop a bilingual Frisian/Dutch speech recognizer for council meetings in Fryslân (the Netherlands) based on an existing Frisian and Dutch speech recognized by FAME!, which was trained and tested on radio broadcasts. |
| Outcome: | The new recognizer is based on an existing speech recognizer for Frisian and Dutch named FAME!, which was trained and tested on radio broadcasts. |
Elderly Conversational Speech Corpus with Cognitive Impairment Test and Pilot Dementia Detection Experiment Using Acoustic Characteristics of Speech in Japanese Dialects (2022.lrec-1)
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| Challenge: | Several studies have explored using only the acoustic and linguistic information of conversational speech as diagnostic material, with some success. |
| Approach: | They propose to use acoustic features of conversational speech to detect dementia even when dialects are present. |
| Outcome: | The proposed method detects dementia using acoustic features even when dialects are present even when spoken from two regions. |
A Spoken Drug Prescription Dataset in French for Spoken Language Understanding (2022.lrec-1)
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Ali Can Kocabiyikoglu, François Portet, Prudence Gibert, Hervé Blanchon, Jean-Marc Babouchkine, Gaëtan Gavazzi
| Challenge: | Existing systems for medical drug prescriptions are in text form and in English. |
| Approach: | They propose to provide a natural language interface to a smartphone that would allow medical practitioners to enter their prescriptions orally at the point of care. |
| Outcome: | The proposed system would allow medical practitioners to enter prescriptions orally at the point of care while leaving the system some control to make sure no legal information is forgotten. |
Towards an Open-Source Dutch Speech Recognition System for the Healthcare Domain (2022.lrec-1)
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| Challenge: | Existing generic speech recognition systems do not include healthcare jargon in the lexicon and do not safeguard privacy of sensitive data. |
| Approach: | They propose to use a language model to train Dutch doctors to use medicines in their audiovisual recordings. |
| Outcome: | The proposed method reduces the word error rate (WER) by 5.2% on the use of medicines in the Netherlands. |
A Dataset for Speech Emotion Recognition in Greek Theatrical Plays (2022.lrec-1)
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| Challenge: | Speech Emotion Recognition (SER) is a task that is difficult to perform by humans due to subjectiveness of the emotional content. |
| Approach: | They propose to use GreThE to collect data for speech emotion recognition in Greek plays. |
| Outcome: | The proposed dataset contains utterances from various actors and plays, along with respective valence and arousal annotations. |
Audiobook Dialogues as Training Data for Conversational Style Synthetic Voices (2022.lrec-1)
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Liisi Piits, Hille Pajupuu, Heete Sahkai, Rene Altrov, Liis Ermus, Kairi Tamuri, Indrek Hein, Meelis Mihkla, Indrek Kiissel, Egert Männisalu, Kristjan Suluste, Jaan Pajupuu
| Challenge: | Synthetic voices are increasingly used in applications that require a conversational speaking style. |
| Approach: | They compare voices trained on audiobook character speech corpus, audiobook narrator speech corpu and neutral-style sentence-based corpus . they conclude that the character speech and neutral style corpus are more suitable . |
| Outcome: | The evaluation of voices trained on three corpora of equal size was conducted by voice chatbots . the results may have been confounded by the greater acoustic variability and poorer phonemic coverage . |
Using a Knowledge Base to Automatically Annotate Speech Corpora and to Identify Sociolinguistic Variation (2022.lrec-1)
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| Challenge: | Speech characteristics vary from speaker to speaker due to many factors, including communication context, provenance, age, and social background. |
| Approach: | They propose a method that uses a knowledge base to provide speaker-specific information. |
| Outcome: | The proposed method can be used to enrich existing corpora with speaker-specific information and to correlate with diastratic features. |
Phone Inventories and Recognition for Every Language (2022.lrec-1)
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| Challenge: | Identifying phone inventories is crucial component in language documentation and preservation of endangered languages. |
| Approach: | They propose a probabilistic and non-probabilistic phone inventory model that estimates the phone inventory for any language listed in Glottolog. |
| Outcome: | The proposed model outperforms baseline models by 6.5 F1 and improves the PER (phone error rate) in phone recognition by 25%. |
Constructing Parallel Corpora from COVID-19 News using MediSys Metadata (2022.lrec-1)
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Dimitrios Roussis, Vassilis Papavassiliou, Sokratis Sofianopoulos, Prokopis Prokopidis, Stelios Piperidis
| Challenge: | Using the COVID-19 related dataset, we generated parallel corpora in 26 languages and 26 languages. |
| Approach: | They propose to exploit COVID-19 related metadata to generate parallel corpora using the EMM/MediSys processing chain of news articles. |
| Outcome: | The proposed corpora were based on the COVID-19 related dataset created with the Europe Media Monitor (EMM) / Medical Information System (MediSys) . |
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes (2022.lrec-1)
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| Challenge: | Biomedical researchers have used manual curation to extract biomedical interactions from research texts to improve coverage. |
| Approach: | They propose a new dataset for training and evaluating multi-class multi-label biomedical relation extraction models using human annotations and the CTD database. |
| Outcome: | The proposed dataset is substantially larger and cleaner than existing datasets and includes annotations linking mentions to their entities. |
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries (2022.lrec-1)
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| Challenge: | a new question answering dataset is being developed for electronic health records . structured tables and unstructured notes can be duplicated, contradictory or provide additional context . |
| Approach: | They develop a question-answer-matching dataset using structured tables and unstructured notes from an EHR. |
| Outcome: | The proposed model is based on a model with a modality selection network . it uses the prediction of a RAT-SQL to choose between EHR tables and clinical notes . |
Efficiently and Thoroughly Anonymizing a Transformer Language Model for Dutch Electronic Health Records: a Two-Step Method (2022.lrec-1)
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| Challenge: | Neural Networks (NNs) are used to model large amounts of data, such as text data, and have shown to be very useful for language modelling. |
| Approach: | They propose to use a Dutch language model for hospital notes to anonymize a model trained on large amounts of data and publish it online. |
| Outcome: | The proposed method predicts a name-like token 0.2% of the time, compared to the original training data. |
BERTrade: Using Contextual Embeddings to Parse Old French (2022.lrec-1)
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| Challenge: | a growing interest in digital humanities for automatic processing and annotation of historical texts is generating new models for historical languages. |
| Approach: | They use POS-tagging and dependency parsing to evaluate contextual word embedding models . Old French is one of the historical languages for which they have the largest amount of syntactically annotated data . |
| Outcome: | The proposed model can be used to improve performance in Old French, the authors show . they use POS-tagging and dependency parsing to evaluate the model's quality . |
Out-of-Domain Evaluation of Finnish Dependency Parsing (2022.lrec-1)
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| Challenge: | prevailing practice in academia evaluates model performance on in-domain evaluation data . however, in many real world applications data on which model is applied may differ from training data - a problem that is not addressed by current literature. |
| Approach: | They propose to use Finnish-OOD out-of-domain treebank for out- of-domain evaluation . they propose to include sections more challenging for the general parser . |
| Outcome: | The proposed treebank includes five distinct data sources and a total of 19,382 syntactic words in 2,122 sentences. |
TArC: Tunisian Arabish Corpus, First complete release (2022.lrec-1)
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| Challenge: | a project focused on Tunisian Arabic encoded in Arabizi is a hybrid approach to linguistics and linguistic research . Arabic dialects are notoriously under-resourced linguistic systems . |
| Approach: | They propose to use Arabic script as a linguistic corpus and a neural network architecture to annotate the latter with various levels of linguistic information. |
| Outcome: | The proposed approach is hybrid and combines linguistic and linguistic tools . the proposed approach produces in cascade different levels of annotation . |
Towards Universal Segmentations: UniSegments 1.0 (2022.lrec-1)
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Zdeněk Žabokrtský, Niyati Bafna, Jan Bodnár, Lukáš Kyjánek, Emil Svoboda, Magda Ševčíková, Jonáš Vidra
| Challenge: | Existing data resources for morphological segmentation are limited to 32 languages . a large number of word forms exist, with some sub-parts being "recycled" many times . |
| Approach: | They propose a multilingual data resource for morphological segmentation in 32 languages . they analyze diversity of how individual linguistic phenomena are captured across them . |
| Outcome: | The proposed scheme is based on 17 existing data resources relevant for segmentation in 32 languages. |
TeDDi Sample: Text Data Diversity Sample for Language Comparison and Multilingual NLP (2022.lrec-1)
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| Challenge: | a deep understanding of language is being achieved through increased access to data from minority and low-resource languages. |
| Approach: | They present a diversity sample of text data for language comparison and multilingual natural language processing. |
| Outcome: | The TeDDi sample features 89 languages based on the typological diversity sample in the World Atlas of Language Structures . |
Leveraging a Bilingual Dictionary to Learn Wolastoqey Word Representations (2022.lrec-1)
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| Challenge: | Existing word embeddings for lowresource languages require large corpora of running text to learn high quality representations. |
| Approach: | They leverage a bilingual dictionary to learn Wolastoqey word embeddings by encoding their corresponding English definitions into vector representations using pretrained English word and sequence representation models. |
| Outcome: | The proposed model outperforms baseline models without language-specific training or fine-tuning. |
Unmasking the Myth of Effortless Big Data - Making an Open Source Multi-lingual Infrastructure and Building Language Resources from Scratch (2022.lrec-1)
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Linda Wiechetek, Katri Hiovain-Asikainen, Inga Lill Sigga Mikkelsen, Sjur Moshagen, Flammie Pirinen, Trond Trosterud, Børre Gaup
| Challenge: | During the last two decades, machine learning approaches have dominated the field of natural language processing (NLP) weak literary traditions give rise to corpora too unreliable to function as a model for NLP tools. |
| Approach: | They propose an alternative to corpus-based language technology that can provide language technology solutions for minority languages. |
| Outcome: | The proposed approach can provide language technology solutions for minority languages outside the reach of corpus-based language technology. |
Building and curating conversational corpora for diversity-aware language science and technology (2022.lrec-1)
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| Challenge: | Language resources that capture language use in its natural habitat of social interaction are rare despite the obvious merits of studying the very environment where we all learn and use it everyday. |
| Approach: | They propose to build an analysis pipeline and best practice guidelines for building and curating corpora of everyday conversation in diverse languages. |
| Outcome: | The proposed pipeline can be used to collect and curate conversational corpora in 67 languages and varieties from 28 phyla. |
EPIC UdS - Creation and Applications of a Simultaneous Interpreting Corpus (2022.lrec-1)
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| Challenge: | EPIC UdS is a multilingual corpus of simultaneous interpreting for English, German and Spanish. |
| Approach: | They describe the creation and annotation of EPIC UdS, a multilingual corpus of simultaneous interpreting for English, German and Spanish. |
| Outcome: | The proposed corpus includes transcripts suitable for research on more than one language pair and on interpreting with regard to German. |
Development of a Benchmark Corpus to Support Entity Recognition in Job Descriptions (2022.lrec-1)
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| Challenge: | Existing tools for identifying and extracting salient entities from job descriptions are limited by the lack of publicly available training data. |
| Approach: | They propose to use a standard definition of entities and a training corpus to develop a benchmark Entity Recognition (ER) model. |
| Outcome: | The proposed model achieves an F1 score of 0.59 from 18.6k entities comprising five types (Skill, Qualification, Experience, Occupation, and Domain). |
CAMIO: A Corpus for OCR in Multiple Languages (2022.lrec-1)
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| Challenge: | CAMIO is a corpus of 70,000 images of machine printed text for optical character recognition (OCR) it covers 35 languages across 24 unique scripts. |
| Approach: | CAMIO is a corpus of annotated multilingual images for optical character recognition . the corpus includes nearly 70,000 images of machine printed text . |
| Outcome: | The corpus includes nearly 70,000 images of machine printed text . most images have been exhaustively annotated for text localization . |
FABRA: French Aggregator-Based Readability Assessment toolkit (2022.lrec-1)
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Rodrigo Wilkens, David Alfter, Xiaoou Wang, Alice Pintard, Anaïs Tack, Kevin P. Yancey, Thomas François
| Challenge: | a large number of readability predictor variables are used to predict reading difficulty of texts . the most important predictors for native texts are lexical diversity, dependency counts and text coherence . |
| Approach: | They propose a readability toolkit based on aggregation of readability predictor variables . they show which features are most predictive on two different corpora . |
| Outcome: | The proposed toolkit improves performance over standard feature-based readability prediction. |
Towards Building a Spoken Dialogue System for Argument Exploration (2022.lrec-1)
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| Challenge: | Argumentative dialogue systems lack a robust natural language understanding framework for complex tasks . drop-down menus hinder the application of natural language learning approaches . |
| Approach: | They propose to integrate a natural language understanding framework into an argumentative dialogue system. |
| Outcome: | The proposed system is compared to a baseline system using a drop-down menu . the drop- down menu convinces, but the willingness to use it is significantly higher . |
FreeTalky: Don’t Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue (2022.lrec-1)
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| Challenge: | FreeTalky is a deep learning-based foreign language learning platform for people who experience anxiety dealing with foreign languages. |
| Approach: | They propose a deep learning-based foreign language learning platform called FreeTalky . it employs a humanoid robot NAO and various deep learning models . |
| Outcome: | The proposed system provides personalized learning based on persona dialogue and grammar error correction, and also helps alleviate xenoglossophobia by replacing the real human in the conversation with a NAO robot, through human evaluation. |
Self-Contained Utterance Description Corpus for Japanese Dialog (2022.lrec-1)
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| Challenge: | Existing task frameworks for dialog-act classification and slot filling can only interpret utterances using pre-defined types and slots. |
| Approach: | They propose a task to describe the intent of an utterance in a dialog with multiple simple natural sentences without the context. |
| Outcome: | The proposed task can describe the intent of an utterance in a dialog with multiple simple natural sentences without the context. |
DialCrowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit (2022.lrec-1)
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| Challenge: | DialCrowd 2.0 helps requesters obtain higher quality data from human intelligence tasks. |
| Approach: | They propose to use DialCrowd 2.0 to help requesters obtain higher quality data . they aim to improve the way requesters present tasks and facilitate effective communication with workers. |
| Outcome: | The proposed toolkit enables requesters to obtain higher quality data by presenting tasks more clearly and facilitating effective communication with workers. |
A Brief Survey of Textual Dialogue Corpora (2022.lrec-1)
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| Challenge: | Several dialogue corpora are available for research purposes, but they do not cover all the necessities of real-world applications. |
| Approach: | They analyze available dialogue corpora and propose possible approaches to create new ones. |
| Outcome: | The proposed corpus of human-human dialogues is based on a list of available dialogue corpora . it covers speakers, size, languages, collection, annotations, and domains . some trends are identified and possible approaches are also discussed . |
A Unified Approach to Entity-Centric Context Tracking in Social Conversations (2022.lrec-1)
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| Challenge: | Context Tracking is a computational task for human-human conversations . it involves identifying important entities and keeping track of their properties and relationships . |
| Approach: | They propose to use a human-human conversation corpus for context tracking with people and location annotations to model the conversation's context. |
| Outcome: | The proposed model is based on a large human-human conversation corpus with people and location annotations. |
A Unifying View On Task-oriented Dialogue Annotation (2022.lrec-1)
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| Challenge: | Recent research attention in task-oriented dialogue systems focuses on end-to-end neural models. |
| Approach: | They present a dataset that combines annotated corpora from four domains to provide a unified ontology and annotation schema for task-oriented dialogues. |
| Outcome: | The proposed dataset improves language, information content and performance in dialogues with two recent models. |
A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis (2022.lrec-1)
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| Challenge: | Graph databases are well-suited for crossreferencing information from multiple sources to support machine learning tasks. |
| Approach: | They propose a graph-based structure of multiple resources enriched with graph analytics approaches to provide an encompassing view of the movie recommendation domain and of the way people talk about it during the recommendation task. |
| Outcome: | The proposed graph-based structure provides an encompassing view of the domain and of the way people talk about it during the recommendation task. |
SHARE: A Lexicon of Harmful Expressions by Spanish Speakers (2022.lrec-1)
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Flor Miriam Plaza-del-Arco, Ana Belén Parras Portillo, Pilar López Úbeda, Beatriz Gil, María-Teresa Martín-Valdivia
| Challenge: | Using natural language processing, offensive comments can be created by composition of words. |
| Approach: | They propose to use a lexical resource with 10,125 offensive terms and expressions collected from Spanish speakers to retrieve the vocabulary. |
| Outcome: | The proposed resource has 10,125 offensive terms and expressions and is used to identify spans in Spanish. |
Wiktextract: Wiktionary as Machine-Readable Structured Data (2022.lrec-1)
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| Challenge: | Unlike previous Wiktionary extractions, the new extractor, Wiktextract, fully interprets and expands templates and Lua modules in Wiktionaries. |
| Approach: | They propose a machine-readable structured version of Wiktionary that interprets and expands templates and Lua modules. |
| Outcome: | The extracted data is multilingual and includes lemmas, inflected forms, translations, etymology, usage examples, pronunciations, and various morphological, syntactic, semantic, topical, and dialectal annotations. |
NyLLex: A Novel Resource of Swedish Words Annotated with Reading Proficiency Level (2022.lrec-1)
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| Challenge: | a text is easy to read if it contains easy words and avoids difficult ones. |
| Approach: | They propose to use a corpus annotated with word frequencies and reading proficiency levels to help writers create more accessible texts. |
| Outcome: | The proposed resource can be used to help writers create more accessible texts for Swedish. |
Making a Semantic Event-type Ontology Multilingual (2022.lrec-1)
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| Challenge: | a new version of SynSemClass is being developed for use in natural language processing . the ontology is a bilingual resource with no links to a valency lexicon . |
| Approach: | They propose to add German entries to the SynSemClass Event-type Ontology . they propose to use the ontology as a human-readable and human-understandable database . |
| Outcome: | The proposed extension of SynSemClass Event-type Ontology is presented in a paper in czech republic . the ontology provides curated data for NLP experiments with cross-lingual synonyms . |
NomVallex: A Valency Lexicon of Czech Nouns and Adjectives (2022.lrec-1)
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| Challenge: | NomVallex is a manual annotated valency lexicon of Czech nouns and adjectives . valencies are the ability of a verb to combine with other sentence constituents based on their morphemic forms . |
| Approach: | They propose a manually annotated valency lexicon of Czech nouns and adjectives . they capture valencies of a lexical unit in a sequence of valence slots . |
| Outcome: | The proposed lexicon is based on corpus data and contains 1027 lexical units . valency properties of lexicals are captured in a valence frame, with morphemic forms . |
TZOS: an Online Terminology Database Aimed at Working on Basque Academic Terminology Collaboratively (2022.lrec-1)
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| Challenge: | TZOS is an online terminology database to work collaboratively on academic terminology. |
| Approach: | They propose to use a terminology database to work collaboratively on academic terminology. |
| Outcome: | The proposed tool integrates the Communicative Theory of Terminology and the methodological matters with the real corpus GARATERM. |
Animacy Denoting German Nouns: Annotation and Classification (2022.lrec-1)
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| Challenge: | Animacy detection is meant to distinguish words which denote humans from words used to denote non-humans. |
| Approach: | They propose a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. |
| Outcome: | The proposed gold standard comprises almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. |
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations (2022.lrec-1)
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| Challenge: | Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes. |
| Approach: | They propose that a classification setup for emotion analysis should be performed in an integrated manner, including the different semantic roles that participate in an emotion episode. |
| Outcome: | The proposed method reveals patterns in the co-occurrence of people’s emotions in interaction. |
Polar Quantification of Actor Noun Phrases for German (2022.lrec-1)
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| Challenge: | Polanyi and Zaenen (2006) focused on the negative polar load of noun phrases, especially those denoting actors. |
| Approach: | They propose a method to measure the negative polar load of noun phrases by using a silver standard and a BERT-based intensity regressor. |
| Outcome: | The proposed model is based on a lexicon-based silver standard and tested empirically. |
Czech Dataset for Cross-lingual Subjectivity Classification (2022.lrec-1)
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| Challenge: | Using the existing English dataset, we can use the subjectivity classification to test the ability of pre-trained multilingual models to transfer knowledge between languages. |
| Approach: | They propose to use a Czech subjectivity dataset of 10k manually annotated subjective and objective sentences as a cross-lingual benchmark. |
| Outcome: | The proposed dataset is the first subjectivity dataset for the Czech language and also includes 200k automatically labeled sentences. |
RED v2: Enhancing RED Dataset for Multi-Label Emotion Detection (2022.lrec-1)
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| Challenge: | RED is a machine learning-based resource developed for the automatic detection of emotions in Romanian texts. |
| Approach: | They propose an open-source extension of RED by adding trust and surprise . they propose two variants of ground truth suitable for multi-label classification and text regression . |
| Outcome: | The proposed model is based on two models with two transformer models, the Romanian BERT and the multilingual XLM-Roberta model, in categorical and regression settings. |
Fine-Grained Error Analysis and Fair Evaluation of Labeled Spans (2022.lrec-1)
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| Challenge: | Annotations with incorrect label or boundaries count as two errors instead of one, despite being closer to the target annotation than false positives or false negatives. |
| Approach: | They propose an algorithm for error identification in flat and multi-level annotations and propose a procedure for calculating meaningful precision, recall, and F1-scores based on the more fine-grained error types. |
| Outcome: | The proposed procedure prevents double penalties and allows for a more detailed error analysis, providing more insight into the actual weaknesses of a system. |
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition (2022.lrec-1)
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| Challenge: | Existing studies have focused on auto-regressive models for generalization in named entity (NE) typing (NET) and recognition (NER) . however, little has been done in this direction for auto-Regressive LMs despite their popularity and potential to express a wide variety of NLP tasks in the same unified format. |
| Approach: | They propose to probe auto-regressive LMs for NET and NER generalization by resorting to meta-learning to assess the model's memorization of NEs. |
| Outcome: | The proposed model performs well on NET and NER generalization tasks, while relying more on NE than contextual cues in few-shot NER. |
Frustratingly Easy Performance Improvements for Low-resource Setups: A Tale on BERT and Segment Embeddings (2022.lrec-1)
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| Challenge: | Understanding why contextualized embeddings work is still an active area of research. |
| Approach: | They propose to use a BERT architecture to encode a sub-word, position and a segment embedding as input representations for each sub- word. |
| Outcome: | The proposed model performs well on single-sentence prediction tasks while swapping segment IDs in paired-sentent tasks. |
The Subject Annotations of the Danish Parliament Corpus (2009-2017) - Evaluated with Automatic Multi-label Classification (2022.lrec-1)
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| Challenge: | The interest in analysing and automatically processing large amounts of political data has increased in the past decades. |
| Approach: | They address the semi-automatic annotation of subjects in the Danish Parliament Corpus (2009-2017) v.2 and describe multi-label classification experiments to verify the consistency of the subject annotation. |
| Outcome: | The proposed method improves on the baseline classifier, which is a majority classifier. |
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation (2022.lrec-1)
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| Challenge: | Transfer learning is a promising direction for low-resource neural machine translation (NMT) but it introduces many new variables which are often selected through ablation studies, costly trial-and-error, or niche expertise. |
| Approach: | They conducted a three-factor experiment to examine how language similarity, pre-training dataset size and main dataset size interacted in their effect on performance in pre-trained transformer-based low-resource NMT. |
| Outcome: | The results suggest that systematic studies of interactions may be a promising long-term direction for guiding research in low-resource neural machine translation. |
Holistic Evaluation of Automatic TimeML Annotators (2022.lrec-1)
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| Challenge: | TimeML is an annotation scheme for representing temporal information in texts. |
| Approach: | They propose to combine eight metrics for holistic evaluation of TimeML graphs. |
| Outcome: | The proposed system produces graphs with 1/3 of the time indeterminacy and 1/3 of gold standard . the proposed system is compared with four other systems and is a good fit for the proposed task. |
Measuring Uncertainty in Translation Quality Evaluation (TQE) (2022.lrec-1)
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| Challenge: | Existing automated tools are not good enough to evaluate translation quality . existing tools are often accused of having low reliability and agreement . |
| Approach: | They propose to use a method to accurately estimate the confidence intervals depending on the sample size of the translated text. |
| Outcome: | The proposed method aims to estimate the confidence intervals (CITATION) depending on the sample size of the translated text, e.g. the amount of words or sentences, that needs to be processed on TQE workflow step for confident and reliable evaluation of overall translation quality. |
Challenging the Transformer-based models with a Classical Arabic dataset: Quran and Hadith (2022.lrec-1)
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| Challenge: | Existing benchmark datasets have a low readability index which does not reflect real-world complex data. |
| Approach: | They constructed a dataset of Quran-verse and Hadith-teaching pairs by consulting sources of reputable religious experts. |
| Outcome: | The proposed models performed on a binary classification task to identify whether two pieces of CA text convey the same underlying message. |
Question Modifiers in Visual Question Answering (2022.lrec-1)
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| Challenge: | Visual Question Answering (VQA) is a multi-disciplinary task that requires integration of several key disciplines. |
| Approach: | They develop a model that adds modifiers to questions based on object properties and spatial relationships using Amazon Mechanical Turk data. |
| Outcome: | The proposed model can improve when questions are modified to include more details. |
Multimodal Pipeline for Collection of Misinformation Data from Telegram (2022.lrec-1)
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| Challenge: | a large portion of misinformation is spread via multimodal means, such as images and videos . a new pipeline for collecting misinformation from Telegram allows us to collect a greater variety of mis-information examples . |
| Approach: | They propose to use AI to understand misinformation flow across social media platforms . they collect data from Telegram groups which promote COVID-19 misinformation . |
| Outcome: | The proposed dataset contains almost one million messages from 2k different public channels related to spreading COVID-19 misinformation. |
Identifying Tension in Holocaust Survivors’ Interview: Code-switching/Code-mixing as Cues (2022.lrec-1)
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| Challenge: | Using CS/CM as a linguistic phenomenon could be a sign of tension in Holocaust survivors’ interviews. |
| Approach: | They annotated CS/CM codes and annotate silence situations in an open corpus . they found that most annotations were captured in the tension places . |
| Outcome: | The proposed method shows that annotations are captured in the tension places . the study calls for more research endeavors on tension detection . |
Fine-tuning vs From Scratch: Do Vision & Language Models Have Similar Capabilities on Out-of-Distribution Visual Question Answering? (2022.lrec-1)
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| Challenge: | Large general-purpose pre-trained models have become ubiquitous, but only limited research exists into their robustness. |
| Approach: | They perform a fine-grained evaluation of two models on Visual Question Answering using out-of-distribution data and a rephrasing analysis. |
| Outcome: | The proposed model is better than a previous model trained from scratch on the training data alone. |
Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset (2022.lrec-1)
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| Challenge: | The goal of the project Multilingual Image Corpus is to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Approach: | They propose to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Outcome: | The project provides a large image dataset with annotated objects and object descriptions in 24 languages. |
Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words (2022.lrec-1)
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| Challenge: | Existing vision-based sign language production approaches suffer from out-of-vocabulary (OOV) and test-time generalization problems. |
| Approach: | They propose an avatar-based sign language production system that generates sign language videos from spoken language expressions. |
| Outcome: | The proposed system achieves higher BLEU-4 and higher ROUGE-L scores on a new Korean-Korean sign language dataset. |
The VoxWorld Platform for Multimodal Embodied Agents (2022.lrec-1)
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| Challenge: | a retrospective of the VoxWorld platform is presented . it is a platform for rapidly building and deploying embodied agents with contextual and situational awareness. |
| Approach: | They present a retrospective on the development of the VoxWorld platform . they focus on three different agent implementations and the functionality needed to accommodate them . |
| Outcome: | The VoxWorld platform has evolved from a theoretical model to a platform capable of multimodal interaction and hybrid reasoning. |
MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes (2022.lrec-1)
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| Challenge: | Recent studies on sentiment analysis of memes have focused on English, but there is a significant barrier to performing multimodal sentiment analysis research in resource-constrained languages like Bengali. |
| Approach: | They propose to use a Bengali dataset to perform multimodal sentiment analysis in low resource languages. |
| Outcome: | The proposed dataset for Bengali contains 4417 memes with three annotated labels positive, negative, and neutral. |
RUSAVIC Corpus: Russian Audio-Visual Speech in Cars (2022.lrec-1)
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| Challenge: | a new audio-visual speech corpus is recorded in a car environment and is noise-robust . currently there is no noiserobustic speech recognition system to be used in real-driving conditions. |
| Approach: | They propose to use a natural speech corpus recorded in a car environment to improve audio-based speech recognition in the presence of severe acoustic noise. |
| Outcome: | The proposed corpus is natural, controlled and adequate size to train state-of-the-art NN approaches. |
A First Corpus of AZee Discourse Expressions (2022.lrec-1)
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| Challenge: | a corpus of AZee discourse expressions which formally describe Sign Language utterances of any length is presented. |
| Approach: | They propose to build a corpus of AZee discourse expressions which formally describe Sign Language utterances of any length using the AZEe approach and language. |
| Outcome: | The proposed corpus is based on a video production of a French Sign Language speech expression containing 40 breves and is evaluated on real-life utterances. |
BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment (2022.lrec-1)
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| Challenge: | Existing metrics to evaluate video captioning systems are based on overlap between the caption and the reference sentence, but fail to include the context of the scene. |
| Approach: | They propose a method to evaluate video captioning systems using a deep learning model . the model is based on a language model that has been shown to work well in NLP tasks . |
| Outcome: | The proposed model outperforms the most commonly used metrics in video to text tasks. |
Abstract Meaning Representation for Gesture (2022.lrec-1)
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| Challenge: | Abstract Meaning Representation (AMR) is an annotated graphbased representation that expresses the meaning of a sentence in terms of its predicate-argument structure. |
| Approach: | They propose an extension to Abstract Meaning Representation (AMR) that captures the meaning of gesture. |
| Outcome: | The proposed model is more challenging than standard AMR while integrating meaningful elements unique to gesture. |
The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild (2022.lrec-1)
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| Challenge: | GINCO is a new training dataset for automatic genre identification based on 1,125 crawled Slovenian web documents that consist of 650,000 words. |
| Approach: | They propose to use 1,125 crawled Slovenian web documents to train a new genre classification system based on a GINCO training dataset . |
| Outcome: | The proposed classifiers perform better on the 1,125 crawled Slovenian web documents than the existing models and achieve higher scores on the task. |
The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools (2022.lrec-1)
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Gaëlle Laperrière, Valentin Pelloin, Antoine Caubrière, Salima Mdhaffar, Nathalie Camelin, Sahar Ghannay, Bassam Jabaian, Yannick Estève
| Challenge: | a growing number of studies address the spoken language understanding domain through a simple task like speech intent detection. |
| Approach: | They focus on the french MEDIA SLU dataset, which is distributed since 2005 . they propose a recipe for its use, including data preparation, training and evaluation scripts . |
| Outcome: | The MEDIA SLU dataset is used as a benchmark dataset for a large number of research projects. |
BasqueGLUE: A Natural Language Understanding Benchmark for Basque (2022.lrec-1)
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| Challenge: | Natural Language Understanding (NLU) benchmarks are costly to develop and language-dependent . basqueGLUE is the first benchmark for Basque, a less-resourced language . |
| Approach: | They propose a benchmark for Basque, a less-resourced language, using existing datasets. |
| Outcome: | The proposed benchmarks take into account a wide and diverse set of NLU tasks that require some form of language understanding beyond the detection of superficial clues. |
Resources and Experiments on Sentiment Classification for Georgian (2022.lrec-1)
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| Challenge: | a dataset for sentiment classification and semantic polarity dictionary for Georgian is available . a large number of linguistic resources are available for sentiment analysis for this language . |
| Approach: | They propose to create the first publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. |
| Outcome: | The results are on par with state-of-the-art models for well-studied languages . the authors compare knowledge-and machine learning-based models to a well-supported language . |
CoFiF Plus: A French Financial Narrative Summarisation Corpus (2022.lrec-1)
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| Challenge: | Existing corpora for financial narrative summarisation exists in English but there is a significant lack of financial text resources in the French language. |
| Approach: | They propose to use natural language processing to analyse financial documents to find the best summarisation methods. |
| Outcome: | The proposed dataset is the first to provide a comprehensive set of financial text written in French. |
Generating Extended and Multilingual Summaries with Pre-trained Transformers (2022.lrec-1)
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| Challenge: | Almost all summarisation methods focus on a single language and short summaries. |
| Approach: | They propose a dataset for extended summarisation tailored for 11 sentences . they compare three multilingual transformer models on extractive and abstractive summarization tasks . |
| Outcome: | The proposed dataset is tailored for extended summaries of approx. 11 sentences. |
MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases (2022.lrec-1)
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| Challenge: | MUSS trains strong models using sentence-level paraphrase data instead of labeled simplification data. |
| Approach: | They propose a multilingual unsupervised sentence simplification system that does not require labeled simplification data. |
| Outcome: | The proposed model outperforms the previous best supervised models on English, French, and Spanish benchmarks despite not using labeled simplification data. |
Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation (2022.lrec-1)
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Samhita Honnavalli, Aesha Parekh, Lily Ou, Sophie Groenwold, Sharon Levy, Vicente Ordonez, William Yang Wang
| Challenge: | Existing studies have not investigated how gender biases in natural language processing (NLP) are compounded with other societal biase. |
| Approach: | They propose a framework for probing compound bias by examining seniority in pre-trained neural generation models. |
| Outcome: | The proposed framework amplifies bias by considering women as junior and men as senior more often than ground truth in both domains. |
Combining ELECTRA and Adaptive Graph Encoding for Frame Identification (2022.lrec-1)
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| Challenge: | Existing studies focus on FI tasks, but none have been done on the computational side. |
| Approach: | They propose a new system for Frame Identification based on pre-trained text encoders trained discriminatively and graphs embedding. |
| Outcome: | The proposed system produces state-of-the-art performance over two benchmarks and all possible splits and cleaning procedures used in the literature. |
Polysemy in Spoken Conversations and Written Texts (2022.lrec-1)
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| Challenge: | a recent study examined the use of polysemous words in discourses . a lexical ambiguity is a result of the use and use of multiple senses in a text . |
| Approach: | They propose a "one sense per discourse" hypothesis to explain the use of polysemous words in discourses . they compare the polysesty level of spoken dialogs with spoken dialog . |
| Outcome: | The proposed "one sense per discourse" hypothesis is not valid in all texts. |
Cross-Level Semantic Similarity for Serbian Newswire Texts (2022.lrec-1)
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| Challenge: | Semantic similarity is a measure of the level of semantic overlap between texts of different lengths. |
| Approach: | They present a cross-level semantic similarity (CLSS) dataset in Serbian and compare it to its English counterpart, SemEval CLSS. They also use pre-trained language models to fine-tune the dataset. |
| Outcome: | The proposed dataset is compared to its preexisting counterpart in English, SemEval CLSS. The results are presented and state-of-the-art pre-trained language models are evaluated on the CLSS task in Serbian. |
Universal Proposition Bank 2.0 (2022.lrec-1)
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Ishan Jindal, Alexandre Rademaker, Michał Ulewicz, Ha Linh, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, Yunyao Li
| Challenge: | Semantic role labeling (SRL) is a shallow semantic parsing task that identifies "who did what to whom when, where etc." SRL is useful in a wide range of downstream NLP tasks and real-world applications. |
| Approach: | They propose a method to generate shallow semantic parsing tasks using monolingual SRL and multilingual parallel data. |
| Outcome: | The proposed method improves the quality of the generated propbanks. |
The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Texts (2022.lrec-1)
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| Challenge: | Corpora of eye movements during reading of contextualized running text is a way of making such records available for natural language processing. |
| Approach: | They present CopCo, the first eye tracking corpus of its kind for the Danish language. |
| Outcome: | The Copenhagen corpus of eye tracking recordings from natural reading of Danish texts is the first of its kind for the Danish language. |
The Brooklyn Multi-Interaction Corpus for Analyzing Variation in Entrainment Behavior (2022.lrec-1)
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| Challenge: | Entrainment is the phenomenon of conversational partners adapting to one another to become more similar. |
| Approach: | They propose to use dyadic conversations to identify speaker traits and conversation contexts that cause variations in entrainment behavior. |
| Outcome: | The proposed corpus identifies speaker traits and conversation contexts that cause variations in entrainment behavior. |
Pro-TEXT: an Annotated Corpus of Keystroke Logs (2022.lrec-1)
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| Challenge: | Pro-TEXT is a corpus of keystroke logs written in french . keystrokes are recordings of the writing process executed on a keyboard . |
| Approach: | They propose to annotate keystroke logs written in French and make them available in a database-like format and in CoNLL format. |
| Outcome: | The Pro-TEXT corpus contains 202K tokens, while the annotated portion is 30K token large. |
Work Hard, Play Hard: Collecting Acceptability Annotations through a 3D Game (2022.lrec-1)
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| Challenge: | Corpus-based studies on acceptability judgements have always been popular thanks to the release of the CoLA corpus, a large-scale corpus of sentences extracted from linguistic handbooks as examples of acceptable/non acceptable phenomena in English. |
| Approach: | They present a 3D video game that was used to collect acceptability judgments on italian sentences and compare them with experts’ acceptability judgements. |
| Outcome: | The proposed game compares the annotations of Italian sentences with those of experts and shows that they are more reliable than crowd-sourced annotations. |
DiHuTra: a Parallel Corpus to Analyse Differences between Human Translations (2022.lrec-1)
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| Challenge: | a new corpus of human translations contains both professional and student translations of news and reviews texts. |
| Approach: | They propose to use the data to compare human and professional translations of news and reviews in a new corpus which contains both professional and student translations. |
| Outcome: | The proposed corpus contains professional and student translations of news and reviews and a subcorpus containing reviews into Finnish. |
Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection (2022.lrec-1)
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| Challenge: | Existing methods to identify cyberbullying from text are limited due to the complexity of the content and the lack of labeled large-scale corpus. |
| Approach: | They propose a data augmentation-based approach that could enhance the automatic detection of cyberbullying in social media texts. |
| Outcome: | The proposed approach overcomes limitations of social media posts with word sense disambiguation and synonymy relation . results show that the proposed approach improves on the existing classifiers with and without data augmentation. |
ALIGNMEET: A Comprehensive Tool for Meeting Annotation, Alignment, and Evaluation (2022.lrec-1)
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| Challenge: | Summarization is a challenging problem, and it is difficult to create, correct, and evaluate the summaries manually. |
| Approach: | They propose an open-source tool for meeting annotation, alignment, and evaluation . the tool aims to provide an efficient and clear interface for fast annotation . |
| Outcome: | The proposed tool is open-source and installable from PyPI. |
KSoF: The Kassel State of Fluency Dataset – A Therapy Centered Dataset of Stuttering (2022.lrec-1)
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| Challenge: | Stuttering is a complex speech disorder that negatively affects an individual’s ability to communicate effectively. |
| Approach: | They present a therapy-based dataset that tracks stuttering events and changes in speech over a long time and labeled them with six syllable-related event types: blocks, prolongations, sound repetitions, word repetitions and interjections. |
| Outcome: | The study introduces the Kassel State of Fluency (KSoF) dataset containing over 5500 clips of people who underwent stuttering therapy. |
EZCAT: an Easy Conversation Annotation Tool (2022.lrec-1)
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| Challenge: | EZCAT is an annotation tool for textual conversations, but it is not customizable. |
| Approach: | They propose an easy-to-use interface to annotate conversations in a configurable schema . they use it to annnotate private chats and chats, and they use the schema to test it . |
| Outcome: | The proposed interface allows users to control data and annotate conversations in two levels . it eliminates the need for a server and accounts management, and allows users access to data . |
Spoken Language Treebanks in Universal Dependencies: an Overview (2022.lrec-1)
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| Challenge: | spoken language treebanks have divergent annotation schemes limiting cross-resource explorations . many spoken language trees have no written form, but many of the world languages have no spoken form at all. |
| Approach: | They propose to use the Universal Dependencies annotation scheme to annotate spoken language treebanks using a morphosyntactic annotation scheme. |
| Outcome: | The proposed treebanks differ significantly with respect to the inventory and format of transcribed phenomena and the principles adopted in their morphosyntactic annotation. |
LeConTra: A Learner Corpus of English-to-Dutch News Translation (2022.lrec-1)
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| Challenge: | a dataset of English-to-Dutch news translations enriched with translation process data is available for free . three students of a Master's programme in Translation were asked to translate 50 different English journalistic texts of approximately 250 tokens each. |
| Approach: | They propose to make a learner corpus of English-to-Dutch news translations enriched with translation process data. |
| Outcome: | The dataset can be used in translation process research, learner corpus research, and corpus-based translation studies. |
Annotating Attribution in Czech News Server Articles (2022.lrec-1)
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| Challenge: | Currently, significant attention is being paid to fact-checking. |
| Approach: | They analyze Czech radio news articles and analyze their attributions using crowdsourcing annotation task. |
| Outcome: | The proposed method aims to detect sources in the Czech news server . it uses crowdsourcing annotation tasks to identify sources and names . |
Xposition: An Online Multilingual Database of Adpositional Semantics (2022.lrec-1)
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| Challenge: | Xposition is an online platform for documenting adpositional semantics across languages . SNACS provides a unified metalanguage for characterizing the major classes of meanings expressed with appositions . |
| Approach: | They propose to use Xposition to document adpositional semantics across languages . Xpos houses annotation guidelines, structured lexicographic documentation, annotated corpora . |
| Outcome: | The proposed platform houses annotation guidelines, structured lexicographic documentation, and annotated corpora. |
A Study in Contradiction: Data and Annotation for AIDA Focusing on Informational Conflict in Russia-Ukraine Relations (2022.lrec-1)
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| Challenge: | This paper describes data resources created for Phase 1 of the DARPA Active Interpretation of Disparate Alternatives (AIDA) program . AIDA systems must extract entities, events, and relations from multimedia documents, aggregate that information across documents and languages, and produce multiple “hypotheses” about what has happened. |
| Approach: | This paper describes data resources created for Phase 1 of the DARPA Active Interpretation of Disparate Alternatives program . the program aims to develop language technology that can help humans manage large volumes of conflicting information . |
| Outcome: | The proposed corpus focuses on the domain of Russia-Ukraine relations and contains source data in English, Russian and Ukrainian . it is designed to support the development and evaluation of systems that extract entities, events, and relations from individual multimedia documents, aggregate the information across documents and languages, and produce multiple “hypotheses” about what has happened. |
Annotating Verbal Multiword Expressions in Arabic: Assessing the Validity of a Multilingual Annotation Procedure (2022.lrec-1)
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Najet Hadj Mohamed, Cherifa Ben Khelil, Agata Savary, Iskandar Keskes, Jean-Yves Antoine, Lamia Hadrich-Belguith
| Challenge: | a subset of 1,062 sentences from the Prague Arabic Dependency Treebank PADT were selected and annotated by two Arabic native speakers independently. |
| Approach: | They propose to use Arabic as an annotation framework to extend PARSEME to modern standard Arabic by measuring inter-annotator agreement. |
| Outcome: | The proposed framework is based on a subset of 1,062 sentences from the Prague Arabic Dependency Treebank PADT and is already exceeding the smallest corpus of the PARSEME suite. |
Annotation of Communicative Functions of Short Feedback Tokens in Switchboard (2022.lrec-1)
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| Challenge: | lexical forms and prosodic characteristics of short feedback tokens are not indicative of their communicative function. |
| Approach: | They propose to annotate short feedback tokens with a lexical annotation scheme . they find that feedback functions have distinguishable prosodic characteristics . |
| Outcome: | The proposed annotations show that lexical forms alone are not indicative of the communicative function. |
A Dataset of Offensive Language in Kosovo Social Media (2022.lrec-1)
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| Challenge: | Social media are a central part of people’s lives but are rife with bullying and offensive language, creating an unsafe environment for their users. |
| Approach: | They propose to use user-generated comments on Facebook and YouTube from selected Kosovo news platforms to annotate offensive language in Albanian. |
| Outcome: | The proposed system improves on Danish but not Albanian, on offensive language recognition and distinguishing targeted and untargeted offence. |
The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses (2022.lrec-1)
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| Challenge: | Gender bias in natural language processing (NLP) applications has been receiving increasing attention, largely due to the lack of datasets and resources. |
| Approach: | They propose a corpus for gender identification and rewriting in contexts involving one or two target users with independent grammatical gender preferences. |
| Outcome: | The proposed corpus expands on Habash et al.'s Arabic Parallel Gender Corpus (APGC) by adding second person targets and increasing the total number of sentences over 6.5 times, reaching over 590K words. |
The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)
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| Challenge: | Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models. |
| Approach: | They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups. |
| Outcome: | The proposed model is based on the behavior of subreddits banned in June 2020 as part of Reddit's efforts to stop the dissemination of hate speech. |
Annotating Arguments in a Corpus of Opinion Articles (2022.lrec-1)
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Gil Rocha, Luís Trigo, Henrique Lopes Cardoso, Rui Sousa-Silva, Paula Carvalho, Bruno Martins, Miguel Won
| Challenge: | Argument annotation is the process of exposing and justifying one's points of view, with the aim of conveying a logical reasoning through a set of semantically related propositions. |
| Approach: | They propose to use argumentative discourse units to annotate arguments in Portuguese using a multi-layered process to analyze the annotations produced. |
| Outcome: | The proposed model exploits the best practices identified in previous studies while fostering the potential use of the resulting annotated corpus for new purposes. |
German Parliamentary Corpus (GerParCor) (2022.lrec-1)
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| Challenge: | German parliaments have a large and partly unexploited treasure trove of publicly accessible texts. |
| Approach: | a new corpus of German-language parliamentary protocols is made available in XMI format . the corpus is genre-specific and contains conversions of scanned protocols . a researcher at the university of berlin and a professor at the berlin university created the corpuus . |
| Outcome: | the German Parliamentary Corpus is a genre-specific corpus of German-language parliamentary protocols from three centuries and four countries. |
NerKor+Cars-OntoNotes++ (2022.lrec-1)
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| Challenge: | In this paper, we present an upgraded version of the Hungarian NYTK-NerKor named entity corpus . it contains twice as many annotated spans and 7 times as many distinct entity types as the original version. |
| Approach: | They present an upgraded version of the Hungarian NYTK-NerKor named entity corpus with an extended OntoNotes 5 annotation scheme. |
| Outcome: | The enhanced version of the corpus contains twice as many annotated spans and 7 times more distinct entity types than the original version. |
A Comparative Cross Language View On Acted Databases Portraying Basic Emotions Utilising Machine Learning (2022.lrec-1)
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| Challenge: | Since several decades emotional databases have been recorded by various laboratories. |
| Approach: | They propose to model similarity as performance in cross database machine learning experiments and to analyze a manually picked set of four acoustic features that represent different phonetic areas. |
| Outcome: | The proposed sets of features represent different phonetic areas and are comparable across languages. |
Nkululeko: A Tool For Rapid Speaker Characteristics Detection (2022.lrec-1)
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| Challenge: | Nkululeko is a software tool that lets users perform semi-supervised machine learning experiments in the speaker characteristics domain. |
| Approach: | They propose a software tool called Nkululeko that lets users perform semi-supervised machine learning experiments in the speaker characteristics domain. |
| Outcome: | The proposed tool is based on audformat, a speech database metadata description . it supports best practise and fast setup of experiments without programming skills . |
Speech Aerodynamics Database, Tools and Visualisation (2022.lrec-1)
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| Challenge: | Aerodynamic processes underlie the characteristics of the acoustic signal of speech sounds. |
| Approach: | a database of aerodynamic processes underlies the characteristics of the acoustic signal of speech sounds . a project was undertaken to obtain data with simultaneous recording of speech acustic signals . |
| Outcome: | the database was designed during an ARC project . it contains recordings of 2 English, 1 Amharic, and 7 French speakers . |
PATATRA and PATAFreq: two French databases for the documentation of within-speaker variability in speech (2022.lrec-1)
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| Challenge: | Variability in speech is pervasive but structured and ruled-governed. |
| Approach: | They propose two databases which contain recordings of 9 to 11 speakers . they compare the delay between repetitions of speech tasks with different speakers based on their own data . |
| Outcome: | The proposed databases compare speakers' performance on a large set of speech tasks with different delays. |
The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Existing work in the area of radio browsing using automatic speech recognition (ASR) has been done by the United Nations in Uganda, and Keyword Spotting systems in Somalia. |
| Approach: | They propose to use a Luganda radio speech corpus of 155 hours to build a usable radio monitoring automatic speech recognition system. |
| Outcome: | The makerere artificial intelligence lab releases a Luganda radio speech corpus of 155 hours. |
Far-Field Speaker Recognition Benchmark Derived From The DiPCo Corpus (2022.lrec-1)
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| Challenge: | Using a publicly-available corpus, we propose a far-field speaker verification benchmark. |
| Approach: | They propose a far-field speaker verification benchmark derived from the publicly available DiPCo corpus. |
| Outcome: | The proposed tasks are very challenging and hope to inspire the speech community to develop new methods and systems for this challenging domain. |
Evaluating Sampling-based Filler Insertion with Spontaneous TTS (2022.lrec-1)
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| Challenge: | Injecting fillers into spoken dialogue systems has a rich history of study . ambiguity of filler occurrence and inter-speaker difference make modeling and evaluation difficult. |
| Approach: | They propose an objective score for filler insertion using sampling-based sampling . they build three models trained on two single-speaker spontaneous corpora and evaluate them with FPP and perceptual tests. |
| Outcome: | The proposed model is useful in analysis but does not correlate well with perceptual MOS. |
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian (2022.lrec-1)
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| Challenge: | Hungarian is spoken by 15 million people, yet, easily accessible Automatic Speech Recognition (ASR) benchmark datasets are practically unavailable. |
| Approach: | They propose to use a subset of the BEA spoken Hungarian database to assess ASR, primarily for conversational AI applications. |
| Outcome: | The proposed framework achieves 45% reduction in recognition error rate compared to classical approach without external language model or additional supervised data. |
SNuC: The Sheffield Numbers Spoken Language Corpus (2022.lrec-1)
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| Challenge: | SNuC is the first published corpus of spoken alphanumeric identifiers . it contains recordings and transcriptions of over 50 native British English speakers . |
| Approach: | They present a corpus of spoken alphanumeric identifiers of the sort typically used as serial and part numbers in the manufacturing sector. |
| Outcome: | The proposed corpus can be used to improve spoken alphanumeric identifier recognition. |
The ManDi Corpus: A Spoken Corpus of Mandarin Regional Dialects (2022.lrec-1)
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| Challenge: | Existing methods of remote speech data collection were limited by the telephone bandwidth and were therefore of low quality for phonetic research. |
| Approach: | They introduce a spoken corpus of regional Mandarin dialects and Standard Mandarin. |
| Outcome: | The proposed corpus contains 357 recordings (about 9.6 hours) of monosyllabic words, disyllable words, short sentences, a short passage and a poem, produced in standard Mandarin and in one of six regional Mandarin dialects. |
The Speed-Vel Project: a Corpus of Acoustic and Aerodynamic Data to Measure Droplets Emission During Speech Interaction (2022.lrec-1)
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Francesca Carbone, Gilles Bouchet, Alain Ghio, Thierry Legou, Carine André, Muriel Lalain, Sabrina Kadri, Caterina Petrone, Federica Procino, Antoine Giovanni
| Challenge: | Conversations and professional interactions are associated with increased risk of SARS-CoV-2 exposure . however, it is unclear to what extent speech properties influence droplets emission . |
| Approach: | They propose to measure velocity and direction of airflow, the number and size of droplets spread during conversation in french. |
| Outcome: | The results will allow future simulation studies to predict the transport, dispersion and evaporation of droplets emitted under different speech conditions. |
Towards Speech-only Opinion-level Sentiment Analysis (2022.lrec-1)
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| Challenge: | Existing systems that estimate user preferences only in static manners or exploit interaction history are inadequate to accurately assess user preferences. |
| Approach: | They propose to integrate rank consistent ordinal regression into a speech-only sentiment prediction task performed by ResNet-like systems and use speaker verification extractors trained on larger datasets as low-level feature extractor. |
| Outcome: | The proposed system beats state-of-the-art unimodal systems on multimodal Opinion Sentiment and Emotion Intensity databases. |
At the Intersection of NLP and Sustainable Development: Exploring the Impact of Demographic-Aware Text Representations in Modeling Value on a Corpus of Interviews (2022.lrec-1)
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| Challenge: | In order to preserve the privacy of speakers, we investigate encoding demographic information using autoencoders. |
| Approach: | They introduce a dataset of qualitative interviews from rural communities in India and Uganda and use it to enhance text representations with demographic information. |
| Outcome: | The proposed model extends the UPV classification model with demographic information to preserve the privacy of speakers. |
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis (2022.lrec-1)
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Michael Gref, Nike Matthiesen, Sreenivasa Hikkal Venugopala, Shalaka Satheesh, Aswinkumar Vijayananth, Duc Bach Ha, Sven Behnke, Joachim Köhler
| Challenge: | Sentiment analysis and emotion recognition can help research in audiovisual interview archives . however, humans perceive sentiments and emotions ambiguously and subjectively . |
| Approach: | They investigate human perceptions of emotions and sentiments in oral history interviews . they show that human perception for different emotions is ambiguous and subjective . authors propose deep learning as a way to categorize and search emotions . |
| Outcome: | The proposed techniques can be used to search and index audiovisual interviews . the authors show that human perceptions differ for different emotions . |
Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism (2022.lrec-1)
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| Challenge: | a recent study has established sentiment analysis as an alluring problem, but many feelings are left unexplored. |
| Approach: | They propose a framework to learn the polarity of emotions from Twitter posts . they compare optimism detection with sentiment analysis and hate speech detection . |
| Outcome: | The proposed framework differs between optimistic and pessimistic users on the Optimism/Pessimism Twitter dataset. |
Dataset and Baseline for Automatic Student Feedback Analysis (2022.lrec-1)
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| Challenge: | Currently, student feedback is collected manually, but it does not indicate the student's opinion on different aspects of the teaching/learning process. |
| Approach: | They propose to annotate student feedback corpus which contains 3000 instances . they propose a hierarchical taxonomy for aspect categorization, which covers all areas . |
| Outcome: | The proposed model can be used for aspects analysis, document level sentiment analysis and document level analysis. |
EENLP: Cross-lingual Eastern European NLP Index (2022.lrec-1)
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Alexey Tikhonov, Alex Malkhasov, Andrey Manoshin, George-Andrei Dima, Réka Cserháti, Md.Sadek Hossain Asif, Matt Sárdi
| Challenge: | Existing NLP resources for Eastern European languages are sparse. |
| Approach: | They propose to use existing Eastern European language resources to build cross-lingual datasets for five different semantic tasks to support commonsense reasoning. |
| Outcome: | The proposed model trains on 104 languages and shows impressive results on text analysis tasks. |
Slovene SuperGLUE Benchmark: Translation and Evaluation (2022.lrec-1)
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| Challenge: | a new benchmarking suite for natural language processing (NLP) is proposed to measure progress in the area of natural language understanding. |
| Approach: | They propose to use machine translation to translate a superGLUE benchmark into Slovene . they propose to combine monolingual, cross-lingual, and multilingual models . |
| Outcome: | The proposed model is superior to multilingual models but lags behind the best English models. |
Speech Resources in the Tamasheq Language (2022.lrec-1)
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Marcely Zanon Boito, Fethi Bougares, Florentin Barbier, Souhir Gahbiche, Loïc Barrault, Mickael Rouvier, Yannick Estève
| Challenge: | In this paper, we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger . we share unlabeled audio data in five languages: french, Fulfulde, Hausa, Tamaheq and Zarma . |
| Approach: | They present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. |
| Outcome: | The proposed datasets are used in the IWSLT 2022 low-resource speech translation track . they consist of radio recordings from daily broadcast news in Niger and Mali . |
Aesop’s fable “The North Wind and the Sun” Used as a Rosetta Stone to Extract and Map Spoken Words in Under-resourced Languages (2022.lrec-1)
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| Challenge: | A method of semi-automatic word spotting in minority languages is described . parallel text translations have made it possible to construct concordances for the Bible . |
| Approach: | They propose a method of semi-automatic word spotting in minority languages . they use orthographic similarity, word position and context to find out how a dozen words were translated in over 200 versions collected in the field. |
| Outcome: | The proposed method was validated on 200 translations of the Aesop fable in romance and french polynesia using orthographic similarity, word position and context. |
Multilingual Open Text Release 1: Public Domain News in 44 Languages (2022.lrec-1)
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| Challenge: | a corpus of permissively licensed text is being developed in 44 languages, many of which have limited existing text resources for natural language processing. |
| Approach: | They propose to create a multilingual corpus containing text in 44 languages . they describe their process for collecting, filtering, and processing the data . |
| Outcome: | The first release of the corpus contains over 2.8 million news articles and an additional 1 million short snippets published between 2001–2022 and collected from Voice of America news websites. |
TweetTaglish: A Dataset for Investigating Tagalog-English Code-Switching (2022.lrec-1)
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| Challenge: | a large dataset is available to study Tagalog-English code-switching in low-resource settings. |
| Approach: | They propose to use a large dataset to investigate Tagalog-English code-switching . they use linguistic data from Tagalogue and Tagalit-English to investigate their results . |
| Outcome: | The proposed dataset achieves a strong performance benchmark for Tagalog-English code-switching. |
Jojajovai: A Parallel Guarani-Spanish Corpus for MT Benchmarking (2022.lrec-1)
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Luis Chiruzzo, Santiago Góngora, Aldo Alvarez, Gustavo Giménez-Lugo, Marvin Agüero-Torales, Yliana Rodríguez
| Challenge: | a corpus of Guarani-Spanish text is presented that is aligned at sentence level . the long history of language contact between Guaran and Spanish in South America has resulted in many interesting language varieties . |
| Approach: | They propose to align Guarani-Spanish text at sentence level with 30,000 sentence pairs and a test set. |
| Outcome: | The proposed corpus contains about 30,000 sentence pairs and is structured as a collection of subsets from different sources, further split into training, development and test sets. |
Assessing Multilinguality of Publicly Accessible Websites (2022.lrec-1)
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| Challenge: | multilingualism on the Web is a problem not only at the world level, but also at the European and regional level. |
| Approach: | They propose a tool that automatically analyses the language diversity of the Web and propose indicators and methodologies to measure multilingualism of European websites. |
| Outcome: | The proposed tool can be independently run at set intervals and concludes that multilingualism on the Web is still a problem not only at the world level, but also at the European and regional level. |
A Methodology for Building a Diachronic Dataset of Semantic Shifts and its Application to QC-FR-Diac-V1.0, a Free Reference for French (2022.lrec-1)
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| Challenge: | Existing algorithms to detect semantic shifts have been criticized for their difficulty in evaluating them. |
| Approach: | They propose a method for building a reference dataset for semantic shift detection . they use a word-sense disambiguation model to associate a date of first appearance to all senses of a term . |
| Outcome: | The proposed method is based on a word-sense disambiguation model . significant changes in sense distributions and stability are detected . the resulting words are inspected by experts using a dedicated interface . |
CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Models (2022.lrec-1)
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| Challenge: | CRASS data set and benchmark provide novel test scheme to evaluate large language models . authors present and explain the CRAS data set, a novel basis to test reasoning and natural language understanding of LLMs . |
| Approach: | They introduce a new test scheme utilizing questionized counterfactual conditionals to evaluate large language models. |
| Outcome: | The proposed model sets out to be the most powerful and valid tool to evaluate large language models. |
Evaluating Gender Bias in Speech Translation (2022.lrec-1)
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| Challenge: | Existing evaluation techniques for gender biases are lacking in the field of machine translation. |
| Approach: | They propose to use a free evaluation set to evaluate gender bias in speech translation. |
| Outcome: | The proposed set is the speech version of WinoMT, an MT challenge set. |
Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training (2022.lrec-1)
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| Challenge: | Recent methods have obtained promising results by extracting relation labels from participants . obtaining linguistic annotations from novice crowdworkers is difficult . crowdsourcing allows for fast and cost-effective collection of labelled data, but because tasks need to be intuitive, crowdworker cannot be asked to perform them. |
| Approach: | They propose to use a selection-only approach to obtain linguistic annotations from novices . current study shows that the method is cost- and time-intensive . |
| Outcome: | The current study shows that selection and training improves the agreement between workers and gold labels, but the method is cost- and time-intensive. |
TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users (2022.lrec-1)
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| Challenge: | Social media are heavily used by many users to share their mental health concerns and diagnoses. |
| Approach: | They propose a dynamic thresholding technique that adjusts the classifier’s sensitivity as a function of the number of posts a user has. |
| Outcome: | The proposed method reduces the margin between users with many and few posts, on average, by 45% across all methods and increases overall performance, onaverage, by 33%. |
SpecNFS: A Challenge Dataset Towards Extracting Formal Models from Natural Language Specifications (2022.lrec-1)
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Sayontan Ghosh, Amanpreet Singh, Alex Merenstein, Wei Su, Scott A. Smolka, Erez Zadok, Niranjan Balasubramanian
| Challenge: | Existing methods for building formal semantic representations of specification texts are laborious and error-prone. |
| Approach: | They propose to use SpecIR to model sentences appearing in NFS specification documents as IF-THEN statements and introduce a representation language to parse them. |
| Outcome: | The proposed models achieve an F1 score of only 60.5 and 33.3 when using a state-of-the-art language model. |
Argument Similarity Assessment in German for Intelligent Tutoring: Crowdsourced Dataset and First Experiments (2022.lrec-1)
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| Challenge: | Recent years have seen increasing interest in applying natural language processing (NLP) applications to the field of education. |
| Approach: | They propose an NLP-based system that supports german secondary school students in an argumentative writing exercise. |
| Outcome: | The proposed system will support students in a German school exercise . the system will assess similarity between arguments in snippets of argumentative text . |
Leveraging Pre-trained Language Models for Gender Debiasing (2022.lrec-1)
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| Challenge: | Existing methods to reduce gender bias in natural language are costly and time-consuming. |
| Approach: | They propose a method to generate gender variants for a given text using pre-trained language models as the resource without any task-specific labelled data. |
| Outcome: | The proposed method can reduce gender bias in a language generation context without a task-specific labelled data. |
Unsupervised Embeddings with Graph Auto-Encoders for Multi-domain and Multilingual Hate Speech Detection (2022.lrec-1)
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| Challenge: | Hate speech detection is a challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author. |
| Approach: | They propose an unsupervised approach to learn embeddings for hate speech detection using Graph Auto-Encoders (GAE) they represent texts as nodes of a graph and use transformer layer and convolutional layer to encode them in low-dimensional space. |
| Outcome: | The proposed method shows competitive results on small datasets. |
FQuAD2.0: French Question Answering and Learning When You Don’t Know (2022.lrec-1)
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| Challenge: | Question Answering, including Reading Comprehension, has seen significant scientific breakthroughs over the past few years . but most of these breakthroughs are centered on the English language . |
| Approach: | They propose a dataset to train Question Answering models in the French language . they extend the dataset to 17,000+ unanswerable questions annotated adversarially . |
| Outcome: | The proposed dataset makes it possible to train French Question Answering models with the ability to distinguish unanswerable questions from answerable ones. |
Large-Scale Hate Speech Detection with Cross-Domain Transfer (2022.lrec-1)
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| Challenge: | Existing datasets for hate speech detection are limited due to the labor cost. |
| Approach: | They construct large-scale tweet datasets for hate speech detection in English and a low-resource language, Turkish, consisting of human-labeled 100k tweets per each. |
| Outcome: | The proposed datasets outperform conventional bag-of-words and neural models by at least 5% in English and 10% in Turkish for large-scale hate speech detection. |
GLoHBCD: A Naturalistic German Dataset for Language of Health Behaviour Change on Online Support Forums (2022.lrec-1)
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| Challenge: | Existing motivational interviewing methods lack the deep understanding of user utterances that is essential to the spirit of motivational interviews. |
| Approach: | They propose to use a German dataset of naturalistic language around health behaviour change to examine the motivational state of the user. |
| Outcome: | The proposed dataset of naturalistic language around health behaviour change is based on a weight loss forum in germany and is evaluated using theoretically grounded motivational interviewing categories. |
Creating a Data Set of Abstractive Summaries of Turn-labeled Spoken Human-Computer Conversations (2022.lrec-1)
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| Challenge: | Digital recorded written and spoken dialogues are becoming more available due to the growing popularity of online messenger services and chatbots. |
| Approach: | They propose to use Dutch spoken human-computer conversations, an annotation layer of turn labels, and conversational abstractive summaries of user answers to build a conversational agent. |
| Outcome: | The proposed system can be integrated into a conversational agent. |
OpenEL: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue (2022.lrec-1)
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| Challenge: | Named entity recognition (NER), named entity linking and discourse modeling are crucial aspects of natural language understanding for open domain dialogue systems. |
| Approach: | They present an annotated multi-domain corpus for linking entities in open-domain dialogue . they use dialogue context and anaphora resolution to assess the effectiveness of the task . |
| Outcome: | The OpenEL corpus is an annotated multi-domain corpus for linking entities in open-domain dialogue . the system Flair + BLINK has the best performance with a 0.65 F1 score . |
Collecting Visually-Grounded Dialogue with A Game Of Sorts (2022.lrec-1)
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| Challenge: | referring in conversation is a collaborative process that cannot be described as an exchange of minimally-specified referring expressions. |
| Approach: | They propose a collaborative image ranking task that allows players to agree on a sorting criteria. |
| Outcome: | The proposed game aims to ground referring expressions in visually-grounded dialogues . it uses a game-like approach to rank images in a role-symmetric dialogue . |
CoRoSeOf - An Annotated Corpus of Romanian Sexist and Offensive Tweets (2022.lrec-1)
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| Challenge: | Using CoRoSeOf, we manually annotate social media for sexist and offensive language. |
| Approach: | They introduce a large corpus of Romanian social media manually annotated for sexist and offensive language. |
| Outcome: | The proposed corpus contains 39 245 tweets annotated by multiple annotators with an agreement rate of Fleiss’= 0.45 . |
ArMIS - The Arabic Misogyny and Sexism Corpus with Annotator Subjective Disagreements (2022.lrec-1)
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| Challenge: | sexist and misogynistic language is increasingly used on social media in recent years . there are few benchmarks for misogorical annotations in Arabic . a dataset characterized by religious beliefs does not reconcile disagreements . |
| Approach: | They propose to use an Arabic misogyny and sexism dataset to analyze disagreements between religious annotators. |
| Outcome: | The proposed dataset shows that disagreements between annotators with different religious beliefs can be reconciled . |
Annotating Interruption in Dyadic Human Interaction (2022.lrec-1)
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| Challenge: | Existing interruption and turn switch classification methods are not yet available. |
| Approach: | They propose a new interruption annotation schema that integrates existing interruption and turn switch classification methods to annotate different types of interruptions. |
| Outcome: | The proposed method can distinguish smooth turn exchange, backchannel and interruption (including interruption types) and to annotate dyadic conversation. |
The Causal News Corpus: Annotating Causal Relations in Event Sentences from News (2022.lrec-1)
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Fiona Anting Tan, Ali Hürriyetoğlu, Tommaso Caselli, Nelleke Oostdijk, Tadashi Nomoto, Hansi Hettiarachchi, Iqra Ameer, Onur Uca, Farhana Ferdousi Liza, Tiancheng Hu
| Challenge: | Existing annotation guidelines for event causality focus on only explicit relations or clauses. |
| Approach: | They propose an annotation schema for event causality that addresses these concerns . they annotated 3,559 event sentences from protest event news with labels on whether it contains causal relations or not. |
| Outcome: | The proposed annotation schema for event causality addresses these concerns . it performs well with 81.20% F1 score on test set and 83.46% in 5-folds cross-validation . |
Samrómur: Crowd-sourcing large amounts of data (2022.lrec-1)
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| Challenge: | Samrómur is the largest prompted speech collection effort for Icelandic so far and verification is as monumental as the collection itself. |
| Approach: | They propose to collect large and diverse corpus for automatic speech recognition and similar tools using crowd-sourced donations. |
| Outcome: | The collected utterances are based on the Mozilla Common Voice platform and are available for free on the Samrómur collection platform. |
An Annotated Corpus of Textual Explanations for Clinical Decision Support (2022.lrec-1)
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Roland Roller, Aljoscha Burchardt, Nils Feldhus, Laura Seiffe, Klemens Budde, Simon Ronicke, Bilgin Osmanodja
| Challenge: | In recent years, machine learning for clinical decision support has gained more and more attention. |
| Approach: | They propose to use XAI to provide an explanation of a model's decision making process by constructing a corpus of sentences that are annotated with different semantic layers. |
| Outcome: | The proposed models outperform physicians on very specific, narrow tasks or can help physicians to work more efficiently. |
LARD: Large-scale Artificial Disfluency Generation (2022.lrec-1)
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| Challenge: | Existing datasets suffer from class imbalance issues, causing performance problems . Disfluency detection is a critical task in real-time dialogue systems . |
| Approach: | They propose a method for generating complex and realistic artificial disfluencies with little effort using a large-scale dataset. |
| Outcome: | The proposed method can handle repetitions, replacements, and restarts on a large-scale dataset with disfluencies. |
The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues (2022.lrec-1)
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Yuru Jiang, Yang Xu, Yuhang Zhan, Weikai He, Yilin Wang, Zixuan Xi, Meiyun Wang, Xinyu Li, Yu Li, Yanchao Yu
| Challenge: | Existing datasets focus on relation extraction between two entities in one sentence, and some focus on cross-sentence relationships. |
| Approach: | They propose to use a Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships. |
| Outcome: | The proposed dataset extracts relationships between 140 entities on the CRECIL corpus and another existing relation extraction corpus. |
The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic (2022.lrec-1)
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| Challenge: | Various corpora of various sizes and representing different genres, have been created for various Arabic dialects. |
| Approach: | They propose to create a specialized corpus of Bahraini Arabic dialect, which includes written texts as well as transcripts of audio files. |
| Outcome: | The proposed corpus includes 620K words representing the Bahraini Arabic dialect . the annotated corpus is available to support researchers interested in Arabic NLP . |
A Universal Dependencies Treebank of Ancient Hebrew (2022.lrec-1)
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| Challenge: | Using a rule-based parser, we construct a treebank with morphological annotations of Ancient Hebrew . the Hebrew Scriptures are a collection of 39 books written in the first millennium BC in Ancient Hebrew. |
| Approach: | They propose to use a Universal Dependencies treebank with morphological annotations of Ancient Hebrew for comparative study with ancient translations and analysis of Hebrew syntax. |
| Outcome: | The proposed treebank can be used in comparative study with ancient translations and analysis of Hebrew syntax. |
Hate Speech Dynamics Against African descent, Roma and LGBTQI Communities in Portugal (2022.lrec-1)
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| Challenge: | Existing resources for detecting hate speech in Portugal are limited to spatiotemporally delimited phenomena, such as the dynamics of online hate speech before and during the Covid-19 pandemic. |
| Approach: | They propose to use a dataset to analyze online hate speech in Portugal before and after the Covid-19 pandemic . they provide statistics on the distribution of tweets included in the dataset and analyze the availability over time of tweet targeting the above-mentioned communities. |
| Outcome: | The proposed dataset contains 63,450 tweets, posted before and after the official declaration of Covid-19 as a pandemic by online users in Portugal. |
Evolving Large Text Corpora: Four Versions of the Icelandic Gigaword Corpus (2022.lrec-1)
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| Challenge: | The Icelandic Gigaword Corpus was first published in 2018 and has since grown to include more than 50 million words. |
| Approach: | They describe the evolution of the Icelandic Gigaword Corpus in its first four years . they show how the corpus has grown almost 50% in size from the first version to the fourth . |
| Outcome: | The Gigaword corpus has grown 50% from its first version to its fourth version and is now available under permissive licenses. |
A Pragmatics-Centered Evaluation Framework for Natural Language Understanding (2022.lrec-1)
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| Challenge: | a number of studies have suggested that models induce universal text representations . current benchmarks focus on semantic phenomena, so pragmatics needs to be the focus . |
| Approach: | They propose a benchmark that unites 11 pragmatics-focused evaluation datasets for English. |
| Outcome: | The proposed benchmark shows that natural language inference does not result in genuinely universal representations. |
Conversational Analysis of Daily Dialog Data using Polite Emotional Dialogue Acts (2022.lrec-1)
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| Challenge: | Literature suggests that analysis and use of social cues is beneficial for human-robot interaction. |
| Approach: | They propose to add linguistic politeness cues to conversational analysis and to find correlations between them. |
| Outcome: | The results confirm that utterances with Anger and Disgust are more likely to be polite than others. |
Inducing Discourse Marker Inventories from Lexical Knowledge Graphs (2022.lrec-1)
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| Challenge: | Discourse marker inventories are important tools for the development of discourse parsers and corpora with discourse annotations. |
| Approach: | They explore the potential of multilingual lexical knowledge graphs to induce multilingual discourse marker lexicons using concept propagation methods previously developed in translation inference across dictionaries. |
| Outcome: | The proposed method can induce multilingual discourse marker lexicons using multilingual knowledge graphs. |
Story Trees: Representing Documents using Topological Persistence (2022.lrec-1)
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| Challenge: | Topological data analysis (TDA) focuses on the inherent shape of (spatial) data. |
| Approach: | They propose to use topological data analysis to represent document structure as story trees . story trees are hierarchical representations created from semantic vector representations of sentences . |
| Outcome: | The proposed methods can be used to extract summary summaries from news stories using story trees. |
Extracting and Analysing Metaphors in Migration Media Discourse: towards a Metaphor Annotation Scheme (2022.lrec-1)
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| Challenge: | Using metaphors in media discourse is an increasingly researched topic . media are an important shaper of social reality and metaphors indicate how we think about issues through references to other things. |
| Approach: | They propose a neural transfer learning method for detecting metaphorical sentences in Slovene . scheme can be used for future metaphor annotations of other socially relevant topics . |
| Outcome: | The proposed method can be used for future metaphor annotations of other socially relevant topics. |
DDisCo: A Discourse Coherence Dataset for Danish (2022.lrec-1)
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| Challenge: | Discourse coherence models have been developed using randomly shuffled texts instead of highly edited and coherent data. |
| Approach: | They propose to annotate Danish Wikipedia and Reddit for discourse coherence using real-world text instead of artificially incoherent text for training and testing models. |
| Outcome: | The proposed model performs well on annotated texts from the Danish Wikipedia and Reddit dataset. |
LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments (2022.lrec-1)
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| Challenge: | Argumentation plays a central role in human communication, where refuting or attacking others’ arguments is a common persuasion strategy. |
| Approach: | They propose a novel annotation scheme that captures common modes and complex rhetorical moves in attacks along with the implicit presuppositions and value judgments. |
| Outcome: | The proposed scheme shows moderate agreement between the two annotations, indicating that human annotation is feasible. |
BeSt: The Belief and Sentiment Corpus (2022.lrec-1)
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Jennifer Tracey, Owen Rambow, Claire Cardie, Adam Dalton, Hoa Trang Dang, Mona Diab, Bonnie Dorr, Louise Guthrie, Magdalena Markowska, Smaranda Muresan, Vinodkumar Prabhakaran, Samira Shaikh, Tomek Strzalkowski
| Challenge: | a corpus of propositional content is a set of cognitive attitudes of different agents towards a text . propositional attitudes are a cognitive attitude, including belief and sentiment, towards . |
| Approach: | They propose a corpus which records cognitive state: who believes what, who has what sentiment . they use newswire and discussion forums in Chinese, English, and Spanish . |
| Outcome: | The proposed corpus records who believes what (i.e., factuality) and who has what sentiment towards what. |
MOTIF: Contextualized Images for Complex Words to Improve Human Reading (2022.lrec-1)
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| Challenge: | Existing studies have shown that multimodal information is crucial for concept formation, accordingly for language acquisition. |
| Approach: | They collect a multimodal dataset enriched with complex word annotations and validated image match. |
| Outcome: | The proposed dataset contains 1125 comprehension texts retrieved from Wikipedia Simple Corpus . |
Challenges with Sign Language Datasets for Sign Language Recognition and Translation (2022.lrec-1)
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Mirella De Sisto, Vincent Vandeghinste, Santiago Egea Gómez, Mathieu De Coster, Dimitar Shterionov, Horacio Saggion
| Challenge: | Sign Languages are the primary means of communication for at least half a million people in Europe . however, the development of SL recognition and translation tools is slowed down by resource scarcity and data formats are not suitable for machine learning. |
| Approach: | They propose a framework to unify available resources and facilitate SL research for different languages. |
| Outcome: | The proposed framework is based on a set of ELAN files and returns textual and visual data ready to train SL recognition and translation models. |
A Low-Cost Motion Capture Corpus in French Sign Language for Interpreting Iconicity and Spatial Referencing Mechanisms (2022.lrec-1)
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| Challenge: | Existing tools for automatic translation of sign language videos into transcribed texts are limited. |
| Approach: | They propose to use deep learning methods to circumvent the use of models in spatial referencing recognition by a 3D skeleton and a software program to capture and post-process the LSF-SHELVES corpus. |
| Outcome: | The proposed system targets iconicity and spatial referencing in french sign language . it is light-weight and low-cost to collect data from a large panel of signers . |
The CLAMS Platform at Work: Processing Audiovisual Data from the American Archive of Public Broadcasting (2022.lrec-1)
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| Challenge: | The Computational Linguistics Applications for Multimedia Services (CLAMS) platform provides access to computational content analysis tools for multimedia material. |
| Approach: | They describe the CLAMS platform as it is and its initial prototype implementation from 2019 . they use a common multi-modal representation language called MMIF to create a workflow . |
| Outcome: | The CLAMS platform is a new version of an initial prototype from 2019 . it can be used to add metadata to mass-digitized multimedia collections . the proposed version is based on the American Archive of Public Broadcasting data . |
BU-NEmo: an Affective Dataset of Gun Violence News (2022.lrec-1)
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Carley Reardon, Sejin Paik, Ge Gao, Meet Parekh, Yanling Zhao, Lei Guo, Margrit Betke, Derry Tanti Wijaya
| Challenge: | Using a dataset that contains headline and image pairings from 840 news articles, we explore the relationship between image and text influence on human emotional response. |
| Approach: | They propose to use a U.S. gun violence news dataset that contains headline and image pairings from 840 news articles with 15K high-quality crowdsourced annotations on emotional responses. |
| Outcome: | The proposed dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. |
RoomReader: A Multimodal Corpus of Online Multiparty Conversational Interactions (2022.lrec-1)
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Justine Reverdy, Sam O’Connor Russell, Louise Duquenne, Diego Garaialde, Benjamin R. Cowan, Naomi Harte
| Challenge: | The corpus of multimodal, multiparty conversational interactions explored in RoomReader can be used to study a wide range of phenomena in online multimodal interaction. |
| Approach: | They propose to use RoomReader to explore multimodal cues of conversational engagement and behavioural aspects of collaborative interaction in online environments. |
| Outcome: | The corpus was developed within the wider RoomReader Project to explore multimodal cues of conversational engagement and behavioural aspects of collaborative interaction in online environments. |
Quevedo: Annotation and Processing of Graphical Languages (2022.lrec-1)
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| Challenge: | graphical languages use images to convey meaning, but they require specialized computational processing . graphical systems use visual features and exploit the two dimensions of the page as a fundamental feature for codifying meaning . |
| Approach: | They present a software tool for automatic processing of graphical languages . they use a command line application and library to collect and manage image datasets . |
| Outcome: | a new software tool is developed for the processing of graphical languages . the tool provides features for the collection and management of image datasets . |
Merkel Podcast Corpus: A Multimodal Dataset Compiled from 16 Years of Angela Merkel’s Weekly Video Podcasts (2022.lrec-1)
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| Challenge: | a dataset of 16 years of (almost) weekly Internet podcasts of former german chancellor Angela Merkel is presented. |
| Approach: | They propose to curate a German podcast corpus from 16 years of podcasts of former german chancellor Angela Merkel using audio-visual-text methods. |
| Outcome: | The proposed pipeline can be used to curate other datasets of similar nature, such as talk show contents. |
Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50 (2022.lrec-1)
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| Challenge: | Using crowdsourcing, we created a signer independent dataset for sign language processing. |
| Approach: | They propose to crowdsource a signer independent Kazakh-Russian Sign Language (KRSL) dataset. |
| Outcome: | The proposed dataset consists of 173 sentences performed by 50 signers for 43,250 video samples. |
Connecting a French Dictionary from the Beginning of the 20th Century to Wikidata (2022.lrec-1)
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| Challenge: | The Petit Larousse illustré is a French dictionary first published in 1905 . its value remains intact, but some descriptions are more historical than contemporary . wikidata identifiers can be used to identify, compare, and verify historically-situated representations . |
| Approach: | They propose a wikidata-based annotation of the Petit Larousse illustré entries from 1905 . they link the entries to current data sources and use them to extract, complement, and process knowledge. |
| Outcome: | The proposed lexical resource connects dictionary entries from 1905 to current data sources. |
Metaphor annotation for German (2022.lrec-1)
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| Challenge: | a corpus annotated for metaphors denotes entities or situations that are in some sense similar to the literal referent, but we believe it is of interest to research on metaphor in general. |
| Approach: | They present a German corpus annotated for metaphor in a project on register and propose to broaden the annotation to include metonymy. |
| Outcome: | The proposed corpus is compiled and annotated in a project on the interdependence of metaphors and register. |
NorDiaChange: Diachronic Semantic Change Dataset for Norwegian (2022.lrec-1)
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| Challenge: | NorDiaChange is the first dataset of diachronic semantic change on the lexical level for Norwegian. |
| Approach: | They describe a manual annotation process for a new dataset of diachronic semantic change for Norwegian. |
| Outcome: | The proposed dataset covers the time periods related to pre- and post-war events, oil and gas discovery in Norway, and technological developments. |
Exploring Transformers for Ranking Portuguese Semantic Relations (2022.lrec-1)
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| Challenge: | Using transformers, we rank instances of Portuguese lexico-semantic relations . despite the negative results, we see the reported experiments as another contribution for better understanding transformer-based language models like BERT and GPT. |
| Approach: | They investigated transformer-based language models for ranking instances of Portuguese lexico-semantic relations using weights based on likelihood of natural language sequences that transmitted the relation instances. |
| Outcome: | The proposed models are not correlated with redundancy, but lower for instances with longer and more specific arguments, and not useful when computing word similarity with network embeddings. |
Building Static Embeddings from Contextual Ones: Is It Useful for Building Distributional Thesauri? (2022.lrec-1)
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| Challenge: | contextual language models are dominant in the field of Natural Language Processing, but they are not suitable for all uses. |
| Approach: | They propose a method for building word or type-level embeddings from contextual models . they evaluate a large set of English nouns from the perspective of extracting semantic similarity relations . |
| Outcome: | The proposed method can be used to build word or type embeddings from contextual models . it can be exploited for a wide set of English nouns, showing it can improve distributional thesauri . |
Sentence Selection Strategies for Distilling Word Embeddings from BERT (2022.lrec-1)
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| Challenge: | Using language models to learn word embeddings is a key feature of transformer-based language models. |
| Approach: | They propose to use language models to learn high-quality word vectors from as few as 5 to 10 sentences with a careful selection strategy. |
| Outcome: | The proposed strategies can learn high-quality word vectors from as few as 5 to 10 sentences. |
DiaWUG: A Dataset for Diatopic Lexical Semantic Variation in Spanish (2022.lrec-1)
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| Challenge: | Existing approaches to dialectology have been limited and rarely address language variation regarding lexical meaning. |
| Approach: | They propose to use existing framework DURel and framework-embedded Word Usage Graphs to distinguish, visualize and interpret diatopic lexical semantic variation of contextualized words in Spanish from these perspectives. |
| Outcome: | The proposed dataset exploits existing frameworks for annotating word senses in context and framework-embedded Word Usage Graphs (WUGs) . it distinguishes, visualizes and interprets lexical semantic variation of contextualized words in Spanish from these two perspectives, i.e., semasiological and onomasiology. |
My Case, For an Adposition: Lexical Polysemy of Adpositions and Case Markers in Finnish and Latin (2022.lrec-1)
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| Challenge: | Polysemy between adpositions and case markers is high in casemarked languages . a singular adeposition can cover a wide range of semantic fields while occupying the same syntactic context. |
| Approach: | They propose to manually annotate adposition and case marker tokens in Finnish and Latin translations of Le Petit Prince. |
| Outcome: | The proposed method can be applied to Finnish and Latin translations of Le Petit Prince . it uses k-means clustering to group raw, contextualized BERT embeddings . |
WiC-TSV-de: German Word-in-Context Target-Sense-Verification Dataset and Cross-Lingual Transfer Analysis (2022.lrec-1)
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| Challenge: | Target Sense Verification is a binary disambiguation task that requires a sense-inventory-independent model to be developed. |
| Approach: | They propose a multi-domain dataset for German Target Sense Verification . they use a domain-independent instance to train and develop sense-inventory-independence models . |
| Outcome: | The proposed model fails to solve the German target Sense Verification dataset . the multi-domain dataset contains domain-bound subsets from four different domains . |
Re-train or Train from Scratch? Comparing Pre-training Strategies of BERT in the Medical Domain (2022.lrec-1)
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| Challenge: | Recent years have witnessed the widespread use of transfer learning techniques in Natural Language Processing (NLP) |
| Approach: | They train BERT models from scratch using many configurations involving general and medical corpora. |
| Outcome: | The initial corpus only has a weak influence when these are further pre-trained on a medical corpus. |
Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing (2022.lrec-1)
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| Challenge: | Existing approaches to understanding textual information are still far from achieving true natural language understanding (NLU). |
| Approach: | They propose a unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantics Parsing. |
| Outcome: | The proposed system can provide users with rich and diverse semantic information, help second-language learners, and integrate explicit semantic knowledge into downstream tasks and real-world applications. |
D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research (2022.lrec-1)
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| Challenge: | DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. |
| Approach: | They extracted metadata from more than 6 million DBLP publications to create the DB3 Discovery Dataset (D3) . they found that computer science is a growing research field (15% annually), with an active and collaborative researcher community. |
| Outcome: | The DBLP Discovery Dataset (D3) can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research. |
SciPar: A Collection of Parallel Corpora from Scientific Abstracts (2022.lrec-1)
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Dimitrios Roussis, Vassilis Papavassiliou, Prokopis Prokopidis, Stelios Piperidis, Vassilis Katsouros
| Challenge: | SciPar is a collection of parallel corpora created from openly available metadata of bachelor theses, master theses and doctoral dissertations hosted in institutional repositories, digital libraries and national archives. |
| Approach: | They propose to harvest and process openly available metadata from repositories to extract bilingual titles and abstracts from scientific publications. |
| Outcome: | The proposed corpora could be useful for cross-lingual plagiarism detection or adapting Machine Translation systems for translation of scientific texts and academic writing in general. |
CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms (2022.lrec-1)
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| Challenge: | Euphemisms are a difficult topic because they are subject to language change and humans may not agree on what is a euphemist. |
| Approach: | They analyze a corpus of potentially euphemistic terms (PETs) and examples from the GloWbE corpus to examine their meanings. |
| Outcome: | The proposed corpus of potentially euphemistic terms and examples from the GloWbE corpus show that PETs generally decrease negative and offensive sentiment. |
Camel Treebank: An Open Multi-genre Arabic Dependency Treebank (2022.lrec-1)
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| Challenge: | CAMELTB is an open-source dependency treebank of Arabic with 13 sub-corpora . texts are publicly available (out of copyright, creative commons, or under open licenses) |
| Approach: | They present the Camel Treebank, a 188K word open-source dependency treebank of Arabic. |
| Outcome: | The CAMELTB is a 188K word open-source dependency treebank of Arabic . the texts are publicly available (out of copyright, creative commons, or under open licenses) |
MentSum: A Resource for Exploring Summarization of Mental Health Online Posts (2022.lrec-1)
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| Challenge: | Mental health remains a significant challenge of public health worldwide . many use online platforms to share their mental health conditions and seek help . |
| Approach: | They analyze a dataset of over 24k user posts from Reddit and 43 mental health subreddits to generate a short summarization. |
| Outcome: | The proposed dataset compared over 24k user posts and 43 mental health subreddits . it shows that the summarization of these posts is faster and more accurate than previous studies. |
Klexikon: A German Dataset for Joint Summarization and Simplification (2022.lrec-1)
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| Challenge: | Traditionally, Text Simplification is a monolingual translation task where individual sentences are "translated" into a simplified version. |
| Approach: | They propose to use a dataset to jointly simplify long source documents by combining sentences from a source and their simplified counterparts. |
| Outcome: | The proposed system can summarize and simplify long source documents using almost 2,900 documents. |
Applying Automatic Text Summarization for Fake News Detection (2022.lrec-1)
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| Challenge: | Social media has been a driver for the spread of misleading and deliberately wrong information, as there is little to no veracity monitoring. |
| Approach: | They propose a framework that combines transformer-based language models with contextual information to circumvent sequential limits and related loss of information. |
| Outcome: | The proposed framework can circumvent sequential limits and related loss of information on two publicly available datasets and achieve state-of-the-art performance benchmarks. |
Increasing CMDI’s Semantic Interoperability with schema.org (2022.lrec-1)
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| Challenge: | CLARIN Concept Registry supports semantic interoperability, but does not extend beyond it. |
| Approach: | They propose to ground CMDI-based metadata using the CLARIN concept registry . they propose to use schema.org to map CMDi-based profiles to schema.com terms . |
| Outcome: | The proposed tool can map CMDI-based profiles to schema.org terms . the tool can be used to map CCR-based metadata to schema ontologies . |
RefCo and its Checker: Improving Language Documentation Corpora’s Reusability Through a Semi-Automatic Review Process (2022.lrec-1)
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| Challenge: | QUEST aims to ensure the reusability of audio-visual datasets . documenting endangered and minority languages is one of the current goals of linguistic research . |
| Approach: | They propose to establish a semi-automatic review process for existing and work-in-progress corpora based on these criteria . goal is to increase the quality of a corpus by increasing its reusability. |
| Outcome: | The project aims to improve the quality of language documentation by increasing its reusability. |
Identification and Analysis of Personification in Hungarian: The PerSECorp project (2022.lrec-1)
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| Challenge: | despite recent findings on the conceptual and linguistic organization of personification, we have relatively little knowledge about its lexical patterns and grammatical templates. |
| Approach: | They propose a corpus-driven approach to personification analysis in cognitive linguistics . they use a semi-automatically processed corpus to annotate personifying linguistic structures . |
| Outcome: | The proposed method consists of annotating a semi-automatic corpus of car reviews in Hungarian . the corpus is structured and annotated manually, and gives an overview of possible data types . |
ISO-based Annotated Multilingual Parallel Corpus for Discourse Markers (2022.lrec-1)
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Purificação Silvano, Mariana Damova, Giedrė Valūnaitė Oleškevičienė, Chaya Liebeskind, Christian Chiarcos, Dimitar Trajanov, Ciprian-Octavian Truică, Elena-Simona Apostol, Anna Baczkowska
| Challenge: | Discourse markers carry information about the discourse structure and organization, and also signal local dependencies or epistemic stance of speaker. |
| Approach: | They propose an ISO-based annotated multilingual parallel corpus for discourse markers . they propose an annotation scheme for discourse relations with a plug-in to ISO 24617-2 . |
| Outcome: | The proposed language resource is based on an ISO-based annotated multilingual parallel corpus of discourse markers. |
LIP-RTVE: An Audiovisual Database for Continuous Spanish in the Wild (2022.lrec-1)
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| Challenge: | Speech perception is considered as a purely auditory process, but it is a multi-modal process involving multiple senses. |
| Approach: | They propose to use a semi-automatically annotated audiovisual database to deal with unconstrained natural Spanish. |
| Outcome: | The proposed system can be used to estimate speech recognition systems in the Deep Learning era. |
Modality Alignment between Deep Representations for Effective Video-and-Language Learning (2022.lrec-1)
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| Challenge: | Existing Video-and-Language models do not take into account the different characteristics of video and text representations. |
| Approach: | They propose a method that exploits Centered Kernel Alignment (CKA) to enhance cross-modality attention by combining multiple modalities. |
| Outcome: | The proposed method outperforms conventional multi-modal methods significantly on video QA tasks with +3.57% accuracy increment compared to the baseline in a popular benchmark dataset. |
Mutual Gaze and Linguistic Repetition in a Multimodal Corpus (2022.lrec-1)
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| Challenge: | a study of linguistic repetitions and mutual understanding is conducted . we find no compelling correlation between mutual gaze and duration of the event . |
| Approach: | They investigate the correlation between mutual gaze and linguistic repetition, a form of alignment, which they take as evidence of mutual understanding. |
| Outcome: | The proposed method is based on the Multisimo corpus, a multimodal corpus which provides authentic task-based interactions among three participants. |
Multidimensional Coding of Multimodal Languaging in Multi-Party Settings (2022.lrec-1)
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Christophe Parisse, Marion Blondel, Stéphanie Caët, Claire Danet, Coralie Vincent, Aliyah Morgenstern
| Challenge: | In natural language settings, many interactions include more than two speakers and real-life interpretation is based on all types of information available in all modalities. |
| Approach: | They propose to use a coding tool to analyze spontaneous interactions in family dinner settings. |
| Outcome: | The proposed method compares the language of two adults and three children in family dinner settings using either French, or French sign language. |
Constructing a Lexical Resource of Russian Derivational Morphology (2022.lrec-1)
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| Challenge: | In Natural Language Processing of Russian, the inflection is satisfactorily processed, but there are only a few machine-trackable resources that capture derivations . |
| Approach: | They propose to use machine-learning methods to improve Russian inflection and derivational resources by using a database of more than 300 thousand lexemes and 164 thousand binary derivations. |
| Outcome: | The proposed method includes more than 300 thousand lexemes connected with more than 164 thousand binary derivational relations. |
Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship (2022.lrec-1)
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Temuulen Khishigsuren, Gábor Bella, Khuyagbaatar Batsuren, Abed Alhakim Freihat, Nandu Chandran Nair, Amarsanaa Ganbold, Hadi Khalilia, Yamini Chandrashekar, Fausto Giunchiglia
| Challenge: | a method to enrich lexical resources with content relating to linguistic diversity is proposed . Typology-based approaches are being used to improve cross-lingual NLP tasks . |
| Approach: | They propose a method to enrich lexical resources with content relating to linguistic diversity based on lexica. |
| Outcome: | The proposed method can be used to improve cross-lingual NLP tasks by removing the need for parallel textual corpora or cross-linguistic transfer from high-to-low-resourced languages. |
Towards Latvian WordNet (2022.lrec-1)
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Peteris Paikens, Mikus Grasmanis, Agute Klints, Ilze Lokmane, Lauma Pretkalniņa, Laura Rituma, Madara Stāde, Laine Strankale
| Challenge: | Currently the dataset consists of 6432 words linked in 5528 synsets . the goal is to provide a structured lexical-semantic resource for Latvian word sense disambiguation . |
| Approach: | They propose to use Princeton's word sense definition and sense linking principles to create a Latvian wordnet . they use corpus evidence and an online dictionary to build a lexical-semantic resource . |
| Outcome: | The proposed resource is based on the Princeton WordNet and is available in Latvian . the initial portion of the data is available for download . |
Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings (2022.lrec-1)
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| Challenge: | a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages is proposed . a number of experiments with Scandinavian language datasets yield state-of-the-art results using a rule-based sentiment analysis algorithm. |
| Approach: | They propose a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages. |
| Outcome: | The proposed method is based on the English Sentiwordnet and a thesaurus in one of the target languages. |
A Thesaurus-based Sentiment Lexicon for Danish: The Danish Sentiment Lexicon (2022.lrec-1)
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| Challenge: | a newly published Danish sentiment lexicon with a high lexical coverage was compiled using lexicographic methods and linked data. |
| Approach: | They propose to use lexicographic methods to compile a Danish sentiment lexicon with a high lexical coverage by linking words from a thesaurus to a comprehensive monolingual dictionary. |
| Outcome: | The proposed lexicon contains 13,859 Danish polarity lemmas and includes morphological information. |
IndoUKC: A Concept-Centered Indian Multilingual Lexical Resource (2022.lrec-1)
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| Challenge: | a new multilingual lexical database for Indian languages is proposed . the database provides words and crosslingually mapped word meanings specific to Indian languages and cultures. |
| Approach: | They propose to create a multilingual lexical database for Indian languages called IndoUKC . the database is based on existing IndoWordNet resources and is available for browsing . |
| Outcome: | The proposed database is based on the existing IndoWordNet resource and is available for download through the LiveLanguage data catalogue. |
Korean Language Modeling via Syntactic Guide (2022.lrec-1)
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| Challenge: | Existing research on pre-trained language models focuses on widely-used languages . however, not every language can benefit from such models due to computational resources . |
| Approach: | They propose to build a pre-trained language model that understands the linguistic phenomena in the target language with low resources. |
| Outcome: | The proposed model improves the performance of Korean language understanding tasks. |
A Whole-Person Function Dictionary for the Mobility, Self-Care and Domestic Life Domains: a Seedset Expansion Approach (2022.lrec-1)
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Ayah Zirikly, Bart Desmet, Julia Porcino, Jonathan Camacho Maldonado, Pei-Shu Ho, Rafael Jimenez Silva, Maryanne Sacco
| Challenge: | Functional limitations affect a large proportion of the world's population, according to the World Health Organization. |
| Approach: | They propose to use a set of manually annotated clinical notes to build a terminology for whole-person function in the domains of mobility, self-care and domestic life. |
| Outcome: | The proposed terminologies were built and evaluated using a small set of manually annotated clinical notes. |
Placing multi-modal, and multi-lingual Data in the Humanities Domain on the Map: the Mythotopia Geo-tagged Corpus (2022.lrec-1)
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Voula Giouli, Anna Vacalopoulou, Nikolaos Sidiropoulos, Christina Flouda, Athanasios Doupas, Giorgos Giannopoulos, Nikos Bikakis, Vassilis Kaffes, Gregory Stainhaouer
| Challenge: | Using mythology as a starting point, visitors of Northern Greece will have a multi-faceted experience using a corpus of textual data supplemented with images, and video. |
| Approach: | They propose to integrate a multi-lingual corpus with a dedicated database with advanced indexing, linking and search functionalities into a platform for scholarly research in the digital humanities. |
| Outcome: | The proposed infrastructure will be integrated into a platform aimed at providing a multi-faceted experience to visitors of Northern Greece using mythology as a starting point. |
An Architecture of resolving a multiple link path in a standoff-style data format to enhance the mobility of language resources (2022.lrec-1)
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| Challenge: | XML data formats that represent a semantic data model are difficult to convert into other formats . this difficulty causes a problem in the reuse of data especially in a personal data management environment. |
| Approach: | They propose a new approach to transform a link structure into an instance structure on a marked-up scheme. |
| Outcome: | The proposed format is based on a so-called standoff-style data format in XML . the proposed format injures the mobility of data because it is hard to convert it into other formats . |
A Corpus of German Citizen Contributions in Mobility Planning: Supporting Evaluation Through Multidimensional Classification (2022.lrec-1)
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| Challenge: | Political authorities in democratic countries consult the public in order to allow citizens to voice their ideas and concerns on specific issues. |
| Approach: | They propose a publicly-available corpus that includes citizen contributions from six mobility-related planning processes in five german municipalities. |
| Outcome: | The proposed corpus includes several thousand citizen contributions from six mobility-related planning processes in five German municipalities. |
Overlooked Data in Typological Databases: What Grambank Teaches Us About Gaps in Grammars (2022.lrec-1)
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| Challenge: | Typological databases can contain a wealth of information beyond the collection of linguistic properties across languages. |
| Approach: | They use a typological database Grambank to classify and quantify the comments that accompany coded values and aggregate these comments and coded value to derive a level of description for 17 grammatical domains. |
| Outcome: | The proposed model can estimate the description level of 17 grammatical domains in the available resources for the given language. |
Hong Kong: Longitudinal and Synchronic Characterisations of Protest News between 1998 and 2020 (2022.lrec-1)
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| Challenge: | This paper examines the utility and timeliness of the Hong Kong Protest News Dataset . it sheds light on whether depth and/or manner of reporting changed over time . |
| Approach: | They use the Hong Kong Protest News Dataset to investigate synchronic news characterisations of protests in Hong Kong between 1998 and 2020. |
| Outcome: | The dataset sheds light on whether depth and/or manner of reporting changed over time, and if so, in what ways, or in response to what. |
Nunc profana tractemus. Detecting Code-Switching in a Large Corpus of 16th Century Letters (2022.lrec-1)
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Martin Volk, Lukas Fischer, Patricia Scheurer, Bernard Silvan Schroffenegger, Raphael Schwitter, Phillip Ströbel, Benjamin Suter
| Challenge: | a corpus of 16th century letters from and to the Zurich reformer Heinrich Bullinger has been preserved . a recent study investigated code-switching in these 8600 letters . |
| Approach: | They investigate the automatic detection of code-switching in a 16th century letter exchange . they use a popular language identifier to bootstrap a word-based language classifier . |
| Outcome: | The proposed language classifier bootstraps with a popular identifier on a small training corpus of 150 sentences per language. |
Quality and Efficiency of Manual Annotation: Pre-annotation Bias (2022.lrec-1)
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| Challenge: | Annotators using pre-annotation are less efficient at producing high quality annotations. |
| Approach: | They propose to use an automatic pre-annotation for a task to judge annotation quality . they also evaluate the effect of automatic linguistically-based checks on the same data . |
| Outcome: | The proposed method improves the quality of annotated sentences without reducing quality. |
A Comprehensive Evaluation and Correction of the TimeBank Corpus (2022.lrec-1)
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| Challenge: | TimeML is an annotation scheme for capturing temporal information in text. |
| Approach: | They propose to use TimeML to validate TimeML and provide a rich dataset of events, temporal expressions, and temporal relationships for training and testing temporal analysis systems. |
| Outcome: | The proposed methods detect and correct errors in the TimeML corpus and provide a reference corpus for training and testing temporal analysis systems. |
Evaluating Multilingual Sentence Representation Models in a Real Case Scenario (2022.lrec-1)
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| Challenge: | a recent study has shown that the infamous Protocols are actually plagiarized . a convoluted task with no standard benchmarks for paraphrase detection and sentence similarity is a problem . |
| Approach: | They evaluate sentence representation models on the paraphrase detection task . they use a forged text from the so-called "Protocols of the Elders of Zion" scholars have demonstrated that the first text plagiarizes from the second . |
| Outcome: | The proposed model is based on the forged “Protocols of the Elders of Zion” . the model is similar to the standard model but has some problems . |
Validity, Agreement, Consensuality and Annotated Data Quality (2022.lrec-1)
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| Challenge: | a wide consensus is rife regarding the need for reference annotated datasets . however, the creation of such datasets is accompanied by theorectical and practical issues . |
| Approach: | They propose to use agreement among annotators as an indicator of consensus . they argue that it is difficult to produce gold-standard annotated datasets . |
| Outcome: | The proposed model focuses on the complex relations between agreement and reference and the emergence of consensus. |
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding (2022.lrec-1)
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Salima Mdhaffar, Valentin Pelloin, Antoine Caubrière, Gaëlle Laperriere, Sahar Ghannay, Bassam Jabaian, Nathalie Camelin, Yannick Estève
| Challenge: | Pretrained models have been introduced for both acoustic and language modeling. |
| Approach: | They present an error analysis of pretrained models using a french MEDIA benchmark dataset. |
| Outcome: | The proposed models have been able to improve on the french MEDIA benchmark dataset, which is one of the most challenging among all benchmarks accessible to the entire research community. |
JGLUE: Japanese General Language Understanding Evaluation (2022.lrec-1)
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| Challenge: | There is no benchmark for Japanese to evaluate and analyze NLU ability from different perspectives. |
| Approach: | They build a Japanese NLU benchmark from scratch without translation to measure general NLU ability in Japanese. |
| Outcome: | a Japanese NLU benchmark is built from scratch without translation to measure general NLU ability in Japanese. |
Using the LARA Little Prince to compare human and TTS audio quality (2022.lrec-1)
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Elham Akhlaghi, Ingibjörg Iða Auðunardóttir, Anna Bączkowska, Branislav Bédi, Hakeem Beedar, Harald Berthelsen, Cathy Chua, Catia Cucchiarin, Hanieh Habibi, Ivana Horváthová, Junta Ikeda, Christèle Maizonniaux, Neasa Ní Chiaráin, Chadi Raheb, Manny Rayner, John Sloan, Nikos Tsourakis, Chunlin Yao
| Challenge: | A popular idea in Computer Assisted Language Learning (CALL) is to use multimodal annotated texts to support reading. |
| Approach: | They propose to use an open source platform to create good quality audio for L2 learning . they use four passages from LARA versions of Saint-Exupèry’s “Le petit prince” to instantiate the 2x2 cross product of dialogue, not-dialogue and humour, not humor. |
| Outcome: | The proposed method is based on a web form and ten languages. |
Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations (2022.lrec-1)
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| Challenge: | toxicity classifiers rely on lexical cues, so creative language use can be detrimental to utility of current corpora and state-of-the-art models. |
| Approach: | They propose to use model-agnostic adversarial behavior to enhance toxic content classification models. |
| Outcome: | The proposed model-agnostic adversarial behavior and augmentation for cyberbullying detection are robust against word-level perturbations at a slight trade-off in overall task performance. |
Constructing Distributions of Variation in Referring Expression Type from Corpora for Model Evaluation (2022.lrec-1)
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| Challenge: | referencing is a non-deterministic task, but the algorithms for RE generation are evaluated against corpora of written texts which only include one RE per reference. |
| Approach: | They propose a method for exploring variation in human RE choice on the basis of longitudinal corpora. |
| Outcome: | The proposed method shows agreement between the evaluations against human judgements and parallel evaluations. |
Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis (2022.lrec-1)
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| Challenge: | Knowledge Graph (KG) Question Answering (QA) is a rapidly growing field in research and industry. |
| Approach: | They propose to create a new leaderboard for any KGQA benchmark dataset as a focal point for the community. |
| Outcome: | The proposed model provides a central and open leaderboard for any KGQA benchmark dataset as a focal point for the community. |
Multi-Task Learning for Cross-Lingual Abstractive Summarization (2022.lrec-1)
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| Challenge: | Existing studies use pseudo cross-lingual abstractive summarization data to train neural encoder-decoders. |
| Approach: | They propose a multi-task learning framework for cross-lingual abstractive summarization that attaches a special token to the beginning of the input sentence to indicate the target task. |
| Outcome: | The proposed model achieves better performance than the model trained with only pseudo cross-lingual abstractive summarization data. |
How Much Context Span is Enough? Examining Context-Related Issues for Document-level MT (2022.lrec-1)
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| Challenge: | Recent advances in neural machine translation (NMT) have made it possible to include discourse into translation systems. |
| Approach: | They use the DELA corpus to examine the context span needed to translate from English into Portuguese. |
| Outcome: | The shortest span to disambiguate issues can appear in different positions in the document including preceding, following, global, world knowledge. |
TANDO: A Corpus for Document-level Machine Translation (2022.lrec-1)
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Harritxu Gete, Thierry Etchegoyhen, David Ponce, Gorka Labaka, Nora Aranberri, Ander Corral, Xabier Saralegi, Igor Ellakuria, Maite Martin
| Challenge: | Document-level Neural Machine Translation aims to increase the quality of neural translation models by taking into account contextual information. |
| Approach: | They propose to use document-level corpus for Basque-Spanish language pairs to take into account contextual information and perform fine-grained evaluations of gender and gender. |
| Outcome: | The proposed corpus is suitable for fine-grained evaluation of document-level machine translation systems. |
Unsupervised Machine Translation in Real-World Scenarios (2022.lrec-1)
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Ona de Gibert Bonet, Iakes Goenaga, Jordi Armengol-Estapé, Olatz Perez-de-Viñaspre, Carla Parra Escartín, Marina Sanchez, Mārcis Pinnis, Gorka Labaka, Maite Melero
| Challenge: | a recent study has shown that unsupervised methods rely on monolingual corpora to build MT systems. |
| Approach: | They present the results of the MT4All CEF project using monolingual corpora . they propose to generate bilingual dictionaries and translation models from monolingual data . |
| Outcome: | The proposed method generates bilingual dictionaries and translation models from monolingual corpora . results show that it is comparable to general domain supervised translation . |
COVID-19 Mythbusters in World Languages (2022.lrec-1)
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| Challenge: | 115 languages are included in the database, including the original English texts . character bi-grams with normalization is an effective proxy for measuring the similarity of the languages and the affinity ranking of language pairs can be obtained. |
| Approach: | They propose a multi-lingual database containing translated COVID-19 mythbusters texts . they use character bi-grams with normalization to measure similarity of languages . |
| Outcome: | The proposed database has translations into 115 languages and the original English texts, of which the original texts are published by the World Health Organization (WHO). |
On the Multilingual Capabilities of Very Large-Scale English Language Models (2022.lrec-1)
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| Challenge: | Generative Pre-trained Transformers (GPTs) have been scaled to unprecedented sizes in the history of machine learning. |
| Approach: | They investigate the potential and limits of Generative Pre-trained Transformers in three tasks . they find it can be almost as useful for many languages as it is for English . |
| Outcome: | The proposed model can perform tasks in five different languages, and its potential is explored . it can learn from a few examples "via text interaction" and is scalable to many languages . |
Evaluating Subtitle Segmentation for End-to-end Generation Systems (2022.lrec-1)
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| Challenge: | Subtitle segmentation can be evaluated with sequence segmentation metrics against a human reference, but cannot be applied when systems generate outputs different than the reference, e.g. with end-to-end subtitling systems. |
| Approach: | They propose to use Sigma to evaluate subtitle segmentation against a human reference and a boundary projection method to disentangle the effect of good segmentation from text quality. |
| Outcome: | The proposed method disentangles the effect of good segmentation from text quality and is compared with existing metrics. |
Using Semantic Role Labeling to Improve Neural Machine Translation (2022.lrec-1)
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| Challenge: | despite progress in machine translation, some form of language understanding may be desirable . current systems rely on pattern recognition, but some form may be useful . |
| Approach: | They use semantic role labeling to annotate a standard parallel corpus with semantic roles . they then train a neural machine translation system using the annotated corpus and original unannotated text . |
| Outcome: | The proposed system improves BLEU scores for English, French, German, Greek and Spanish. |
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference (2022.lrec-1)
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| Challenge: | Natural Language Inference (NLI) is a crucial task in AI and natural language processing. |
| Approach: | They propose an effective transfer learning approach for cross-lingual NLI . they perform experiments on English-Hindi language pairs in cross-linguistic setting . |
| Outcome: | The proposed model improves the baseline model by 10% over the state-of-the-art model. |
Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts (2022.lrec-1)
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| Challenge: | Specialist high-quality information is typically first available in English, and it is written in a language that may be difficult to understand by most readers. |
| Approach: | They propose to use a new language resource to simplify COVID-19 texts . they propose to employ four annotators who simplified over 6,000 sentences . |
| Outcome: | The proposed dataset improves readability from the original texts to their simplified versions. |
Building Comparable Corpora for Assessing Multi-Word Term Alignment (2022.lrec-1)
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| Challenge: | Existing methods to extract bilingual terminologies from corpora are limited . MWTs pose serious challenges for alignment and machine translation systems . |
| Approach: | They propose an approach to build comparable corpora and bilingual term dictionaries that evaluate bilingual term alignment in comparable corpus. |
| Outcome: | The proposed method is validated on an existing dataset and manually annotated data. |
Mean Machine Translations: On Gender Bias in Icelandic Machine Translations (2022.lrec-1)
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| Challenge: | a study conducted on Icelandic translations in the translation systems Google Translate and Véling.is . results show a pattern which corresponds to certain societal ideas about gender. |
| Approach: | They examine how gender bias appears in English-Icelandic translations . they conducted a study on Icelandic translation in the translation systems Google Translate and Véling.is . |
| Outcome: | The main purpose of the study is to examine how gender bias appears in English-Icelandic translations. |
An Analysis of Dialogue Act Sequence Similarity Across Multiple Domains (2022.lrec-1)
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| Challenge: | a recent study shows that many machine learning models perform poorly when exposed to domain shifts due to contextual differences. |
| Approach: | They analyze dialogue act sequences from related domains to predict performance degradation . they find that when dialogue acts sequences are dissimilar they lie further away in embedding space . |
| Outcome: | The proposed model can be trained even when the datasets are corrupted with noise. |
Constructing a Culinary Interview Dialogue Corpus with Video Conferencing Tool (2022.lrec-1)
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| Challenge: | Existing interview dialogue corpora are based on news interviews which serve the purpose of information broadcasting or entertainment. |
| Approach: | They propose an interview dialogue corpus in the culinary domain in which interviewers play an active role to elicit culinary knowledge from the cooking expert. |
| Outcome: | The proposed corpus consists of 308 interview dialogues, each about 13 minutes long, which add up to a total of 69,000 utterances. |
UgChDial: A Uyghur Chat-based Dialogue Corpus for Response Space Classification (2022.lrec-1)
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| Challenge: | Using a chatroom environment, we present a Uyghur dialogue corpus based on a conversation room environment. |
| Approach: | They propose to use UgChDial to collect two-party dialogues and multi-party conversations in a chatroom environment. |
| Outcome: | The proposed corpus is based on a chatroom environment and is available online. |
A Speculative and Tentative Common Ground Handling for Efficient Composition of Uncertain Dialogue (2022.lrec-1)
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| Challenge: | a study explores how the grounding process is composed and adapts to human cognitive processes . common ground is a set of information shared among participants that serves as a precondition for understanding individual utterances . |
| Approach: | a study investigates how the grounding process is composed by participants . it suggests that common ground may not necessarily be formed bottom-up through analytic expressions . |
| Outcome: | a new approach to human-like dialogue may be more suitable for natural human communication, the authors say . they show that common ground is mutually accepted among participants through holistic expressions . |
BaSCo: An Annotated Basque-Spanish Code-Switching Corpus for Natural Language Understanding (2022.lrec-1)
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| Challenge: | Basque-Spanish code-switching is a widespread phenomenon among bilingual speakers in the Basque Country. |
| Approach: | They propose to use annotated utterances to train bilingual chatbots in Basque and Spanish to cover the phenomenon of code-switching. |
| Outcome: | The proposed corpus is the first with annotated linguistic resources encompassing Basque-Spanish code-switching. |
ProDial – An Annotated Proactive Dialogue Act Corpus for Conversational Assistants using Crowdsourcing (2022.lrec-1)
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| Challenge: | Especially in the household domain, robots may become indispensable helpers by overtaking tedious tasks, e.g. keeping the place tidy. |
| Approach: | They propose a conversational approach for explicitly collecting personal user information using natural dialogue. |
| Outcome: | The proposed approach is compared to a baseline dialogue strategy for interactive personalization and has shown that it is friendlier. |
ELITR Minuting Corpus: A Novel Dataset for Automatic Minuting from Multi-Party Meetings in English and Czech (2022.lrec-1)
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| Challenge: | Automated minuting is a rather unstructured writing activity and can be difficult due to a variety of factors including the quality of automatic speech recorders, availability of public meeting data, subjective knowledge of the minuter, etc. |
| Approach: | They propose a dataset on automatic minuting which includes transcripts from ASRs and minuted by annotators. |
| Outcome: | The proposed dataset covers more than 160 hours of meeting content. |
Extracting Age-Related Stereotypes from Social Media Texts (2022.lrec-1)
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| Challenge: | a method for extracting age-related stereotypes from Twitter data is under-studied in NLP . stereotyping on the basis of protected characteristics has been understudied . |
| Approach: | They propose a method for extracting age-related stereotypes from Twitter data . they generate a corpus of 300,000 over-generalizations about four contemporary generations . |
| Outcome: | The method uncovers common stereotypes as reported in media and psychological literature . it also finds that stereotypes for different generations vary across topics . |
Borrowing or Codeswitching? Annotating for Finer-Grained Distinctions in Language Mixing (2022.lrec-1)
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| Challenge: | a corpus of tweets annotated for codeswitching and borrowing between Spanish and English is presented . the annotation does not treat common “internet-speak” as codeswitched when used in an otherwise monolingual context. |
| Approach: | They present a new corpus of tweets annotated for codeswitching and borrowing between Spanish and English. |
| Outcome: | The proposed corpus contains 9,500 tweets annotated with codeswitches, borrowings, and named entities. |
Multi-Aspect Transfer Learning for Detecting Low Resource Mental Disorders on Social Media (2022.lrec-1)
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| Challenge: | Mental disorders are an important and pervasive public health issue. |
| Approach: | They propose to use linguistic features to improve mental disorder detection . they propose to apply multi-aspect transfer learning to detecting disorders from social media . |
| Outcome: | The proposed methods can be used to improve mental disorder detection in the context of data scarcity and understanding the overlapping symptoms between disorders. |
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination (2022.lrec-1)
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| Challenge: | Social media are integrated with our daily life and are used to circulate information. |
| Approach: | They develop and publicly release the first largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign. |
| Outcome: | The proposed dataset is the largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign, covering many countries in the Arab region. |
FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias (2022.lrec-1)
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| Challenge: | Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. |
| Approach: | They propose a new reddit dataset for fake news spreader analysis, called FACTOID, which tracks political discussions on Reddit since the beginning of 2020. |
| Outcome: | The proposed dataset contains over 4K users with 3.4M posts and includes their credibility level (very low to very high) and political bias strength (extreme right to extreme left). |
Multitask Learning for Grapheme-to-Phoneme Conversion of Anglicisms in German Speech Recognition (2022.lrec-1)
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| Challenge: | Anglicisms are a challenge in German speech recognition due to their irregular pronunciation compared to native German words. |
| Approach: | They propose a multitask sequence-to-sequence approach for grapheme-tophoneme conversion to improve the phonetization of Anglicisms. |
| Outcome: | The proposed model reduces the word error rate by 1 % and the Anglicism error rate, while still maintaining the accuracy of the baseline model. |
SDS-200: A Swiss German Speech to Standard German Text Corpus (2022.lrec-1)
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Michel Plüss, Manuela Hürlimann, Marc Cuny, Alla Stöckli, Nikolaos Kapotis, Julia Hartmann, Malgorzata Anna Ulasik, Christian Scheller, Yanick Schraner, Amit Jain, Jan Deriu, Mark Cieliebak, Manfred Vogel
| Challenge: | Using a web recording tool, participants were asked to translate their Swiss German text to their own dialect before recording it. |
| Approach: | They present a corpus of Swiss German dialectal speech with Standard German text translations . the dataset allows for training speech translation, dialect recognition, and speech synthesis systems . |
| Outcome: | The dataset allows for training speech translation, dialect recognition, and speech synthesis systems. |
Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages (2022.lrec-1)
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| Challenge: | voicing alternation phenomena of stops are a common problem in connected speech . phoneticians and phonologists are interested in analyzing phonetic variation . |
| Approach: | They use forced alignment with pronunciation variants and machine learning techniques to examine voicing alternations of stops in Romance languages. |
| Outcome: | The proposed method enables linguists to use large corpora and speech recognition systems . the results show that voicing alternations occur in all Romance languages . |
Overlaps and Gender Analysis in the Context of Broadcast Media (2022.lrec-1)
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| Challenge: | Using gender and overlap annotations, we characterise interactions between speakers according to their gender and role in broadcast media. |
| Approach: | They propose to characterise interactions between speakers according to their gender and role in broadcast media by using a small dataset of 93 recordings from LCP French channel. |
| Outcome: | The proposed method could improve the efficiency of qualitative studies conducted in human sciences. |
A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification. (2022.lrec-1)
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Rémi Uro, David Doukhan, Albert Rilliard, Laetitia Larcher, Anissa-Claire Adgharouamane, Marie Tahon, Antoine Laurent
| Challenge: | Existing methods for creating diachronic corpus of voices are based on speaker characteristics and require human intervention. |
| Approach: | They propose to use a semi-automatic pipeline to create a diachronic corpus of voices balanced for speaker’s age, gender and recording period, according to 32 categories. |
| Outcome: | The proposed method cut down on manual annotations by ten and provides high quality speech for most of the selected excerpts. |
DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations (2022.lrec-1)
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| Challenge: | DiscoGeM is a crowdsourced corpus of 6,505 implicit discourse relations . the results show that a significant proportion of discourse relations are ambiguous . text genre is crucially affected by the distribution of discourse relation labels . |
| Approach: | They propose to use crowdsourced corpus of 6,505 implicit discourse relations to classify relations . they propose to include genre as a factor in automatic relation classification . |
| Outcome: | The proposed dataset shows that a significant proportion of discourse relations are ambiguous and can express multiple relation senses. |
QT30: A Corpus of Argument and Conflict in Broadcast Debate (2022.lrec-1)
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| Challenge: | Broadcast political debate is the public's easiest access to opinions that shape policies and enables the general public to make informed choices. |
| Approach: | They present the largest corpus of analysed dialogical argumentation ever created using 30 episodes of BBC's 'Question Time' from 2020 and 2021. |
| Outcome: | The resource is freely available at http://corpora.aifdb.org/qt30. |
Scaling up Discourse Quality Annotation for Political Science (2022.lrec-1)
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| Challenge: | Existing annotations on deliberative quality are time-consuming and suffer from class imbalance . ephd thesis: deliberation is not only the output of the decision making, but also the discussion that leads up to it. |
| Approach: | They propose to use data augmentation techniques to improve deliberative quality predictions in a standard dataset. |
| Outcome: | The proposed methods outperform classifiers based on linguistic features and argument quality annotations with or without data augmentation. |
Clarifying Implicit and Underspecified Phrases in Instructional Text (2022.lrec-1)
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| Challenge: | Natural language consists of implicit and underspecified phrases, which can cause misunderstandings. |
| Approach: | They propose to use wikiHow to extract human clarifications that resolve an implicit or underspecified phrase. |
| Outcome: | The proposed model can be used to generate alternate clarifications, which may or may not be compatible with the human clarification. |
Multilingual Pragmaticon: Database of Discourse Formulae (2022.lrec-1)
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| Challenge: | a database of DF from various languages is used for typological analysis of response constructions. |
| Approach: | They propose to use a multilingual database to analyze response constructions called discourse formulae (DF) . they include Russian, Serbian and Slovene DF as a starting point . |
| Outcome: | The proposed database is based on a database from Russian, Serbian and Slovene DF. |
Distant Reading in Digital Humanities: Case Study on the Serbian Part of the ELTeC Collection (2022.lrec-1)
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Ranka Stanković, Cvetana Krstev, Branislava Šandrih Todorović, Dusko Vitas, Mihailo Skoric, Milica Ikonić Nešić
| Challenge: | Distant reading is a new scale of description that does not displace previous scales of literary description. |
| Approach: | They present the Serbian part of the ELTeC multilingual corpus . they propose to test various methods and tools for distant reading . |
| Outcome: | The Serbian part of the ELTeC multilingual corpus is being built to test various methods and tools . Several use examples show that this sub-collection is usefull for both close and distant reading approaches. |
Exploring Text Recombination for Automatic Narrative Level Detection (2022.lrec-1)
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| Challenge: | Existing annotation workflows do not scale well to the annotation of complex narrative phenomena. |
| Approach: | They propose a workflow for narrative level detection that includes operationalization and a model . they propose generating training data synthetically to improve the prediction results . |
| Outcome: | The proposed workflow improves predictions by using training data synthetically. |
Automatic Normalisation of Early Modern French (2022.lrec-1)
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| Challenge: | Spelling normalisation is a useful step in the study and analysis of historical language texts, whether it is manual analysis by experts or automated analysis using downstream natural language processing (NLP) tools. |
| Approach: | They propose a new benchmark for the normalisation of Early Modern French into contemporary French using ABA, alignment-based approach and MT-approaches. |
| Outcome: | The proposed method homogenises the variable spelling in historical documents and reduces the gap between the historical state of the language and the contemporary state. |
From FreEM to D’AlemBERT: a Large Corpus and a Language Model for Early Modern French (2022.lrec-1)
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Simon Gabay, Pedro Ortiz Suarez, Alexandre Bartz, Alix Chagué, Rachel Bawden, Philippe Gambette, Benoît Sagot
| Challenge: | Anguage models for historical states of language are becoming more complex to process and more scarce in the corpora available. |
| Approach: | They propose to use a contextualised language model to analyse historical states of language in French. |
| Outcome: | The proposed model is based on a corpus of historical texts and is evaluated with an NLP task. |
Detecting Multiple Transitions in Literary Texts (2022.lrec-1)
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| Challenge: | Existing systems that can detect multiple transitions in texts are ineffective due to the large amount of texts available. |
| Approach: | They propose a system that can detect multiple transitions in literary texts . they extend existing system so it can detect transitions, and introduce multiple transition topics . |
| Outcome: | The proposed system outperforms the existing system on texts with known transitions and on single boundary texts. |
BasqueParl: A Bilingual Corpus of Basque Parliamentary Transcriptions (2022.lrec-1)
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Nayla Escribano, Jon Ander Gonzalez, Julen Orbegozo-Terradillos, Ainara Larrondo-Ureta, Simón Peña-Fernández, Olatz Perez-de-Viñaspre, Rodrigo Agerri
| Challenge: | a new corpus of Basque parliamentary transcripts is released to study political discourse in contrasting languages . a corpus containing political discourses from public institutions can be used for computational social science research . |
| Approach: | They present a corpus from Basque parliamentary transcripts and enrich it with metadata related to relevant attributes of speakers and speeches. |
| Outcome: | The proposed corpus is characterized by heavy Basque-Spanish code-switching . it provides interesting insights about language use of political representatives across time, parties and gender . |
GerEO: A Large-Scale Resource on the Syntactic Distribution of German Experiencer-Object Verbs (2022.lrec-1)
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| Challenge: | Psych verbs and their properties in multiple languages have ignited discussions among linguists for several decades . Psych-verbs are often considered syntactically deviant, although this has occasionally been called into question . |
| Approach: | They propose to use a large-scale database of more than 10,000 examples for 64 verbs from a newspaper corpus annotated for several syntactic and semantic features relevant for their analysis. |
| Outcome: | The proposed database contains 10,000 examples for 64 verbs from a newspaper corpus and includes syntactic construction, semantic stimulus type, and form of possible stimulus preposition. |
ACT2: A multi-disciplinary semi-structured dataset for importance and purpose classification of citations (2022.lrec-1)
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| Challenge: | Existing methods for classifying citations rely on bibliometric measures to consider the semantics of citation. |
| Approach: | They propose to use a Citation Context Classification (3C) shared task dataset to classify citations according to their purpose and importance. |
| Outcome: | The proposed model can be used to link research works to graphs and enable efficient knowledge discovery. |
Quantification Annotation in ISO 24617-12, Second Draft (2022.lrec-1)
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Harry Bunt, Maxime Amblard, Johan Bos, Karën Fort, Bruno Guillaume, Philippe de Groote, Chuyuan Li, Pierre Ludmann, Michel Musiol, Siyana Pavlova, Guy Perrier, Sylvain Pogodalla
| Challenge: | a project aimed at establishing an interoperable annotation schema for quantification phenomena was relaunched in early 2022 due to the Covid-19 pandemic . |
| Approach: | This paper describes the continuation of a project that aims at establishing an interoperable annotation schema for quantification phenomena as part of the ISO suite of semantic annotation standards. |
| Outcome: | The proposed schema is part of the ISO suite of semantic annotation standards known as the Semantic Annotation Framework (SemAF). |
The LTRC Hindi-Telugu Parallel Corpus (2022.lrec-1)
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| Challenge: | a qualitative corpus of 700K parallel sentences was created using multiple methods such as extract, align and review of Hindi-Telugu corpora. |
| Approach: | They propose to create a Hindi-Telugu parallel corpus of different technical domains using different methods including extract, align and review. |
| Outcome: | The proposed corpus is the largest, publicly available domain parallel corpus for Hindi-Telugu. |
MHE: Code-Mixed Corpora for Similar Language Identification (2022.lrec-1)
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| Challenge: | a new corpus of code-mixed data-sets is presented for similar language identification . the data-settings are based on a more-resourced minority language, Magahi . |
| Approach: | They propose a Magahi-Hindi-English code-mixed corpus for similar language identification . they discuss the complexity of the data-set and provide a few baselines . |
| Outcome: | The proposed corpus provides a language id at two levels: word and sentence. |
Bazinga! A Dataset for Multi-Party Dialogues Structuring (2022.lrec-1)
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Paul Lerner, Juliette Bergoënd, Camille Guinaudeau, Hervé Bredin, Benjamin Maurice, Sharleyne Lefevre, Martin Bouteiller, Aman Berhe, Léo Galmant, Ruiqing Yin, Claude Barras
| Challenge: | a dataset of 16 TV and movie series is filled with challenging multi-party dialogues. |
| Approach: | They propose a dataset built around 16 TV and movie series with challenging multi-party dialogues. |
| Outcome: | The proposed dataset is a step towards better multi-party dialogue structuring and understanding. |
The Ellogon Web Annotation Tool: Annotating Moral Values and Arguments (2022.lrec-1)
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| Challenge: | Using the Ellogon infrastructure, the ElLogon Web annotation tool is a collaborative, web-based tool with a user-friendly interface. |
| Approach: | They present a new version of the Ellogon Web Annotation Tool . the tool offers document analytics, annotation inspection and comparison features and a modern UI . |
| Outcome: | The Ellogon Web Annotation Tool offers document analytics, annotation inspection and comparison features, a modern UI, and formatted text import. |
WeCanTalk: A New Multi-language, Multi-modal Resource for Speaker Recognition (2022.lrec-1)
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| Challenge: | The WeCanTalk corpus is a multi-modal, multi-language resource for speaker recognition. |
| Approach: | The WeCanTalk corpus is a multi-modal resource for speaker recognition. |
| Outcome: | The corpus contains data from 202 native speakers in Hong Kong who were fluent in at least one other language. |
Using Wiktionary to Create Specialized Lexical Resources and Datasets (2022.lrec-1)
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| Challenge: | Using Wiktionary data to build specialized lexical datasets can be used for evaluating or improving NLP tasks, like Word Sense Disambiguation (WSD), Word-in-Context challenges (WiC), or Machine Translation (MT). |
| Approach: | They propose to use Wiktionary data to create specialized lexical datasets that can be used for evaluating or improving NLP tasks. |
| Outcome: | The proposed datasets can be used to improve and/or evaluate NLP tasks, like Word Sense Disambiguation (WSD), Word-in-Context challenges (WiC), or Sense Linking (SL), or machine translation (MT). |
STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents (2022.lrec-1)
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| Challenge: | Formal documents are organized into sections of text, each with a title . but there is no corpus of web documents annotated with titles and prose texts . cnn.com's john mccarthy and daniel mclears are working on a new title-text dataset . |
| Approach: | They propose a first title-text dataset on web documents that incorporates a wide variety of domains to facilitate downstream training. |
| Outcome: | The proposed system outperforms baseline models in terms of title-text identification. |
ELTE Poetry Corpus: A Machine Annotated Database of Canonical Hungarian Poetry (2022.lrec-1)
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Péter Horváth, Péter Kundráth, Balázs Indig, Zsófia Fellegi, Eszter Szlávich, Tímea Borbála Bajzát, Zsófia Sárközi-Lindner, Bence Vida, Aslihan Karabulut, Mária Timári, Gábor Palkó
| Challenge: | ELTE Poetry Corpus is a database that stores canonical Hungarian poetry with automatic annotations of the poems’ structural units, grammatical features and sound devices. |
| Approach: | They propose to use a TEI XML format to make it easier and faster to execute queries on the corpus. |
| Outcome: | The proposed method combines the results of a manual evaluation of the quality of the automatic annotation of rhythm with the results from an automatic evaluation of rule sets used for the automatic annotation of rhyme patterns. |
HAWP: a Dataset for Hindi Arithmetic Word Problem Solving (2022.lrec-1)
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| Challenge: | Word problem solving is a challenging and interesting task in NLP. |
| Approach: | They propose to use equations to solve Hindi arithmetic word problems . they propose to also use equation equivalence to evaluate word problem solvers . |
| Outcome: | The proposed dataset is based on 2336 arithmetic word problems in Hindi . it also includes baseline systems and evaluation techniques . |
The Bulgarian Event Corpus: Overview and Initial NER Experiments (2022.lrec-1)
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| Challenge: | Initial experiments on standard NER task due to complexity of dataset and rich NE annotation scheme are promising with respect to some labels and give insights on handling better other ones. |
| Approach: | They describe a Bulgarian Event Corpus (BEC) that includes named entities and events with their roles. |
| Outcome: | The proposed corpus is multi-domain and oriented towards Social Sciences and Humanities (SSH) it includes named entities and events with their roles. |
A Corpus for Commonsense Inference in Story Cloze Test (2022.lrec-1)
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| Challenge: | Story Cloze Test (SOTA) models can achieve over 90% accuracy on predicting the last sentence, but high accuracy can be achieved by merely using surface-level features. |
| Approach: | They constructed a human-labeled and human-verified commonsense knowledge inference dataset using data from 1871 stories and three human workers labeled each story. |
| Outcome: | The proposed models can achieve 90% accuracy on predicting the last sentence, but they don't perform well on new and more challenging tasks. |
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish (2022.lrec-1)
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Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren
| Challenge: | a prerequisite for building large-scale generative models for other languages is access to large amounts of high-quality text data and powerful computational resources. |
| Approach: | They present a 3.5 billion parameter autoregressive language model, trained on a 100 GB Swedish corpus. |
| Outcome: | The proposed model performs well on a 100 GB Swedish corpus and is competent in comparison with existing models of similar size. |
Constrained Language Models for Interactive Poem Generation (2022.lrec-1)
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Andrei Popescu-Belis, Àlex Atrio, Valentin Minder, Aris Xanthos, Gabriel Luthier, Simon Mattei, Antonio Rodriguez
| Challenge: | Neural language models cannot learn constraints from data, which is scarce for a well-resourced language such as French. |
| Approach: | They propose a system that combines neural language models with constraints that can be set by users on form, topic, emotion, and rhyming scheme. |
| Outcome: | The proposed system generates poems and stanzas using LMs and rule-based algorithms . it has been demonstrated at public events and log analysis shows that users found it engaging . |
ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls (2022.lrec-1)
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| Challenge: | a new dataset aims to automate the method to counter trolls . trolleds cause psychological damage to individuals and increase social costs . |
| Approach: | They propose to use a dataset to generate counter responses by varying counter responses according to a given strategy. |
| Outcome: | The proposed method improves strategy-controlled sentence generation. |
Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task (2022.lrec-1)
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| Challenge: | Numerical tables are widely used to communicate or report the classification performance of machine learning models with respect to a set of evaluation metrics. |
| Approach: | They propose a task where neural models are trained to generate textual explanations based on the metrics’ scores reported in numerical tables. |
| Outcome: | The proposed model outperforms existing methods and can be used to explain the performance of ML models. |
Barch: an English Dataset of Bar Chart Summaries (2022.lrec-1)
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| Challenge: | a new dataset of human-written summaries of bar charts is available in english . a chart summary is a textual description of a data point, which is often analytical . |
| Approach: | They propose a dataset of human-written summaries describing bar charts in english . a total of 47 charts are presented in the dataset, which includes 47 charts . |
| Outcome: | a new dataset of human-written summaries describing bar charts is presented in english . the dataset shows that human speakers often include such statements into chart summary . |
Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings (2022.lrec-1)
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| Challenge: | Neural approaches for natural language generation (NLG) have mushroomed due to large textual resources. |
| Approach: | They propose to use a pretrained multilingual encoder-decoder model and a combination of two pretrained language models to train a model in a low-resource setting. |
| Outcome: | The proposed model outperforms the previous model on English and on a small subset of the same data. |
Effectiveness of French Language Models on Abstractive Dialogue Summarization Task (2022.lrec-1)
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| Challenge: | Pre-trained language models have established the state-of-the-art on various natural language processing tasks, including dialogue summarization. |
| Approach: | They propose to use several language specific pre-trained models to summarize spontaneous oral dialogues in French using several language-specific pre-trainers: BARThez, BelGPT-2, mBARThes, and mT5. |
| Outcome: | The proposed models outperform the existing models on the DECODA (Call Center) dialogue corpus and show that they are far superior to the current models. |
ALEXSIS: A Dataset for Lexical Simplification in Spanish (2022.lrec-1)
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| Challenge: | Lexical Simplification is the process of replacing difficult words with easier synonyms while preserving the original information and meaning. |
| Approach: | They introduce ALEXSIS, a dataset for Lexical Simplification, and use it to benchmark Lexical simplification systems in Spanish. |
| Outcome: | The proposed dataset compares three approaches to Lexical Simplification in Spanish and a previous dataset for English. |
The IARPA BETTER Program Abstract Task Four New Semantically Annotated Corpora from IARPA’s BETTER Program (2022.lrec-1)
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| Challenge: | IARPA’s Better Extraction from Text Towards Enhanced Retrieval (BETTER) Program created multiple multilingual datasets to spawn and evaluate cross-language information extraction and information retrieval research and development in zero-shot conditions. |
| Approach: | The paper presents the event and argument annotation in the Abstract Evaluation phase of BETTER . it also presents the data collection, preparation, partitioning and mark-up of the datasets . |
| Outcome: | The “Abstract” dataset will be released to the public at LREC 2022 in four languages to champion further information extraction research in this area. |
A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in information extraction. |
| Approach: | They construct a labelled NER corpus of Vietnamese academic biomedical text . they annotate documents with five categories of named entities: Organisation, Location, Date and Time, Symptom and Disease, and Diagnostic Procedure. |
| Outcome: | The proposed system could provide answers to questions related to TB in Vietnamese . the system could also be used to identify TB-related diseases in the country . |
RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour (2022.lrec-1)
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| Challenge: | Forced labour is the most common type of modern slavery, affecting at least 24.9 million people worldwide. |
| Approach: | They propose to annotate an English corpus for multi-class and multi-label forced labour detection using specialised data from specialised sources. |
| Outcome: | The proposed corpus consists of 989 news articles annotated according to risk indicators defined by the International Labour Organization (ILO). |
Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is integral to many NLP applications such as chatbots and question answering. |
| Approach: | They propose to annotate Arabic nested entities instead of flat annotations by manually annotating 550K tokens with 21 entity types including person, organization, location, event and date. |
| Outcome: | The proposed model achieved an overall micro F1-score of 0.884 and the annotation guidelines and source code are publicly available. |
Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient’s Perspective (2022.lrec-1)
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| Challenge: | a recent study shows that the class labels of german documents containing ADRs are imbalanced . clinical trials and physicians prescribing medications cannot cover every potential use case. |
| Approach: | They propose to use binary annotated documents from a german patient forum to detect ADRs. |
| Outcome: | The proposed model achieves an F1 score of 37.52 for the positive class on the German patient forum. |
GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers (2022.lrec-1)
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Florian Borchert, Christina Lohr, Luise Modersohn, Jonas Witt, Thomas Langer, Markus Follmann, Matthias Gietzelt, Bert Arnrich, Udo Hahn, Matthieu-P. Schapranow
| Challenge: | despite advances in language resources, there is still a shortage of annotated corpora covering (German) medical language. |
| Approach: | They propose to build on clinical guidelines with an annotation scheme based on SNOMED CT . they also train named entity recognition models on the new data set . |
| Outcome: | The new corpus can be built upon clinical guidelines with reasonable coverage of medical terminology. |
ClinIDMap: Towards a Clinical IDs Mapping for Data Interoperability (2022.lrec-1)
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| Challenge: | a tool for mapping identifiers between clinical ontologies and lexical resources is available. |
| Approach: | They propose a tool for mapping identifiers between clinical ontologies and lexical resources. |
| Outcome: | The proposed mapping tool can be used to enrich existing annotated corpora in multiple languages. |
Identifying Draft Bills Impacting Existing Legislation: a Case Study on Romanian (2022.lrec-1)
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| Challenge: | Using manual annotations provided by legal experts, we identify future draft bills that have the potential to impact existing policies on public procurement. |
| Approach: | They propose to use an annotated corpus of historical legal documents to identify future draft bills that could impact existing policies on public procurement. |
| Outcome: | The proposed draft bills are screened by legal experts and identified as impacting past public procurement legislation. |
MuLD: The Multitask Long Document Benchmark (2022.lrec-1)
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| Challenge: | Existing benchmarks for NLP focus on tasks for one or two sentences, but efficient techniques are needed for processing much longer sequences. |
| Approach: | They propose to modify existing NLP tasks to create a long document benchmark which requires models to successfully model long-term dependencies in the text. |
| Outcome: | The proposed benchmark is much more challenging than its ‘short document’ equivalents. |
A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports (2022.lrec-1)
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| Challenge: | Oftentimes, these findings and devices are referred to multiple times in a single report and are also referred across different reports of a patient. |
| Approach: | They propose a new cross-document coreference resolution (CDCR) dataset for identifying co-referring radiological findings and medical devices across a patient's radiology reports. |
| Outcome: | The proposed dataset contains 5872 mentions (findings and devices) spanning 638 MIMIC-III radiology reports across 60 patients, covering multiple imaging modalities and anatomies. |
How’s Business Going Worldwide ? A Multilingual Annotated Corpus for Business Relation Extraction (2022.lrec-1)
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| Challenge: | The 21st century economy has shaped the economic landscape and changed the way market stakeholders interact with each other in the global market where national borders have melted and trades became more open and free. |
| Approach: | They propose a multilingual dataset for automatic extraction of binary business relations involving organizations from the web. |
| Outcome: | The proposed dataset is the first multilingual dataset for automatic extraction of binary business relations involving organizations from the web. |
Do Transformer Networks Improve the Discovery of Rules from Text? (2022.lrec-1)
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| Challenge: | Existing rules that model binary relations such as X treat Y are based on the distributional hypothesis of Harris (1954). |
| Approach: | They propose to implement the distributional hypothesis using contextualized embeddings provided by a transformer-network-based language model to measure the similarity between slots instead of lexical overlap. |
| Outcome: | The proposed approach outperforms the original DIRT algorithm in the question answering-based evaluation. |
Offensive language detection in Hebrew: can other languages help? (2022.lrec-1)
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| Challenge: | Various approaches for offensive language detection have been applied for this task . contamination of social networks with offensive content is a new reality affecting almost all of us . |
| Approach: | They propose to use multiple supervised models and text representations to detect offensive language in three languages, including two Semitic languages. |
| Outcome: | The proposed model can detect offensive content in two Semitic languages, including Hebrew and Arabic, and it is able to perform cross-lingual and multilingual learning. |
JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation (2022.lrec-1)
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| Challenge: | Existing tools for analyzing medical information extraction are limited . empirical results show satisfactory analyzing performance . |
| Approach: | They propose a relation annotation schema for investigating medical and temporal relations in Japanese medical reports. |
| Outcome: | The proposed schema shows that it performs better than existing models and is feasible for high-accuracy applications. |
Enhanced Entity Annotations for Multilingual Corpora (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is a new language for natural language processing. |
| Approach: | They propose to improve the annotation quality of the English Wikipedia tool WEXEA . they propose to use a proven NER system to annotate entities in Wikipedia . |
| Outcome: | The proposed tool can be used to exhaustively annotate entities in Wikipedia articles. |
Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification (2022.lrec-1)
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| Challenge: | Existing disease surveillance systems use indicators to monitor official sources and unofficial sources. |
| Approach: | They propose a way to perform epidemiological document classification by enriching thematic features . they use a pre-trained biomedical language model with a novel approach . |
| Outcome: | The proposed method improves the classifier's ability to avoid false positive alerts on disease surveillance systems. |
Spanish Datasets for Sensitive Entity Detection in the Legal Domain (2022.lrec-1)
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| Challenge: | The de-identification of sensible data is essential for data sharing and reuse, both for research and commercial purposes. |
| Approach: | They propose to use four datasets annotated for named entity detection in Spanish to fine-tune models for the task of named entity-detection. |
| Outcome: | The proposed model is based on four datasets annotated for named entity detection in Spanish with an estimated error rate of 14%. |
ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification (2022.lrec-1)
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| Challenge: | Recent advances in natural language processing (NLP) have reshaped the industry . complexity of such models makes them a “black box” and can cause ethical concerns . |
| Approach: | They propose a convolutional TM architecture that breaks down text into a sequence of fragments . they propose to use a tokenization scheme to bind the tokens to the text fragments. |
| Outcome: | The proposed architecture improves on a set of text fragments and eliminates the need for a corpus-specific vocabulary. |
Elvis vs. M. Jackson: Who has More Albums? Classification and Identification of Elements in Comparative Questions (2022.lrec-1)
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| Challenge: | Comparative Question Answering (cQA) is the task of providing accurate answers to questions . most question answering systems focus on answering factoid questions, but they fail at answering comparative questions in an efficient argumentative manner. |
| Approach: | They propose two new open-domain datasets for identifying and labeling comparative questions . they use a binary classification task and an unsupervised sequence labeling task . |
| Outcome: | The proposed datasets reach close-to-human results on a binary classification task with a neural model using ALBERT embeddings. |
Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction (2022.lrec-1)
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| Challenge: | Recent research shows that prompt-based learning improves performance on relation extraction tasks. |
| Approach: | They propose a prompt-based learning method that generates comprehensive prompts for biomedical relation extraction using a ChemProt dataset. |
| Outcome: | The proposed method improves fine-tuning on a biomedical relation extraction task with a cloze-test task and fewer training examples to make reasonable predictions. |
Comparing Annotated Datasets for Named Entity Recognition in English Literature (2022.lrec-1)
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| Challenge: | Generally speaking, the majority of NER tools struggle to perform well when the entities in the text contain specific characteristics. |
| Approach: | They analysed two existing annotated datasets and two additional gold standard datasets to evaluate the performance of two NER tools. |
| Outcome: | The results show that the performance of two NER tools varies significantly depending on the gold standard used for the individual evaluations. |
Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion (2022.lrec-1)
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| Challenge: | Existing models that model fine-grained opinion shifts of social media users are lacking . lack of publicly available datasets for this task presents a major challenge . |
| Approach: | They propose an annotated social media opinion dataset that provides a model for subtle opinion fluctuations and fine-grained stances. |
| Outcome: | The proposed dataset is comparable to the annotations of experts and non-experts. |
APPReddit: a Corpus of Reddit Posts Annotated for Appraisal (2022.lrec-1)
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Marco Antonio Stranisci, Simona Frenda, Eleonora Ceccaldi, Valerio Basile, Rossana Damiano, Viviana Patti
| Challenge: | Existing resources for emotion recognition are lacking for appraisal models. |
| Approach: | They propose to use APPReddit to annotate non-experimental data according to Appraisal theories . they compare it with enISEAR, a corpus of events created in an experimental setting and annotated according to this theory. |
| Outcome: | The proposed model predicts four appraisal dimensions without significant loss . the proposed model is compared with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. |
Evaluating Methods for Extraction of Aspect Terms in Opinion Texts in Portuguese - the Challenges of Implicit Aspects (2022.lrec-1)
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| Challenge: | In aspect-based sentiment analysis, the implicit mention of aspects is difficult to identify and may require world knowledge to do so. |
| Approach: | They evaluate frequency-based, hybrid, and machine learning methods to extract aspect terms from opinionated texts in Portuguese. |
| Outcome: | The proposed methods show that they are more efficient and more efficient than previous methods. |
SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis (2022.lrec-1)
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| Challenge: | Despite recent advances, AI still struggles with complex tasks that require commonsense reasoning such as natural language understanding. |
| Approach: | They propose a commonsense-based framework that aims to overcome these limitations in the context of sentiment analysis. |
| Outcome: | The proposed framework overcomes these limitations in the context of sentiment analysis. |
Building an Endangered Language Resource in the Classroom: Universal Dependencies for Kakataibo (2022.lrec-1)
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Roberto Zariquiey, Claudia Alvarado, Ximena Echevarría, Luisa Gomez, Rosa Gonzales, Mariana Illescas, Sabina Oporto, Frederic Blum, Arturo Oncevay, Javier Vera
| Challenge: | 12 This paper describes the collaborative methodology implemented to create a UD treebank for a Peruvian endangered language. |
| Approach: | They propose to create a UD treebank for a Peruvian endangered language . they use a collaborative methodology to create the treebank in a course . |
| Outcome: | The proposed treebank would enhance the future development of an NLP toolkit for this endangered language. |
The Norwegian Colossal Corpus: A Text Corpus for Training Large Norwegian Language Models (2022.lrec-1)
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| Challenge: | Norwegian is one of many languages lacking sufficient textual data to train quality language models. |
| Approach: | They propose to release 49GB of clean Norwegian textual data containing over 7B words . they hope to foster the creation of better Norwegian language models and multilingual language models . |
| Outcome: | The Norwegian Colossal Corpus (NCC) contains 49GB of clean Norwegian textual data containing over 7B words. |
Embeddings models for Buddhist Sanskrit (2022.lrec-1)
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| Challenge: | Despite extensive scholarly endeavors, much uncertainty still surrounds this body of literature, especially regarding matters of chronology, authorship, compositional history. |
| Approach: | They propose a corpus of Buddhist texts, a general corpus and word similarity and word analogy datasets for embeddings models. |
| Outcome: | The proposed models perform better on semantic similarity and word analogy tasks than on contextual models. |
Development of Automatic Speech Recognition for the Documentation of Cook Islands Māori (2022.lrec-1)
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Rolando Coto-Solano, Sally Akevai Nicholas, Samiha Datta, Victoria Quint, Piripi Wills, Emma Ngakuravaru Powell, Liam Koka’ua, Syed Tanveer, Isaac Feldman
| Challenge: | a new study describes the process of data processing and training of an automatic speech recognition system for Cook Islands Mori . the system is based on statistical and Deep Learning techniques, and is available under a license . |
| Approach: | They describe the process of data processing and training of an automatic speech recognition system for Cook Islands Mori . they transcribed four hours of speech from adults and elderly speakers of the language and prepared two experiments . |
| Outcome: | The proposed system can perform better with low-resource Indigenous languages . the system can be used to accelerate the documentation of Cook Islands Mori . |
A Generalized Approach to Protest Event Detection in German Local News (2022.lrec-1)
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| Challenge: | Social scientists conduct protest event analysis to learn about developments and trends of the forms, scale and hot topics of political protests. |
| Approach: | They propose to use a German language resource to analyze newspaper articles on protest events . they train and evaluate transformer-based text classifiers to automatically detect relevant newspaper articles . |
| Outcome: | The proposed method achieves a binary F1-score of 93.3 %, but does not generalize well to other datasets. |
Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements (2022.lrec-1)
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| Challenge: | a paper presents text mining approaches on German-speaking job advertisements . transfer learning and domain adaptation are used to build text mining applications . |
| Approach: | They propose text mining approaches on German-speaking job advertisements . they use transfer learning and domain adaptation to build language models adapted to job ads . |
| Outcome: | The proposed approaches outperform general-domain language models pre-trained on ten times more data. |
Pre-Training Language Models for Identifying Patronizing and Condescending Language: An Analysis (2022.lrec-1)
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| Challenge: | Patronizing and Condescending Language (PCL) is a subtle but harmful type of discourse. |
| Approach: | They propose to pre-train PCL detection models on other NLP tasks to improve their detection . they find that performance gains are possible when pre-training on sentiment, harmful language and commonsense morality. |
| Outcome: | The proposed models improve on pre-training on other NLP tasks focusing on sentiment, harmful language and commonsense morality, compared with tasks concentrating on political speech and social justice, the authors show . |
HeLI-OTS, Off-the-shelf Language Identifier for Text (2022.lrec-1)
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| Challenge: | Existing off-the-shelf language identification tools favor widely used languages, but Heli-OTS can be used to identify a large group of languages. |
| Approach: | They introduce an off-the-shelf text language identification tool using the HeLI method . they compare the He LI-OTS language identifier with fastText on two different data sets . |
| Outcome: | The proposed language identification tool is compared with fastText on two different data sets. |
Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages (2022.lrec-1)
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| Challenge: | Existing methods to extract named entity datasets from parallel corpora require large monolingual corporata or word aligners that are unavailable or perform poorly for underresourced languages. |
| Approach: | They propose a method for creating a multilingual named entity resource from parallel corpora and apply it to the Parallel Bible Corpus, a corpus of more than 1000 languages. |
| Outcome: | The proposed method outperforms existing methods in two tasks. |
Towards the Construction of a WordNet for Old English (2022.lrec-1)
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Fahad Khan, Francisco J. Minaya Gómez, Rafael Cruz González, Harry Diakoff, Javier E. Diaz Vera, John P. McCrae, Ciara O’Loughlin, William Michael Short, Sander Stolk
| Challenge: | In this paper we discuss our preliminary work towards the construction of a WordNet for Old English, taking our inspiration from other similar WN construction projects for ancient languages such as Ancient Greek, Latin and Sanskrit. |
| Approach: | They propose to use a legacy Old English dictionary to build a WordNet for Old English using a lexicographic resource and the naisc system to automatically compile a provisional version of the WordNet. |
| Outcome: | The proposed OldEWN will be based on lemmas and definitions extracted from a legacy Old English dictionary and will be automatically compile and enriched by experts using the naisc system. |
A Framenet and Frame Annotator for German Social Media (2022.lrec-1)
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| Challenge: | In corpus linguistics, semantic annotation is a valuable addition to ordinary, morphosyntactic tagging, lemmatization and dependency relations. |
| Approach: | They propose a parsing- and annotation-oriented framenet for German with almost 15,000 frames . they propose valency, syntactic function and semantic noun class as input conditions for frame disambiguation . |
| Outcome: | The proposed resource is based on a Danish/German study on hate speech . it achieves an overall F-score for frame senses of 93.6% on twitter . |
The Robotic Surgery Procedural Framebank (2022.lrec-1)
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| Challenge: | Surgical practice has steadily improved thanks to the support of the approaches made available by observational science. |
| Approach: | They propose to extract from robot-surgical texts verbs and nouns that describe surgical actions and extend PropBank frames by adding any of new lemmas, frames or role sets required to cover missing lemae. |
| Outcome: | The proposed resource can be used to train and evaluate Semantic Role Labeling (SRL) systems in a fine-grained domain setting. |
Representing the Toddler Lexicon: Do the Corpus and Semantics Matter? (2022.lrec-1)
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| Challenge: | Existing studies on child language development have relied on adult-based measures to model their lexicons. |
| Approach: | They propose to use transcripts of child-directed conversations, picture books and dialog from G-rated movies to approximate the language input a North American preschooler might hear. |
| Outcome: | The proposed model outperforms models based on the existing corpus and the existing model. |
Organizing and Improving a Database of French Word Formation Using Formal Concept Analysis (2022.lrec-1)
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| Challenge: | Fig. 1 shows examples of five derivational families. |
| Approach: | They propose to use Formal Concept Analysis to organize and improve Démonette2 . they use a poset to represent derivational families in a partially ordered set . |
| Outcome: | The proposed approach can be used to improve the quality of a derivational database in French. |
Towards a new Ontology for Sign Languages (2022.lrec-1)
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| Challenge: | Linked Data (LD) compliant datasets for sign languages are not available in the LLOD cloud. |
| Approach: | They propose to create an ontology for representing constitutive elements of Sign Languages (SL) they propose to publish such data in the Linguistic Linked Open Data cloud. |
| Outcome: | The proposed ontology can be used to represent sign languages in the Linguistic Linked Open Data cloud. |
Towards the Detection of a Semantic Gap in the Chain of Commonsense Knowledge Triples (2022.lrec-1)
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| Challenge: | a commonsense knowledge resource organizes common sense that is not necessarily correct all the time, but most people are expected to know or believe. |
| Approach: | They propose a machine learning-based approach to detect semantic gaps in a commonsense knowledge graph . they use a conceptNet dataset to test the validity of two adjacent triples . |
| Outcome: | The proposed approach detects a semantic gap in a commonsense knowledge graph . the proposed approach also provides insights into the effectiveness of sense embeddings . |
COPA-SSE: Semi-structured Explanations for Commonsense Reasoning (2022.lrec-1)
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| Challenge: | Semi-structured explanations for Choice of Plausible Alternatives (COPA-SSE) are a crowdsourced dataset of 9,747 common sense explanations . |
| Approach: | They propose a semi-structured approach to explain Choice of Plausible Alternatives questions using a crowdsourced dataset of 9,747 common sense explanations with ConceptNet relations but freely written concepts. |
| Outcome: | The proposed explanations are geared towards commonsense reasoners operating on knowledge graphs and serve as a starting point for improving such systems. |
GRhOOT: Ontology of Rhetorical Figures in German (2022.lrec-1)
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| Challenge: | GRhOOT is a domain ontology of rhetorical figures in the German language . the goal is to allow for easier detection of non-literal language based tasks . |
| Approach: | GRhOOT is a domain ontology of 110 rhetorical figures in the german language . the goal is to allow for easier detection and sentiment analysis . |
| Outcome: | The ontology of rhetorical figures in the German language is based on 110 rhetorical figure domains . the goal is to make the ontologies more accurate and to allow for easier detection . |
Querying a Dozen Corpora and a Thousand Years with Fintan (2022.lrec-1)
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| Challenge: | Large-scale quantitative diachronic corpus studies are difficult if multiple corpus are to be consulted . multi-layer corpus technology can solve the problem, but it requires the user to run queries manually. |
| Approach: | They propose a platform for studying word order in German using syntactically annotated corpora . fintan is a flexible integrated transformation and annotation platform . |
| Outcome: | The proposed platform can be used to study word order in German . it hints at two major phases in the development of scrambling in modern german . |
The Index Thomisticus Treebank as Linked Data in the LiLa Knowledge Base (2022.lrec-1)
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| Challenge: | a series of Latin treebanks with word-by-word account of syntax and morphology of Latin texts have been published only in recent years. |
| Approach: | They propose to publish Latin treebanks that contain morphology and syntax annotations . they propose to use principles of the Linguistic Linked Open Data community . |
| Outcome: | The proposed approach enables interoperability between corpora and lexical resources for Latin . language learning and corpus-based research are the most obvious applications . |
Building a Multilingual Taxonomy of Olfactory Terms with Timestamps (2022.lrec-1)
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| Challenge: | olfactory references play a crucial role in our memory and experiences . but only few works in NLP have attempted to capture this sensory dimension from a computational perspective. |
| Approach: | They describe a process that has led to the semi-automatic development of a taxonomy for olfactory information in four languages (English, French, German and Italian) |
| Outcome: | The proposed taxonomy can be extended using existing language models and n-grams to include olfactory terms in four languages. |
Attention Understands Semantic Relations (2022.lrec-1)
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| Challenge: | Present-day monopoly of foundation language models in most tasks forces researchers and practitioners to rely on popular large models without genuinely understanding the models' behaviour. |
| Approach: | They propose a probing pipeline to study the representedness of semantic relations in transformer language models and propose 'attention mechanisms' that focus on syntactic relational information and semantic one. |
| Outcome: | The proposed pipeline shows that attention scores are expressive as output activations on this task, despite their lesser ability to represent surface cues. |
Analysis of Dialogue in Human-Human Collaboration in Minecraft (2022.lrec-1)
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| Challenge: | Recent studies have focused on developing dialogue systems that enable collaborative work, but few studies have centered on creative collaborative work. |
| Approach: | They collected 500 dialogues of human-human collaboration in Minecraft as a basis for developing a dialogue system that enables creative collaborative work. |
| Outcome: | The proposed system can be used to create a collaborative garden in Minecraft and collect text chats, action logs, and subjective evaluations. |
Data Collection for Empirically Determining the Necessary Information for Smooth Handover in Dialogue (2022.lrec-1)
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| Challenge: | Despite advances in deep learning, dialogue systems struggle to achieve fully autonomous transactions with users. |
| Approach: | They conducted an experiment in which two operators switched periodically while performing chat, consultation, and sales tasks in dialogue. |
| Outcome: | The results show that adjacency pairs are useful for recording conversation history . key-value pairs are also useful when there are underlying tasks, such as consultation and sales . |
The slurk Interaction Server Framework: Better Data for Better Dialog Models (2022.lrec-1)
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| Challenge: | slurk is a lightweight dialog data collection and testing tool for crowdsourcing platforms. |
| Approach: | They present a lightweight dialog server that allows to set up dialog data collections and run experiments. |
| Outcome: | The slurk software allows to set up dialog data collections and run experiments with no limitations on the number of participants. |
Corpus Design for Studying Linguistic Nudges in Human-Computer Spoken Interactions (2022.lrec-1)
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Natalia Kalashnikova, Serge Pajak, Fabrice Le Guel, Ioana Vasilescu, Gemma Serrano, Laurence Devillers
| Challenge: | linguistic nudges can influence people to the same degree as a human agent, according to Thaler and Sunstein (2008). |
| Approach: | They propose to use a corpus design method to compare influence between linguistic nudges with positive or negative influences and three conversational agents: robot, smart speaker, and human. |
| Outcome: | The results show that linguistic nudges can influence participants to the same degree as human agents. |
Dialogue Corpus Construction Considering Modality and Social Relationships in Building Common Ground (2022.lrec-1)
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| Challenge: | Several studies have examined the process of building common ground in text chat, but none have investigated the process in depth. |
| Approach: | They constructed a dialogue corpus to investigate the process of building common ground with a particular focus on the modality of dialogue and the social relationship between workers. |
| Outcome: | The results suggest that adding the modality or developing the relationship between workers speeds up the building of common ground. |
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems (2022.lrec-1)
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Shutong Feng, Nurul Lubis, Christian Geishauser, Hsien-chin Lin, Michael Heck, Carel van Niekerk, Milica Gasic
| Challenge: | Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. |
| Approach: | They propose a large-scale manually emotion-annotated corpus of task-oriented dialogues based on a multi-domain task-orientated dataset. |
| Outcome: | The proposed method is based on a task-oriented dialogue dataset with 11K dialogues and 83K emotion annotations of user utterances. |
Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System (2022.lrec-1)
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| Challenge: | Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. |
| Approach: | They propose to build a multimodal dialogue system for children learning basic math concepts using limited datasets. |
| Outcome: | The proposed system improves the Natural Language Understanding (NLU) module of a task-oriented SDS pipeline with limited dataset resources. |
Towards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems (2022.lrec-1)
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| Challenge: | In order to overcome this “self-imposed filter bubble” (SFB), it is crucial to identify influential indicators for the user’s SFB, namely Reflective User Engagement (RUE), Personal Relevance ranking of content-related subtopics as well as False (FK) and True Knowledge (TK). |
| Approach: | They propose to model an SFB by focusing on four indicators for the user's Reflective User Engagement (RUE), their Personal Relevance ranking of content-related subtopics and their False (FK) and True Knowledge (TK) indicators are based on the responses of 202 users of an online argumentative dialogue system BEA. |
| Outcome: | The proposed system aims to break the self-imposed filter bubble (SFB) by identifying indicators for the user's SFB . |
Telling a Lie: Analyzing the Language of Information and Misinformation during Global Health Events (2022.lrec-1)
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| Challenge: | a new dataset is available to stimulate research on health misinformation . linguistic characteristics of health misinfonia are unique to COVID-19 and other events . |
| Approach: | They propose a new dataset that analyzes health misinformation at scale . it includes 2.8 million news articles and social media posts covering diseases . authors propose an annotation framework that allows for strong agreement between annotators . |
| Outcome: | The proposed dataset is based on 2.8 million news articles and social media posts spanning 1900s to present . it shows that the proposed model is robust and can be used to detect misinformation . |
Misogyny and Aggressiveness Tend to Come Together and Together We Address Them (2022.lrec-1)
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| Challenge: | Using a binary task to identify whether a tweet is misogynous and aggressive, we compare two approaches to address these problems: one multi-class model that discriminates between all the classes at once; and a cascaded approach where the binary classification is carried out separately. |
| Approach: | They propose a multi-class model that discriminates between all the classes at once and a cascaded approach where the binary classification is carried out separately and then joined together. |
| Outcome: | The proposed models outperform the top submissions to Evalita on the 2020 shared task on automatic misogyny and aggressiveness identification in Italian tweets. |
The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse (2022.lrec-1)
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Ritesh Kumar, Shyam Ratan, Siddharth Singh, Enakshi Nandi, Laishram Niranjana Devi, Akash Bhagat, Yogesh Dawer, Bornini Lahiri, Akanksha Bansal, Atul Kr. Ojha
| Challenge: | 59,152 comments are annotated with a hierarchical, fine-grained taget marking aggression and bias of various kinds on social media platforms. |
| Approach: | They propose to annotate a multilingual dataset with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. |
| Outcome: | The proposed dataset contains 59,152 comments in four languages, mostly code-mixed with English. |
Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada (2022.lrec-1)
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| Challenge: | a dataset of 45 million geo-located tweets from the US and Canada is used to analyze emotions . early work identified tweets as a crucial indicator of public sentiment . |
| Approach: | They propose a dataset of more than 45 million geo-located tweets from US and Canada . they also introduce Tweet Emotion Dynamics (TED) metrics to capture patterns of emotions associated with tweets . |
| Outcome: | The proposed dataset includes more than 45 million geo-located tweets from US and Canada . it shows that Canadian tweets tend to have higher valence, lower arousal, and higher dominance than the US tweets . |
A Turkish Hate Speech Dataset and Detection System (2022.lrec-1)
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| Challenge: | Davidson et al., 2017: hate speech is a discourse that targets a specific group based on race, gender, religion, sexual orientation, etc. |
| Approach: | They propose a machine learning system for automatic detection of hate speech in Turkish . they use a hate speech dataset and a dataset to collect tweets about immigrants . |
| Outcome: | The proposed system is able to detect hate speech in Turkish and annotate it using BERTurk. |
Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression (2022.lrec-1)
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| Challenge: | a new study explores the relationship between depression and manifestations of happiness in social media . we use Positive-Unlabeled learning paradigm to extract happy moments from social media posts . 264 million people of all ages suffer from depression, according to the u.s. |
| Approach: | They propose a positive-unlabeled learning paradigm to extract happy moments from social media . they use LIWC and keyness information to qualitatively analyze the happy moments . |
| Outcome: | The proposed method extracts happy moments from social media posts of depressed users and controls . it qualitatively analyzes the results with LIWC and keyness information . |
Evaluating Tokenizers Impact on OOVs Representation with Transformers Models (2022.lrec-1)
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| Challenge: | Pre-trained Transformer models have proven their effectiveness in adapting to multiple NLP tasks and domains. |
| Approach: | They evaluated three categories of out-of-vocabulary words using three French domain-specific datasets on the legal, medical, and energetical domains to robustly analyze these categories. |
| Outcome: | The proposed models can create new representations for out-of-vocabulary words by adding external morpho-syntactic context rather than improving the semantic understanding of the words directly. |
Assessing the Quality of an Italian Crowdsourced Idiom Corpus:the Dodiom Experiment (2022.lrec-1)
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| Challenge: | a crowdsourcing experiment has been used to collect idiom-related language resources . the data were collected through a game-with-a-purpose . |
| Approach: | They propose to use a game-with-a-purpose to collect idiom-related language resources . they use criteria adopted for the data annotation and evaluation process . |
| Outcome: | The proposed project evaluated idiom-related language resources from a game-with-a-purpose . the results and future work are presented. |
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking (2022.lrec-1)
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Anton Alekseev, Zulfat Miftahutdinov, Elena Tutubalina, Artem Shelmanov, Vladimir Ivanov, Vladimir Kokh, Alexander Nesterov, Manvel Avetisian, Andrei Chertok, Sergey Nikolenko
| Challenge: | Existing approaches to medical entity linking are limited in terms of data volume and languages. |
| Approach: | They propose to use clinical reports, clinical guidelines, and medical research papers to evaluate cross-lingual medical entity linking. |
| Outcome: | The proposed model outperforms existing models on clinical reports, clinical guidelines, and medical research papers. |
MTLens: Machine Translation Output Debugging (2022.lrec-1)
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Shreyas Sharma, Kareem Darwish, Lucas Pavanelli, Thiago Castro Ferreira, Mohamed Al-Badrashiny, Kamer Ali Yuksel, Hassan Sawaf
| Challenge: | a demo demonstrates a system for quantitatively evaluating MT systems in isolation or multiple MT models collectively . performance of machine translation systems varies significantly with inputs of diverging features, such as genres, genres and surface properties. |
| Approach: | They propose a benchmarking interface that quantitatively evaluates MT systems in isolation or collectively . the interface can be extended to include additional filters such as lexical, morphological, and syntactic features. |
| Outcome: | The proposed system quantitatively evaluates MT systems on multiple domains and evaluation metrics. |
IceBATS: An Icelandic Adaptation of the Bigger Analogy Test Set (2022.lrec-1)
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| Challenge: | a new test set that measures word embeddings' ability to recognize linguistic regularities is presented in a paper in elijsson, iran . the test sets are a good quality estimator for extrinsic evaluation of word embedded models . |
| Approach: | They propose a test set that measures language models' ability to recognize linguistic regularities in a balanced way. |
| Outcome: | The proposed set is apt at measuring the capabilities of word embedding models. |
Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets (2022.lrec-1)
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| Challenge: | Technical logbook data typically has both a domain, the field it comes from, and an application, what it is used for. |
| Approach: | They propose to use domain-specific technical language to identify technical logbook entries by using transfer learning to learn from different domains and from different datasets. |
| Outcome: | The proposed approach improves performance in all cases but one of the three domains studied. |
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data (2022.lrec-1)
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| Challenge: | Automatic de-identification systems introduce errors due to their imperfect precision and may negatively impact the utility of the de-identified dataset. |
| Approach: | They propose to de-identifie a large clinical corpus in Swedish by removing entire sentences containing sensitive data or by replacing sensitive words with realistic surrogates. |
| Outcome: | The proposed models are safe to distribute to other academic researchers and reduce privacy risks. |
Dilated Convolutional Neural Networks for Lightweight Diacritics Restoration (2022.lrec-1)
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| Challenge: | Diacritics restoration is a ubiquitous task in the Latin-alphabet-based English-dominated Internet language environment. |
| Approach: | They propose a 1D dilated convolution-based approach which operates on a character-level. |
| Outcome: | The proposed approach surpasses similar models and is competitive with larger models. |
Generating Artificial Texts as Substitution or Complement of Training Data (2022.lrec-1)
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| Challenge: | Existing approaches to generate text for supervised learning tasks use transformers to generate learning data. |
| Approach: | They propose to use transformers to generate supervised learning data for supervised machine learning tasks and propose to train a neural language model trained on the original training texts. |
| Outcome: | The proposed models can be used in a certain extend but require pre-processing to significantly improve performance. |
From Pattern to Interpretation. Using Colibri Core to Detect Translation Patterns in the Peshitta. (2022.lrec-1)
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| Challenge: | Using Colibri Core to detect translation patterns in the Hebrew Bible and its Syriac source text is a promising approach, but does not allow the creation of a bilingual model. |
| Approach: | They propose to use Colibri Core to detect n-gram and skipgram patterns in the Hebrew Bible and its Syriac source text. |
| Outcome: | The proposed modeller can detect n-gram and skipgram patterns in both Hebrew and Syriac texts without the need for textual annotations. |
PAGnol: An Extra-Large French Generative Model (2022.lrec-1)
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Julien Launay, E.l. Tommasone, Baptiste Pannier, François Boniface, Amélie Chatelain, Alessandro Cappelli, Iacopo Poli, Djamé Seddah
| Challenge: | a growing number of pre-trained language models are available in many different languages. |
| Approach: | They propose a French-language GPT model with scaling laws to train it efficiently . they evaluate the models on discriminative and generative tasks in French . |
| Outcome: | The proposed model trains with the same computational budget as CamemBERT, a model 13 times smaller. |
CEPOC: The Cambridge Exams Publishing Open Cloze dataset (2022.lrec-1)
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| Challenge: | This paper presents the first dataset of open cloze tests for language learners at different proficiency levels. |
| Approach: | They present the Cambridge Exams Publishing Open Cloze (CEPOC) dataset . they perform a set of experiments on three tasks: gap filling, gap prediction, and CEFR text classification. |
| Outcome: | The results of the study are promising for a number of NLP tasks. |
ALBETO and DistilBETO: Lightweight Spanish Language Models (2022.lrec-1)
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| Challenge: | Recent advances in pre-trained language models have made them more popular . however, there are still limited versions of these models for other languages . |
| Approach: | They present ALBETO and DistilBETO which are versions of ALBERT and DistillBERT pre-trained exclusively on Spanish corpora. |
| Outcome: | The proposed models outperform BETO and ALBERT on Spanish datasets . the models outpace BETO on MLDoc, PAWS-X, XNLI, MLQA, SQAC and XQuAD datasets. |
On the Robustness of Cognate Generation Models (2022.lrec-1)
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| Challenge: | We examine different types of noise generated by human errors and how these noisy inputs affect the performance of cognate generation models. |
| Approach: | They evaluate two popular neural cognate generation models’ robustness to human-plausible noise. |
| Outcome: | The proposed models are robust to deletion, duplication, swapping, keyboard errors, and a new type of error, phonological errors. |
CLISTER : A Corpus for Semantic Textual Similarity in French Clinical Narratives (2022.lrec-1)
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| Challenge: | Modern Natural Language Processing relies on the availability of annotated corpora for training and evaluation. |
| Approach: | They propose to annotate sentences in French using a definition of similarity guided by clinical facts and use it to evaluate the corpus. |
| Outcome: | The proposed model can capture similarity with state-of-the-art performance on the DEFT STS shared task evaluation data set. |
The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task (2022.lrec-1)
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| Challenge: | Recent research questions whether these models really understand the meaning of natural language. |
| Approach: | They propose to transform the disambiguation of causative-passive homonymy (CPH) to a challenging natural language inference task using a pretrained transformer model RoBERTa. |
| Outcome: | The pretrained model RoBERTa performs poorly on the CANLI dataset . the model's internal representation of CPH is not captured in the model . |
Modeling Noise in Paraphrase Detection (2022.lrec-1)
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| Challenge: | Noisy labels in training data are challenging and can lead to incorrect decisions . large pre-trained language models have achieved great results in many NLP tasks . |
| Approach: | They propose to use a linear noise model to augment pre-trained language models to account for label noise in fine-tuning. |
| Outcome: | The proposed model can be applied without further knowledge about annotation quality and label confidence of training examples and their results are compared with other models. |
Give me your Intentions, I’ll Predict our Actions: A Two-level Classification of Speech Acts for Crisis Management in Social Media (2022.lrec-1)
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| Challenge: | Using social networks, social media is a vital tool for emergency management and social media has been used to generate valuable information in crisis situations. |
| Approach: | They propose to measure for the first time the role of SA on urgency detection in tweets . they propose to use a two-layer annotation scheme to annotate tweets for both SA and urgency . |
| Outcome: | The proposed scheme combines two-layer annotation scheme and deep learning experiments to detect SA in a crisis corpus. |
Towards a Cleaner Document-Oriented Multilingual Crawled Corpus (2022.lrec-1)
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| Challenge: | Existing web crawling pipelines are used to collect large corpora raw data, but the main way to collect such data is through manual data extraction. |
| Approach: | They propose to use a web crawler to extract and classify data from a multilingual web corpus and an automated annotation pipeline to improve it. |
| Outcome: | The proposed version of OSCAR could be used to pre-train large generative language models and other applications in Natural Language Processing and Digital Humanities. |
A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models (2022.lrec-1)
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Vésteinn Snæbjarnarson, Haukur Barri Símonarson, Pétur Orri Ragnarsson, Svanhvít Lilja Ingólfsdóttir, Haukur Jónsson, Vilhjalmur Thorsteinsson, Hafsteinn Einarsson
| Challenge: | Pre-trained neural language models have shown impressive results when adapted for a variety of classification and text generation tasks. |
| Approach: | They propose to use Icelandic's Icelandic Common Crawl Corpus to train language models that achieve state-of-the-art performance in downstream tasks. |
| Outcome: | The proposed models achieve state-of-the-art in a variety of downstream tasks including part-of speech tagging, named entity recognition and constituency parsing. |
Adapting Language Models When Training on Privacy-Transformed Data (2022.lrec-1)
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| Challenge: | Using data sanitization methods to remove personal information from spoken messages is not effective because privacy-transformed data is unlikely to match the test distribution. |
| Approach: | They propose to use a data sanitization approach to remove personal information from spoken messages by replacing named entities with other words from the same class. |
| Outcome: | The proposed approach removes personal information from the spoken messages using an automatic named entity recognition method. |
Evaluation of Transfer Learning for Polish with a Text-to-Text Model (2022.lrec-1)
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Aleksandra Chrabrowa, Łukasz Dragan, Karol Grzegorczyk, Dariusz Kajtoch, Mikołaj Koszowski, Robert Mroczkowski, Piotr Rybak
| Challenge: | Recent years have brought significant progress in natural language understanding (NLU) and natural language generation (NLG). |
| Approach: | They propose a benchmark for assessing the quality of text-to-text models for Polish . they evaluate the performance of plT5, mT5, Polish BART, and Polish GPT-2 . |
| Outcome: | The proposed model can be fine-tuned on various NLP tasks with a single training objective. |
Evaluation of HTR models without Ground Truth Material (2022.lrec-1)
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Phillip Benjamin Ströbel, Martin Volk, Simon Clematide, Raphael Schwitter, Tobias Hodel, David Schoch
| Challenge: | Optical Character Recognition (OCR) is a well-established technique for digitising historical printed collections in libraries and archives. |
| Approach: | They propose to use masked language models to evaluate handwritten text recognition models . they propose to introduce GT-free metrics to evaluate models to ensure best results . |
| Outcome: | The proposed model evaluations are based on lexicon-based and masked language models. |
A Semi-Automated Live Interlingual Communication Workflow Featuring Intralingual Respeaking: Evaluation and Benchmarking (2022.lrec-1)
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| Challenge: | Traditionally, live interlingual communication has been achieved only with the help of human interpreters. |
| Approach: | They propose a semi-automated workflow which uses a human respeaker and speaker-dependent speech recognition software to deliver punctuated same-language output of superior quality than the out-of-the-box ASR system. |
| Outcome: | The proposed workflow produces a similar quality output to the best-in-class simultaneous interpreters working with the same source speeches from the European Parliament. |
Are Embedding Spaces Interpretable? Results of an Intrusion Detection Evaluation on a Large French Corpus (2022.lrec-1)
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| Challenge: | Word embedding methods use word co-occurrences to encode, syntactic and semantic information to describe vocabulary in a low-dimensional space. |
| Approach: | They evaluate word embedding interpretability using two methods . they use a word-in-space vector encoder and graph-based method SPINE . |
| Outcome: | The proposed methods show that they can be interpretable on a large French corpus. |
Corpus for Automatic Structuring of Legal Documents (2022.lrec-1)
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Prathamesh Kalamkar, Aman Tiwari, Astha Agarwal, Saurabh Karn, Smita Gupta, Vivek Raghavan, Ashutosh Modi
| Challenge: | In populous countries, pending legal cases are growing exponentially. |
| Approach: | They propose a corpus of legal judgment documents in English that is annotated with a label coming from a list of pre-defined rhetorical roles. |
| Outcome: | The proposed corpus of legal judgment documents is annotated with a label coming from a list of pre-defined rhetorical roles. |
The Search for Agreement on Logical Fallacy Annotation of an Infodemic (2022.lrec-1)
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Claire Bonial, Austin Blodgett, Taylor Hudson, Stephanie M. Lukin, Jeffrey Micher, Douglas Summers-Stay, Peter Sutor, Clare Voss
| Challenge: | a parallel "infodemic" has emerged with the COVID-19 pandemic . logical fallacies can be subtly encoded in the structure of a document across multiple sentences . |
| Approach: | They evaluate an annotation schema for labeling logical fallacy types using linguist annotations . they propose to use a machine learning algorithm to train annotators for fallacy detection . |
| Outcome: | The proposed annotation schema is clear and non-overlapping for manual and system assignment. |
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR) (2022.lrec-1)
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| Challenge: | Existing medical social media corpora focus on a small set of entities and relations . existing text mining and information extraction methods focus on scientific text generated by researchers but their access to individual patient experiences or patient-doctor interactions is limited. |
| Approach: | The dataset consists of 2,100 medical tweets with approx. 6,000 entities and 2,200 relations. |
| Outcome: | The proposed dataset consists of 2,100 tweets with approx. 6,000 entities and 2,200 relations. |
Improving Event Duration Question Answering by Leveraging Existing Temporal Information Extraction Data (2022.lrec-1)
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| Challenge: | Existing tasks that require knowledge about event duration require limited duration data . a two-stage fine-tuning approach might fail due to discrepancy between task and duration data. |
| Approach: | They propose to recast existing event duration classification task to a question answering task similar to McTACO. |
| Outcome: | The proposed model achieves a 13% Exact Match score improvement over baseline on the McTACO duration question answering task. |
Entity Linking over Nested Named Entities for Russian (2022.lrec-1)
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Natalia Loukachevitch, Pavel Braslavski, Vladimir Ivanov, Tatiana Batura, Suresh Manandhar, Artem Shelmanov, Elena Tutubalina
| Challenge: | Entity linking is a popular NLP task, where a system needs to link a named entity to a concept in a knowledge base such as Wikidata. |
| Approach: | They describe the main design principles behind entity linking annotation in the recently released Russian NEREL dataset for information extraction. |
| Outcome: | The NEREL dataset is the largest Russian dataset annotated with entities and relations. |
HiNER: A large Hindi Named Entity Recognition Dataset (2022.lrec-1)
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Rudra Murthy, Pallab Bhattacharjee, Rahul Sharnagat, Jyotsana Khatri, Diptesh Kanojia, Pushpak Bhattacharyya
| Challenge: | Named Entity Recognition (NER) is a lowerlevel task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. |
| Approach: | They propose to use a standard-abiding Hindi NER dataset to analyze the annotations of a class of naming entities in free text. |
| Outcome: | The proposed dataset achieves a weighted F1 score of 88.78 with all the tags and 92.22 when we collapse the tag-set. |
Bootstrapping Text Anonymization Models with Distant Supervision (2022.lrec-1)
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| Challenge: | Personal data is ubiquitous in text documents. |
| Approach: | They propose a method to bootstrap text anonymization models based on distant supervision by using a knowledge graph to annotate text documents including personal data. |
| Outcome: | The proposed method uses a knowledge graph to annotate text documents including personal data about a subset of individuals. |
Natural Questions in Icelandic (2022.lrec-1)
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| Challenge: | Developing such datasets is important for the development and evaluation of Icelandic QA systems. |
| Approach: | They present the first extractive question answering dataset for Icelandic, Natural Questions in Icelandic. |
| Outcome: | The proposed dataset is a valuable resource for Icelandic which is being evaluated by a team of researchers. |
QA4IE: A Quality Assurance Tool for Information Extraction (2022.lrec-1)
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| Challenge: | Existing tools for data annotation do not provide comprehensive support for quality assurance. |
| Approach: | They propose a QA tool for information extraction that detects potential problems in text annotations in a timely manner and accurately assesses the quality of annotations. |
| Outcome: | The proposed tool can detect potential problems in text annotations in a timely manner, accurately assess the quality of annotations, and visually display and summarize annotation discrepancies among annotation team members. |
A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts (2022.lrec-1)
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| Challenge: | Recent advances in natural language processing have lowered the barriers for people outside the NLP community to tap into the tools and resources applied to a variety of domain-specific applications. |
| Approach: | They propose to annotate court transcripts from genocide-related cases using transformer-based approaches and to establish benchmarks for the task of paragraph identification of violence-related witness statements. |
| Outcome: | The first annotated corpus of genocide-related court transcripts is aimed at providing a first reference corpus for the community and to establish benchmark performances using state-of-the-art transformer-based approaches. |
DeepREF: A Framework for Optimized Deep Learning-based Relation Classification (2022.lrec-1)
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| Challenge: | Existing frameworks for relation extraction (RE) are limited due to lack of implementation details. |
| Approach: | They propose to use deep learning to develop relation extraction systems using deep learning models. |
| Outcome: | The proposed framework is inspired by the OpenNRE and REflex existing frameworks. |
Exploring Data Augmentation Strategies for Hate Speech Detection in Roman Urdu (2022.lrec-1)
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| Challenge: | a number of social media platforms are generating hateful content, a new study finds . augmentation techniques are needed to improve the performance of the models . |
| Approach: | They evaluate different data augmentation techniques for the improvement of hate speech detection in Roman Urdu. |
| Outcome: | The proposed techniques improve hate speech detection in Roman Urdu on two datasets. |
Incorporating LIWC in Neural Networks to Improve Human Trait and Behavior Analysis in Low Resource Scenarios (2022.lrec-1)
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| Challenge: | Psycholinguistic knowledge resources have been widely used in constructing features for text-based human trait and behavior analysis. |
| Approach: | They propose to incorporate a widely-used psycholinguistic lexicon into NN models to improve human trait and behavior analysis in low resource scenarios. |
| Outcome: | The proposed methods perform significantly better than baselines that use only LIWC or NN-based feature learning methods. |
Using Sentence-level Classification Helps Entity Extraction from Material Science Literature (2022.lrec-1)
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| Challenge: | Material Science research articles are a rich source of information about entities related to material science. |
| Approach: | They propose to use a sentence-level classifier to identify sentences containing at least one entity mention . they then apply the information extraction models only on the filtered sentences to extract various entities of interest. |
| Outcome: | The proposed model improves the F1 score by more than 4% . the proposed model removes redundant sentences from the articles that contain informative entities . |
A Twitter Corpus for Named Entity Recognition in Turkish (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is a subtask of information extraction that uses predefined named entities to identify NEs in noisy texts. |
| Approach: | They propose to use a Turkish Twitter Named Entity Recognition dataset to identify predefined named entities (NEs) the dataset contains 5000 tweets from a year-long period with a high agreement score. |
| Outcome: | The proposed dataset contains 5000 tweets from a year-long period and has high agreement scores. |
A STEP towards Interpretable Multi-Hop Reasoning:Bridge Phrase Identification and Query Expansion (2022.lrec-1)
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| Challenge: | Identifying bridge phrases remains one of the challenges for multi-hop question answering . |
| Approach: | They propose an unsupervised method for the identification of bridge phrases in multi-hop question answering . they construct a graph of noun phrases from the question and available context . |
| Outcome: | The proposed method improves all downstream components in a multi-hop QA system. |
Question Generation and Answering for exploring Digital Humanities collections (2022.lrec-1)
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| Challenge: | Recent advances in representation learning of text have achieved impressive results on benchmark Natural Language Understanding (NLU) tasks. |
| Approach: | They propose a question answering paradigm that uses a BART Transformer based generative model to generate question data. |
| Outcome: | The proposed approach is validated on a new corpus of digitized archive collections of a French Social Science journal. |
Evaluating Retrieval for Multi-domain Scientific Publications (2022.lrec-1)
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Nancy Ide, Keith Suderman, Jingxuan Tu, Marc Verhagen, Shanan Peters, Ian Ross, John Lawson, Andrew Borg, James Pustejovsky
| Challenge: | a new framework for retrieval and mining of scientific publications is being developed . the AskMe retrieval engine is a bridge between xDD's publication database and the LAPPS Grid suite of NLP tools. |
| Approach: | They evaluate AskMe retrievalengine using BEIR benchmark datasets . they aim to determine when and why certain approaches perform well on in-domain and out-of-domain data. |
| Outcome: | The AskMe retrieval engine performs well on both in-domain and out-of-domain data. |
Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients (2022.lrec-1)
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Jenia Kim, Stella Verkijk, Edwin Geleijn, Marieke van der Leeden, Carel Meskers, Caroline Meskers, Sabina van der Veen, Piek Vossen, Guy Widdershoven
| Challenge: | Electronic Health Records contain a lot of information in natural language that is not expressed in structured clinical data. |
| Approach: | They propose a Dutch language model that can determine the functional level of patients according to a WHO coding framework. |
| Outcome: | The proposed model can determine the functional level of patients according to a WHO coding framework. |
Hierarchical Aggregation of Dialectal Data for Arabic Dialect Identification (2022.lrec-1)
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| Challenge: | Previous work on Arabic Dialect identification focused on specific dialect levels and labels . since dialectal differences tend to be more subtle relative terms to language differences, the DID task is harder than language identification. |
| Approach: | They propose to define a standard hierarchical schema for Arabic Dialect identification . they map 29 different data sets to this schema and use it to aggregate the data . |
| Outcome: | The proposed schemas and methods are extensible to other languages and dialect groups. |
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification (2022.lrec-1)
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| Challenge: | Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate due to domain experts taking a lot of time working through all the classes. |
| Approach: | They propose to build classifiers on multi-label text datasets using Active Learning to reduce labeling effort. |
| Outcome: | The proposed classifiers can be used to reduce labeling effort on multi-label datasets. |
German Light Verb Constructions in Business Process Models (2022.lrec-1)
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| Challenge: | a resource of German light verb constructions is presented for graphical business process models . the language in BPM is worth to be studied for other purposes, authors say . |
| Approach: | They present a resource of German light verb constructions extracted from business process models . they use textual labels to analyze the models and to infer the meaning of their texts . |
| Outcome: | The proposed resource contains German light verb constructions extracted from business process models . the work is a step towards better automatic analysis of business process model models based on the proposed language . |
PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics (2022.lrec-1)
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| Challenge: | physicists use mathematics to reason and explain, separates their field from other disciplines, including mathematics. |
| Approach: | They present a dataset to evaluate the performance of language models in physics . they find that language models are challenged by coherence related tasks in physicists . |
| Outcome: | The proposed models are able to perform well on coherence-related tasks even when trained on natural language objectives. |
HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French (2022.lrec-1)
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| Challenge: | Existing systems for automatic text simplification (ATS) focus on lexical and syntactic transformations, but there is no end-to-end system for French. |
| Approach: | They propose to use word embeddings for lexical simplification and rule-based strategies for syntax and discourse adaptations to improve the complexity of texts. |
| Outcome: | The proposed system performs at lexical, syntactic and discourse levels according to automatic and humanevaluations. |
AiRO - an Interactive Learning Tool for Children at Risk of Dyslexia (2022.lrec-1)
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| Challenge: | AiRO learning tool is designed for use in classrooms and homes by children at risk of developing dyslexia. |
| Approach: | They propose to use the AiRO learning tool in classrooms and homes by children at risk of developing dyslexia. |
| Outcome: | The AiRO learning tool outperforms the control group in the first test 'in vivo' with 49 pupils aged 6 . |
Creating a Basic Language Resource Kit for Faroese (2022.lrec-1)
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| Challenge: | a few resources exist for Faroese, but many of them are insufficient size and quality or are not easily accessible. |
| Approach: | They propose to make a BLARK for Faroese that will be open-source and use other languages' resources. |
| Outcome: | The proposed BLARK will be a pillar in Faroese LR, and will be open-source . authors comment on the faulty yet sprouting LT situation in the Faroest islands . |
Developing a Spell and Grammar Checker for Icelandic using an Error Corpus (2022.lrec-1)
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| Challenge: | lack of datasets for spelling and grammar correction in Icelandic has caused a dearth of spell and grammar checking systems for the low-resource language. |
| Approach: | They propose to use an error corpus to guide the development of a spell and grammar checking tool for Icelandic, using tokenizer, tagger, and parser. |
| Outcome: | The proposed system is based on an error corpus at all stages of development and is reproducible for other low- or medium-resource languages and especially well-suited to morphologically rich languages. |
The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves (2022.lrec-1)
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| Challenge: | Currently, classroom recordings are limited due to practical and privacy concerns and sharing is restricted due to limited access to valuable resources and data sets. |
| Approach: | They propose to use the TalkMoves dataset to analyze the nature of teacher and student discourse in K-12 math classrooms. |
| Outcome: | The TalkMoves dataset contains 567 human-annotated K-12 mathematics lesson transcripts derived from video recordings. |
Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis (2022.lrec-1)
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| Challenge: | In second language learning, summaries are among the most popular type of student assignments. |
| Approach: | They propose to revise the annotation guidelines to allow machine implementation of the new annotation guidelines. |
| Outcome: | The proposed algorithm achieves 0.789 precision and 0.844 recall over the L2WS 2021 corpus. |
IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments (2022.lrec-1)
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| Challenge: | Using crowdsourcing, we show that models trained with domain-specific implicit reasonings outperform domain-general models in both automatic and human evaluations. |
| Approach: | They propose to create a domain-specific corpus of implicit reasonings annotated for a wide range of arguments and use it to generate models. |
| Outcome: | The proposed corpus outperforms domain-general models in automatic and human evaluations. |
Conversational Speech Recognition Needs Data? Experiments with Austrian German (2022.lrec-1)
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| Challenge: | Recent advances in self-supervision have allowed such scenarios to take advantage of large amounts of otherwise unrelated data. |
| Approach: | They characterise an Austrian German conversational task using a non-pre-trained baseline and a leave-one-conversation out technique. |
| Outcome: | The proposed model fine-tunes well with a non-pre-trained baseline and shows that the advantage of pre-training arises from the larger database rather than the self-supervision. |
A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications (2022.lrec-1)
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| Challenge: | Automated text generation has achieved performance levels that make the generated text almost indistinguishable from those written by humans. |
| Approach: | They propose to use a completely synthetic dataset and a partial text substitution dataset to evaluate the quality of the generated research content. |
| Outcome: | The proposed datasets compare the generated texts to aligned original texts using fluency metrics such as BLEU and ROUGE. |
Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification (2022.lrec-1)
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| Challenge: | Existing work on question answering systems assumes all questions are intelligible and unambiguous . however, available datasets do not meet requirements for building commercial virtual assistants . |
| Approach: | They propose to make question answering systems more robust by classifying if question is intelligible and returning a clarification question for contextual ambiguity. |
| Outcome: | The proposed system can classify if the input question is intelligible and return a clarification question for ambiguous questions. |
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers (2022.lrec-1)
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Thomas Kolb, Sekanina Katharina, Bettina Manuela Johanna Kern, Julia Neidhardt, Tanja Wissik, Andreas Baumann
| Challenge: | Existing methods for sentiment analysis are limited in the Austrian German domain due to lexical idiosyncrasies and word sentiment. |
| Approach: | They propose to use crowd-sourced crowd-sourcing to create a sentiment dictionary for Austrian German . they use an austriacism list and a posting data set to increase the diversity of the language resource. |
| Outcome: | The proposed dictionary is available for future research and free to use for anyone. |
Text Classification and Prediction in the Legal Domain (2022.lrec-1)
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| Challenge: | a case study combines text classification and legal judgment prediction for flight compensation . a human-in-the-loop model outperformed human prediction when predicting a claim being successful . |
| Approach: | They combine transformer-based classification models with human-in-the-loop data to classify airlines' responses to flight compensation claims. |
| Outcome: | The proposed models outperform human prediction when predicting a legal claim success. |
I still have Time(s): Extending HeidelTime for German Texts (2022.lrec-1)
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| Challenge: | HeidelTime is a widely used tool for detecting temporal expressions in texts. |
| Approach: | They propose to extend HeidelTime's pattern matching system by observing false negatives within real world texts and various time banks. |
| Outcome: | The proposed extension can detect expressions in texts and time banks in a convenient way. |
Morphological Complexity of Children Narratives in Eight Languages (2022.lrec-1)
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Gordana Hržica, Chaya Liebeskind, Kristina Š. Despot, Olga Dontcheva-Navratilova, Laura Kamandulytė-Merfeldienė, Sara Košutar, Matea Kramarić, Giedrė Valūnaitė Oleškevičienė
| Challenge: | morphological complexity of a corpus representing the language production of younger and older children is compared across different languages. |
| Approach: | a study compares morphological complexity of a corpus representing language production of younger and older children across different languages. |
| Outcome: | The results show that younger children corpora have lower morphological complexity than older children corpus for Spanish and Russian. |
EXPRES Corpus for A Field-specific Automated Exploratory Study of L2 English Expert Scientific Writing (2022.lrec-1)
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| Challenge: | Developing proficient writing skills in English is a debated topic since the 1990s . RAs are an academic genre that hold a central place in academia . |
| Approach: | They propose to use a linguistic assessment model to assess the linguistic profile of research articles written in L2 English. |
| Outcome: | The proposed model can help scholars adapt to the writing norms of their communities of practice. |
An Evaluation Framework for Legal Document Summarization (2022.lrec-1)
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| Challenge: | Existing metrics for summarizing legal documents fail to evaluate intent in the original text. |
| Approach: | They propose an automated intent-based summarization metric which shows a better agreement with human evaluation as compared to other automated metrics like BLEU, ROUGE-L etc. |
| Outcome: | The proposed method shows that human evaluation is more accurate than other metrics. |
Complex Labelling and Similarity Prediction in Legal Texts: Automatic Analysis of France’s Court of Cassation Rulings (2022.lrec-1)
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Thibault Charmet, Inès Cherichi, Matthieu Allain, Urszula Czerwinska, Amaury Fouret, Benoît Sagot, Rachel Bawden
| Challenge: | Detecting divergences in the applications of the law is an important task . divergencies can occur at three levels: within the Cour de Cassation, between trial courts and, more rarely, between a trial court and the Cour of Cassion. |
| Approach: | They propose to provide automatic tools to facilitate the search for similar rulings . they provide automatic keyword sequence generation models and predict keyword sequences based on available texts . |
| Outcome: | The proposed tools improve correlations between the obtained similarities and human judgments of similarity. |
Cyrillic-MNIST: a Cyrillic Version of the MNIST Dataset (2022.lrec-1)
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| Challenge: | Existing datasets for computer vision to distinguish printed or handwritten characters in digital images are limited to one language. |
| Approach: | They propose to use a Cyrillic version of the MNIST dataset to analyze handwritten letters. |
| Outcome: | The proposed dataset is compared to the Extended MNIST (EMNIST) dataset and is available at https://github.com/bolattleubayev/cmnist. |
gaBERT — an Irish Language Model (2022.lrec-1)
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James Barry, Joachim Wagner, Lauren Cassidy, Alan Cowap, Teresa Lynn, Abigail Walsh, Mícheál J. Ó Meachair, Jennifer Foster
| Challenge: | We compare gaBERT to multilingual BERT and the monolingual Irish WikiBERT and show that gaBERt provides better representations for downstream parsing tasks. |
| Approach: | They propose a monolingual BERT model for the Irish language that provides better representations for a downstream parsing task. |
| Outcome: | The proposed model performs better than the multilingual BERT and the monolingual Irish WikiBERT on a parsing task. |
PoS Tagging, Lemmatization and Dependency Parsing of West Frisian (2022.lrec-1)
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Wilbert Heeringa, Gosse Bouma, Martha Hofman, Jelle Brouwer, Eduard Drenth, Jan Wijffels, Hans Van de Velde
| Challenge: | a lemmatizer/PoS tagger/dependency parser for west frisian is released as a web app and as . web service. |
| Approach: | They propose a lemmatizer/PoS tagger/dependency parser for West Frisian using a corpus of 44,714 words in 3,126 sentences that were annotated according to the guidelines of Universal Dependencies version 2. |
| Outcome: | The proposed lemmatizer/PoS tagger/dependency parser performs better than the previous version of Oersetter . the current corpus contains 44,714 words in 3,126 sentences . |
A Dataset of Offensive German Language Tweets Annotated for Speech Acts (2022.lrec-1)
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| Challenge: | Using speech act analysis, we analysed 600 offensive and non-offensive tweets in germany . a large body of research exists on the pragmatic characteristics of offensive language . |
| Approach: | They analyze German offensive and non-offensive tweets and use a subset of the 2019 GermEval Shared Task on the Identification of Offensive Language dataset. |
| Outcome: | The proposed dataset includes 600 offensive and non-offensive tweets annotated for speech acts in germany. |
Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German (2022.lrec-1)
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| Challenge: | a recent study has focused on the syntactic development of scientific discourse in English and German. |
| Approach: | They present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th d.) annotated with Universal Dependencies. |
| Outcome: | The presented corpora are comparable to existing studies on grammatical change in English and German . the results show that the pre-processing steps significantly improve parsing accuracy . |
The Tembusu Treebank: An English Learner Treebank (2022.lrec-1)
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| Challenge: | a new treebank is created to help diagnose ungrammatical sentences using mal-rules . the Tembusu Learner Treebank is an open treebank created from the corpus of Learner English . |
| Approach: | They propose to use the Tembusu Learner Treebank to train a new parse-ranking model for the English Resource Grammar . the model incorporates mal-rules in the annotation of ungrammatical sentences . |
| Outcome: | The Tembusu Learner Treebank is an open treebank created from the NTU Corpus of Learner English . the treebank is unique for incorporating mal-rules in the annotation of ungrammatical sentences . |
The Norwegian Dialect Corpus Treebank (2022.lrec-1)
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Andre Kåsen, Kristin Hagen, Anders Nøklestad, Joel Priestly, Per Erik Solberg, Dag Trygve Truslew Haug
| Challenge: | The NDC Treebank consists of recordings made between 2006 and 2012 and is annotated with morphological and syntactic information. |
| Approach: | They present the NDC Treebank of spoken Norwegian dialects in the Bokml variety of Norwegian. |
| Outcome: | The treebank consists of 4587 speech segments, overall 66009 tokens, from 17 different Norwegian dialects from south, west, east and north of Norway. |
RRGparbank: A Parallel Role and Reference Grammar Treebank (2022.lrec-1)
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Tatiana Bladier, Kilian Evang, Valeria Generalova, Zahra Ghane, Laura Kallmeyer, Robin Möllemann, Natalia Moors, Rainer Osswald, Simon Petitjean
| Challenge: | Existing treebanks for Role and Reference Grammar (RRG) are not yet available. |
| Approach: | They propose to use a multilingual parallel treebank for Role and Reference Grammar to apply RRG to large-scale corpus annotations of 1984 and its translations. |
| Outcome: | The proposed treebank contains annotations of Orwell's 1984 and translations thereof. |
Unifying Morphology Resources with OntoLex-Morph. A Case Study in German (2022.lrec-1)
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| Challenge: | OntoLex is a widely used community standard for machine-readable lexical resources on the web. |
| Approach: | They propose a module for representing morphology that can be used to encode and integrate morphological resources on a unified basis. |
| Outcome: | The proposed module can be used to represent morphological resources on a unified basis. |
Building Dataset for Grounding of Formulae — Annotating Coreference Relations Among Math Identifiers (2022.lrec-1)
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| Challenge: | Generally speaking, the meanings of math symbols are not necessarily constant, and the same symbol is used in multiple meanings. |
| Approach: | They annotated 15 papers with the meanings of math symbols and found they can be grounding . they developed a special annotation tool to help them identify the meaning of each symbol . |
| Outcome: | The constructed dataset shows that the meanings of symbols can be ground with a high agreement . the authors developed a special annotation tool to analyze the data . |
CorefUD 1.0: Coreference Meets Universal Dependencies (2022.lrec-1)
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| Challenge: | Recent advances in standardization for annotated language resources have led to successful large scale efforts, such as the Universal Dependencies (UD) project for multilingual syntactically annotized data. |
| Approach: | They propose a multilingual collection of corpora and a standardized format for coreference resolution compatible with morphosyntactic annotations in the UD framework. |
| Outcome: | The proposed framework is compatible with morphosyntactic annotations and includes facilities for related tasks such as named entity recognition. |
The Universal Anaphora Scorer (2022.lrec-1)
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| Challenge: | a new version of the Reference Coreference Scorer is proposed to evaluate anaphoric interpretations . the proposed approach to evaluation of split antecedent anaphorisms is entirely novel . |
| Approach: | They propose an extended version of the Reference Coreference Scorer to evaluate anaphoric interpretations . the UA scorer supports the evaluation of split antecedent anaphorisms and discourse deixis . |
| Outcome: | The proposed method can be used to evaluate anaphoric interpretations in an extended range of anas . it supports evaluations of split antecedent anaphorisms and discourse deixis, for which no tools exist . |
Towards Evaluation of Cross-document Coreference Resolution Models Using Datasets with Diverse Annotation Schemes (2022.lrec-1)
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| Challenge: | Existing cross-document coreference resolution (CDCR) datasets contain event-centric coreference chains of events and entities with identity relations. |
| Approach: | They propose to use a phrasing diversity metric to evaluate lexical diversity of CDCR datasets . they propose to combine CDCR annotation schemes with multiple properties of the coreference chains . |
| Outcome: | The proposed phrasing diversity metric evaluates the CDCR datasets with higher precision. |
Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment (2022.lrec-1)
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| Challenge: | Existing models for fake news classification are difficult to explain and quality-assure . however, they are black-box-based and lack a clear explanation of their decisions. |
| Approach: | They propose an interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM) they use conjunctive clauses to capture lexical and semantic properties of both true and fake news text and use clause ensembles to calculate the credibility of fake news. |
| Outcome: | The proposed framework outperforms baseline models on PolitiFact and GossipCop datasets in terms of accuracy and provides higher F1-score than BERT and XLNet, but lower accuracy. |
Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition (2022.lrec-1)
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| Challenge: | Word embeddings can capture the semantics of words and other hidden features, but the Arabic language is complex and requires a large amount of information to process. |
| Approach: | They propose to add morphological and syntactical features to Arabic word embeddings to train the model. |
| Outcome: | The proposed model outperforms the previous systems to the best of our knowledge. |
SCAI-QReCC Shared Task on Conversational Question Answering (2022.lrec-1)
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| Challenge: | evaluating systems for conversational QA remains an open research problem in its own right . evaluating (conversational) QA systems remains an important challenge for developing conversational information retrieval (conversional search) systems. |
| Approach: | They propose to use a conversational question answering task to extend the original conversational QA dataset with alternative correct answers produced by participant systems. |
| Outcome: | The proposed task was based on the SCAI-QReCC 2021 shared task on conversational question answering. |
Semantic Relations between Text Segments for Semantic Storytelling: Annotation Tool - Dataset - Evaluation (2022.lrec-1)
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| Challenge: | Semantic Storytelling is the goal of the future to generate stories based on extracted, processed, classified and annotated information from large content resources. |
| Approach: | They propose to create an automatic classifier for semantic relations between extracted text segments from different news articles. |
| Outcome: | The proposed method has high accuracy scores and is validated by a trained model. |
Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages (2022.lrec-1)
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| Challenge: | a lack of parallel data is a major limitation for Neural Machine Translation systems, especially for morphologically rich languages. |
| Approach: | They propose to leverage target monolingual data to overcome the lack of parallel data . they introduce a new technique called PT-Inflect to train NMT systems . |
| Outcome: | The proposed techniques outperform NMT systems trained on parallel data on four typologically diverse target languages. |
Aligning Images and Text with Semantic Role Labels for Fine-Grained Cross-Modal Understanding (2022.lrec-1)
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| Challenge: | Currently, image retrieval systems can retrieve relevant results for diverse inputs, but they do not provide a way to intentionally inject variety into the search results. |
| Approach: | They propose a multimodal dataset that combines semantic annotations with image bounding boxes. |
| Outcome: | The proposed system improves image retrieval performance and flexibility. |
Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation (2022.lrec-1)
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Elise Bertin-Lemée, Annelies Braffort, Camille Challant, Claire Danet, Boris Dauriac, Michael Filhol, Emmanuella Martinod, Jérémie Segouat
| Challenge: | a new corpus of french Sign Language (LSF) data is created to support future studies on the automatic translation of written French into LSF, rendered through the animation of a virtual signer. |
| Approach: | They propose to use a French Sign Language corpus called "Rosetta-LSF" it is intended to support studies on automatic translation of written French into LSF . |
| Outcome: | The proposed corpus supports future studies on automatic translation of written French into LSF, rendered through animation of a virtual signer. |
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset (2022.lrec-1)
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Marina Fomicheva, Shuo Sun, Erick Fonseca, Chrysoula Zerva, Frédéric Blain, Vishrav Chaudhary, Francisco Guzmán, Nina Lopatina, Lucia Specia, André F. T. Martins
| Challenge: | Existing datasets for machine translation quality estimation and post-editing have several shortcomings. |
| Approach: | They propose a dataset for machine translation quality estimation and automatic post-editing . they report the performance of baseline systems trained on the MLQE-PE dataset . |
| Outcome: | The proposed dataset contains human labels for up to 10,000 translations per language pair. |
OpenKorPOS: Democratizing Korean Tokenization with Voting-Based Open Corpus Annotation (2022.lrec-1)
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| Challenge: | Korean uses spaces at larger-than-word boundaries, unlike other East-Asian languages. |
| Approach: | They propose to use Korean morphological analyzers to provide a sequence of morpheme-level tokens, losing information in the tokenization process. |
| Outcome: | The proposed scheme improves existing tagging scheme and makes it friendlier to generative tasks. |
Enriching Grammatical Error Correction Resources for Modern Greek (2022.lrec-1)
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| Challenge: | Davidson and Kilgarriff, 2011) have focused on the English language, but there are limited efforts to expand GEC in other languages. |
| Approach: | They develop and test a multilingual text-to-text transformer for Greek . they provide a model that can be fully-fledged for Greek with annotation corrections . |
| Outcome: | The proposed model achieves 52.63% F0.5 on part of the Greek Native Corpus, 16% below the winning system on English GEC. |
A Hmong Corpus with Elaborate Expression Annotations (2022.lrec-1)
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| Challenge: | SCH is the first substantial corpus to be annotated for elaborate expressions . a plurality of speakers are located in China, but many Hmong speakers left Laos as refugees . |
| Approach: | They describe the first publicly available corpus of Hmong, a minority language of China, Vietnam, Laos, Thailand, and various countries in Europe and the Americas. |
| Outcome: | The first publicly available corpus of Hmong is scraped from a long-running Usenet newsgroup . it is the first substantial corpus to be annotated for elaborate expressions . |
ELAL: An Emotion Lexicon for the Analysis of Alsatian Theatre Plays (2022.lrec-1)
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| Challenge: | a novel and manually corrected emotion lexicon is presented for Alsatian dialects . the dialects are used mainly orally and lack a stable and consensual spelling convention . |
| Approach: | They propose a novel and manually corrected emotion lexicon for Alsatian dialects . they use graphical variants of Alsalian lexical items to perform automatic emotion analysis . |
| Outcome: | The novel and manually corrected emotion lexicon is used to perform automatic emotion analysis in Alsatian theatre plays. |
Universal Dependencies for Western Sierra Puebla Nahuatl (2022.lrec-1)
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| Challenge: | Annotated corpus of western Sierra Puebla Nahuatl conforms to universal dependency project annotation guidelines . morphological and syntactic phenomena can be analyzed quantitatively with a large enough corpus . |
| Approach: | They present a morpho-syntactically-annotated corpus of western Sierra Puebla Nahuatl . it is the first indigenous language of Mexico to be added to the Universal Dependencies project . UD is a widely-used annotation framework whose aim is to provide a consistent schema for morphological and syntactic phenomena for all of the world's languages. |
| Outcome: | The morpho-syntactically-annotated corpus of western Sierra Puebla Nahuatl conforms to the universal dependency project annotation guidelines. |
The Construction and Evaluation of the LEAFTOP Dataset of Automatically Extracted Nouns in 1480 Languages (2022.lrec-1)
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| Challenge: | The Ethnologue reports the existence of 7,139 living languages. |
| Approach: | They propose to use probabilistic inference to identify likely translations in 1480 other languages . they use a Koine Greek New Testament as the source language and a custom scraper . |
| Outcome: | The proposed method provides lexiconaries with accuracy from 42% (Korafe) to 99% (Runyankole) it also provides language families where the technique works, and future improvements and extensions. |
Huqariq: A Multilingual Speech Corpus of Native Languages of Peru forSpeech Recognition (2022.lrec-1)
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| Challenge: | the Huqariq corpus is a multilingual collection of speech from native Peruvian languages . the project is designed to preserve endangered languages in the public domain . |
| Approach: | They propose to use crowdsourcing to collect transcribed audio from native Peruvian languages . they propose to do 220 hours of speech recognition experiments to verify quality . |
| Outcome: | The Huqariq corpus is a multilingual collection of speech from native Peruvian languages . the project is expected to reach 20 native languages out of 48 native languages by 2022 . |
Writing System and Speaker Metadata for 2,800+ Language Varieties (2022.lrec-1)
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| Challenge: | Currently, language technologies are easily available in only a small minority of the world's 7,000+ language varieties. |
| Approach: | They propose to use an open-source dataset to provide the writing system(s) for each of the 2,800+ languages used in the world today and an estimated speaker count for each. |
| Outcome: | The dataset provides the attested writing system(s) for each of these 2,800+ varieties, as well as an estimated speaker count for each variety. |
The PALMA Corpora of African Varieties of Portuguese (2022.lrec-1)
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Tjerk Hagemeijer, Amália Mendes, Rita Gonçalves, Catarina Cornejo, Raquel Madureira, Michel Généreux
| Challenge: | a corpus of urban varieties of Portuguese is being studied in Angola, Mozambique and So Tomé and Prncipe . the corpora are transcribed spoken data, complemented by metadata describing the setting of the audio recordings and sociolinguistic information about the speakers. |
| Approach: | They present three new corpora of urban varieties of Portuguese spoken in Angola, Mozambique and So Tomé and Prncipe . they provide new, contemporary data for the study of each variety and for comparative research on African, Brazilian and European varieties . |
| Outcome: | The corpora are transcribed spoken data and annotated with POS and lemma information . they are already being used for comparative research on possession and location . |
A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish Language (2022.lrec-1)
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Büşra Marşan, Oğuz K. Yıldız, Aslı Kuzgun, Neslihan Cesur, Arife B. Yenice, Ezgi Sanıyar, Oğuzhan Kuyrukçu, Bilge N. Arıcan, Olcay Taner Yıldız
| Challenge: | a team of linguists manually annotated a set of constituency trees. |
| Approach: | They propose to create the first Turkish-based dependency-to-constituency conversion algorithm using a morphologic analyser and feature-based machine learning model. |
| Outcome: | The proposed algorithm can be used to generate new constituency treebanks and training data for NLP resources like constituency parsers. |
Standard German Subtitling of Swiss German TV content: the PASSAGE Project (2022.lrec-1)
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| Challenge: | In Switzerland, two thirds of the population speak Swiss German, a primarily spoken language with no standardised written form. |
| Approach: | They propose to combine a speech recognition system with an intralingual machine translation system to automate the subtitling process. |
| Outcome: | The proposed systems improve the quality of the standardized Swiss German subtitles but are not capable of producing correct Standard German. |
A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
| Approach: | They propose to use unlabeled speech data to build multilingual ASR models that can be used for improved performance on low-resource languages. |
| Outcome: | The proposed models can be used to improve performance on low-resource languages by using unlabeled speech data. |
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish (2022.lrec-1)
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Cedric Lothritz, Bertrand Lebichot, Kevin Allix, Lisa Veiber, Tegawende Bissyande, Jacques Klein, Andrey Boytsov, Clément Lefebvre, Anne Goujon
| Challenge: | Pre-trained Language Models such as BERT are ubiquitous in NLP but are scarce for low-resource languages such as Luxembourgish. |
| Approach: | They propose a BERT model for Luxembourgish language that they use to augment pre-training datasets by partially translating text data from a closely related language. |
| Outcome: | The proposed model outperforms the baseline model and the mBERT model in Luxembourgish. |
PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection (2022.lrec-1)
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| Challenge: | In this paper, we present a dataset that is collected from users’ input in a plagiarism detection system. |
| Approach: | They propose to use a Persian paraphrase dataset that is collected from users’ input in a plagiarism detection system to improve the quality of the data. |
| Outcome: | The proposed dataset contains 2446 instances of paraphrasing. |
Introducing the Welsh Text Summarisation Dataset and Baseline Systems (2022.lrec-1)
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| Challenge: | Welsh is an official language in Wales and is spoken by an estimated 884,300 people . historically, the language has been in decline and represents a minority language in the country despite having official status . |
| Approach: | They introduce the first Welsh summarisation dataset which is available to researchers as a free resource. |
| Outcome: | The proposed summarisation system will be used as a benchmark for summarisers in other minority language contexts. |
A Systematic Approach to Derive a Refined Speech Corpus for Sinhala (2022.lrec-1)
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| Challenge: | Despite being large and generic, some languages such as Sinhala are left to underutilize the technology due to the lack of adequate resources. |
| Approach: | They propose to derive a corpus from a publicly available corpus for Sinhala speech recognition using crowdsourcing and web scraping techniques. |
| Outcome: | The proposed corpus reduces the Word-Error-Rate by 15.9%. |
IgboBERT Models: Building and Training Transformer Models for the Igbo Language (2022.lrec-1)
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| Challenge: | This paper focuses on building resources for named entity recognition for Igbo, a language mainly spoken in the south eastern part of Nigeria. |
| Approach: | They present a standard Igbo named entity recognition dataset and results from fine-tuning transformer IgbeNER models. |
| Outcome: | The proposed dataset and model improves on the IgboNER task while training and fine-tuning a transformer model with comparatively little Igbe text data. |
Latvian National Corpora Collection – Korpuss.lv (2022.lrec-1)
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Baiba Saulite, Roberts Darģis, Normunds Gruzitis, Ilze Auzina, Kristīne Levāne-Petrova, Lauma Pretkalniņa, Laura Rituma, Peteris Paikens, Arturs Znotins, Laine Strankale, Kristīne Pokratniece, Ilmārs Poikāns, Guntis Barzdins, Inguna Skadiņa, Anda Baklāne, Valdis Saulespurēns, Jānis Ziediņš
| Challenge: | Latvian National Corpora Collection (LNCC) is a multi-institutional and multi-project effort supporting the Latvian language research and language modelling. |
| Approach: | They propose to use Latvian corpora for linguistic research and language modelling. |
| Outcome: | LNCC is a multi-institutional and multi-project effort supported by the Digital Humanities and Language Technology communities in Latvia. |
Investigating the Relationship Between Romanian Financial News and Closing Prices from the Bucharest Stock Exchange (2022.lrec-1)
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| Challenge: | a new data set is used to extract information related to one company . a model that is based on previous information about transactions is not enough . |
| Approach: | They use a Romanian financial news website to extract only information related to one company . they use lexicon-based Vader tool, Financial BERT and Transformer-based models . |
| Outcome: | The proposed model shows that the extracted sentiment scores correlate with stock closing prices . the proposed model is based on data from a Romanian financial news website . |
A Free/Open-Source Morphological Analyser and Generator for Sakha (2022.lrec-1)
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| Challenge: | a morphological transducer for Sakha is being developed for use in downstream tasks . the marginalised language is subject to increasing economic and cultural peril due to climate change . |
| Approach: | They describe the development of a morphological analyser and generator for Sakha . the transducer has coverage of solidly above 90%, and high precision . it is already being used in downstream tasks such as linguistic maintenance . |
| Outcome: | The proposed morphological analyser has coverage of 90% and high precision . it is already being used in computer assisted language learning applications . |
An Expanded Finite-State Transducer for Tsuut’ina Verbs (2022.lrec-1)
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| Challenge: | a finite state transducer (FST) for the transitive verb system of Tsuut'ina (ISO 639-3: srs) is described for the Dene (Athabaskan) language spoken in Alberta, Canada. |
| Approach: | They describe the expansion of a finite state transducer (FST) for the transitive verb system of Tsuut'ina (ISO 639-3: srs) Dene languages have unique templatic morphology, in which lexical, inflectional and derivational tiers are interlaced. |
| Outcome: | The proposed model can handle a great range of common and rare argument structure types, including ditransitive and uniquely Dene object experiencer verbs. |
BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts (2022.lrec-1)
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Nauros Romim, Mosahed Ahmed, Md Saiful Islam, Arnab Sen Sharma, Hriteshwar Talukder, Mohammad Ruhul Amin
| Challenge: | Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS) due to the massive amount of user-generated content, modern machine learning techniques are feasible and cost-effective to tackle this problem. |
| Approach: | They propose to use a large manually labeled Bangla HS dataset to train generalizable models. |
| Outcome: | The proposed dataset includes more than 50,200 offensive comments crawled from online social networking sites and is at least 60% larger than existing Bangla HS datasets. |
Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction (2022.lrec-1)
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Mehdi Mirzapour, Waleed Ragheb, Mohammad Javad Saeedizade, Kevin Cousot, Helene Jacquenet, Lawrence Carbon, Mathieu Lafourcade
| Challenge: | Knowledge graphs are used for information extraction, search engines, question answering, and recommendation systems. |
| Approach: | They propose a French knowledge graph dataset based on RezoJDM. |
| Outcome: | The proposed dataset can be used in many downstream tasks for the French language . it shows that it embeds knowledge graph baselines for link prediction tasks . |
The Badalona Corpus - An Audio, Video and Neuro-Physiological Conversational Dataset (2022.lrec-1)
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Philippe Blache, Salomé Antoine, Dorina De Jong, Lena-Marie Huttner, Emilia Kerr, Thierry Legou, Eliot Maës, Clément François
| Challenge: | Using the same dyads at different periods, we can study the evolution of interlocutors’ alignment during the time. |
| Approach: | They propose to record 5 dyads with all modalities and neuro-physiological signals in a natural conversation corpus. |
| Outcome: | The proposed corpus is the first to capture all modalities and neuro-physiological signals in a natural conversation situation. |
Reading Time and Vocabulary Rating in the Japanese Language: Large-Scale Japanese Reading Time Data Collection Using Crowdsourcing (2022.lrec-1)
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| Challenge: | a study examines how differences in human vocabulary affect reading time . vocabulary size is inversely correlated to reading time due to the COVID-19 pandemic . |
| Approach: | They assume that vocabulary is random effect of research participants . they then asked participants to take part in a self-paced reading task to collect reading times . |
| Outcome: | The proposed method clarifies the tendency that vocabulary differences give to reading time. |
Thematic Fit Bits: Annotation Quality and Quantity Interplay for Event Participant Representation (2022.lrec-1)
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| Challenge: | linguistically machine-annotated large corpus requires a large burden of labeled data. |
| Approach: | They compare linguistically machine-annotated large corpus output with higher-quality taggers to model thematic fit using a high-performing neural approach. |
| Outcome: | The proposed model shows that quality improves with training size, but plateaus or declines with size. |
ChiSense-12: An English Sense-Annotated Child-Directed Speech Corpus (2022.lrec-1)
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| Challenge: | Recent evidence suggests that the speech children hear early in development is rich in word sense ambiguity, and also that children's early vocabularies are populated by ambiguous words. |
| Approach: | They sense-tagged 53 corpora of American and English speech directed to 958 target children up to 59 months of age and selected target senses that they know young children understand. |
| Outcome: | The sense-tagged corpus ChiSense-12 was used to examine the role of verb-event structure in child word sense disambiguation. |
Making People Laugh like a Pro: Analysing Humor Through Stand-Up Comedy (2022.lrec-1)
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| Challenge: | a lot of computational tools focus on standalone jokes or on occasional humorous sentences during presentations. |
| Approach: | They propose to use stand-up comedy transcripts to extract humor from a larger narrative. |
| Outcome: | The dataset, SCRIPTS, is built using stand-up comedy shows transcripts. |
Testing Focus and Non-at-issue Frameworks with a Question-under-Discussion-Annotated Corpus (2022.lrec-1)
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| Challenge: | Annotated German driving reports for question-under-discussion analysis are lacking in the literature on QUDs. |
| Approach: | They propose to annotate a German driving report corpus for QUD analysis . they show focus-related meaning aspects are essentially confirmed . |
| Outcome: | The annotated corpus of German driving reports shows that focus-related meaning aspects are essentially confirmed, indicating a sufficent accuracy of the annotations. |
Development of a Multilingual CCG Treebank via Universal Dependencies Conversion (2022.lrec-1)
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| Challenge: | Combinatory Categorial Grammar (CCG) is a lexicalized grammar formalism that can capture both syntactic and semantic information. |
| Approach: | They propose an algorithm to convert UD treebanks to CCG treebank and propose future extensions. |
| Outcome: | The proposed algorithm performs lexical, sentential, and syntactic rule coverage analysis, as well as CCG parsing experiments. |
The Automatic Extraction of Linguistic Biomarkers as a Viable Solution for the Early Diagnosis of Mental Disorders (2022.lrec-1)
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| Challenge: | Digital Linguistic Biomarkers extracted from spontaneous language productions proved to be very useful for the early detection of various mental disorders. |
| Approach: | They propose a computational pipeline for the automatic extraction of DLBs from speech samples and written texts. |
| Outcome: | The proposed pipeline is designed to extract DLBs from speech samples and written texts. |
Singlish Where Got Rules One? Constructing a Computational Grammar for Singlish (2022.lrec-1)
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| Challenge: | Singlish is a variety of English spoken in Singapore and has many non-standard features. |
| Approach: | They propose to use Singlish as a branch of English grammar to implement new rules and add new lexical types to it. |
| Outcome: | The proposed grammar is based on the existing rules and lexical types from the English resource grammar and compared with the standard English grammar. |
COSMOS: Experimental and Comparative Studies of Concept Representations in Schoolchildren (2022.lrec-1)
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| Challenge: | COSMOS is a multidisciplinary research project investigating schoolchildren’s beliefs and representations of specific concepts under control variables (age, gender, language spoken at home). |
| Approach: | They present a lexical study of seven concepts in a french school . they use a word-level lexicon to examine their representations under control variables . |
| Outcome: | The results of the study show that children's linguistic proficiency and lexical diversity increase with age, and that gender and age influence lexicality. |
Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality (2022.lrec-1)
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| Challenge: | a metaphorical discourse is more emotional and abstract than a literal one, according to a new study . a metaphorical discourse may be more abstract than literal, but it is not triggered by its emotionality or metaphoricity. |
| Approach: | They examine which features human annotators perceive as important for metaphoricity . they ask: is a metaphorical expression preceded by a more metaphorical/abstract/emotional context? |
| Outcome: | The proposed dataset shows that metaphorical discourses are more emotional and abstract than literal ones. |
Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues (2022.lrec-1)
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Sandhya Singh, Prapti Roy, Nihar Sahoo, Niteesh Mallela, Himanshu Gupta, Pushpak Bhattacharyya, Milind Savagaonkar, Nidhi Sultan, Roshni Ramnani, Anutosh Maitra, Shubhashis Sengupta
| Challenge: | Movies reflect society and also hold power to transform opinions. |
| Approach: | They propose to annotate movie scripts for identity bias using a dataset that is annotated for gender, race/ethnicity, religion, age, occupation, LGBTQ, and other . |
| Outcome: | The proposed dataset contains dialogue turns annotated for gender, race/ethnicity, religion, age, occupation, LGBTQ, and other, which contains biases like body shaming, personality bias, etc. |
VoxCommunis: A Corpus for Cross-linguistic Phonetic Analysis (2022.lrec-1)
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| Challenge: | Until recently, the movement towards large-scale cross-linguistic phonetic research has been limited. |
| Approach: | They propose to use the VoxCommunis Corpus to facilitate cross-linguistic phonetic research . corpus contains acoustic models, pronunciation lexicons, word- and phone-level alignments . |
| Outcome: | The VoxCommunis Corpus contains acoustic models, pronunciation lexicons, word- and phone-level alignments . the corpus is free to download and use under a CC0 license . |
Tracking Textual Similarities in Neo-Latin Drama Networks (2022.lrec-1)
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| Challenge: | a wide international network of exchanges between writers of different nationalities is characteristic of the early Modern Era. |
| Approach: | They describe the first experiments to track the inter-national network of text reuse within the Early Modern community of Neo-Latin humanists. |
| Outcome: | The results show that the early Modern writers and writers are part of a wider, universal, intellectual community. |
Named Entity Recognition in Estonian 19th Century Parish Court Records (2022.lrec-1)
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| Challenge: | a corpus of 19th century Parish Court records annotated for named entities (NE) in Estonian is a valuable resource for historians, linguists and the public at large. |
| Approach: | They propose to annotate a corpus of Estonian Parish Court records annotated for named entities (NE) and report on named entity recognition experiments using this corpus. |
| Outcome: | The proposed model achieves microaverage F1 score of 93.6, comparable to state-of-the-art NER performance on the contemporary Estonian. |
Investigating Independence vs. Control: Agenda-Setting in Russian News Coverage on Social Media (2022.lrec-1)
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| Challenge: | a major challenge in the media industry has always been its targeted manipulation, says a new study . agenda-setting is a well-known phenomenon in political science . authors explore the relationship between economic indicators and mentions of foreign geopolitical entities . |
| Approach: | They investigate agenda-setting in the Russian social media landscape . they explore the relation between economic indicators and mentions of foreign geopolitical entities . |
| Outcome: | The authors examine the relationship between economic indicators and mentions of foreign geopolitical entities, as well as of Russia itself. |
SLäNDa version 2.0: Improved and Extended Annotation of Narrative and Dialogue in Swedish Literature (2022.lrec-1)
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| Challenge: | SLäNDa is the only large-scale corpus of literature annotated for narrative, speech and speakers. |
| Approach: | They propose to annotate a version 2.0 of the SLäNDa corpus which includes 19 novels . they specifically examine different ways of marking speech segments such as quotation marks, dashes, or no marking at all. |
| Outcome: | The proposed corpus contains excerpts from 19 novels written between 1809 and 1940. |
AGILe: The First Lemmatizer for Ancient Greek Inscriptions (2022.lrec-1)
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| Challenge: | Existing models for ancient Greek inscriptions are not performant on epigraphic data due to language differences . a lemmatizer for ancient inscription data can enable meaningful generalizations, we show . |
| Approach: | They propose to train an automatic lemmatizer for ancient Greek inscriptions with 80% accuracy . they also show that existing models are not performant on epigraphic data . |
| Outcome: | The proposed model achieves above 80% accuracy on epigraphic data, and makes it available to the community. |
»textklang« – Towards a Multi-Modal Exploration Platform for German Poetry (2022.lrec-1)
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Nadja Schauffler, Toni Bernhart, Andre Blessing, Gunilla Eschenbach, Markus Gärtner, Kerstin Jung, Anna Kinder, Julia Koch, Sandra Richter, Gabriel Viehhauser, Ngoc Thang Vu, Lorenz Wesemann, Jonas Kuhn
| Challenge: | »textklang« aims to explore the relationship between written text and its potential and actual sonic realisation in lyric poetry . the platform will combine three modalities: the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of . musical setting of lyrical poetry. |
| Approach: | They propose to combine a multi-modal corpus of German lyric poetry from the Romantic era with a platform for systematic exploration. |
| Outcome: | The platform will combine the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of . a musical setting of lyric poetry. |
Predicting the Proficiency Level of Nonnative Hebrew Authors (2022.lrec-1)
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| Challenge: | a recent study shows that nonnative Hebrew learners can be accurately predicted from their essays . the proficiency level of nonnativ speakers is important for educational purposes . |
| Approach: | They propose to use feature-based classifiers to accurately predict the proficiency level of nonnative Hebrew learners. |
| Outcome: | The proposed classifiers can predict the proficiency level of nonnative Hebrew learners . the results are compared with human graders on a corpus of Hebrew essays . |
Trends, Limitations and Open Challenges in Automatic Readability Assessment Research (2022.lrec-1)
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| Challenge: | Readability assessment is the task of evaluating the reading difficulty of a given piece of text. |
| Approach: | They examine the common approaches used for automatic readability assessment and identify their shortcomings and some challenges for the future. |
| Outcome: | The proposed models are compared with existing models and are based on existing ones. |
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models (2022.lrec-1)
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| Challenge: | Hate speech detection models are evaluated on a held-out test data, but they are incapable of identifying weaknesses. |
| Approach: | They propose to use multilingual hate speech detection models to evaluate their performance on social media conversation. |
| Outcome: | The proposed model can detect hate speech in multiple languages using a real-world conversation on social media. |
Surfer100: Generating Surveys From Web Resources, Wikipedia-style (2022.lrec-1)
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Irene Li, Alex Fabbri, Rina Kawamura, Yixin Liu, Xiangru Tang, Jaesung Tae, Chang Shen, Sally Ma, Tomoe Mizutani, Dragomir Radev
| Challenge: | Recent work on Wikipedia page generation focuses on generating the initial leading paragraph of a page, while recent pretrained language models improve upon both extractive and abstractive steps of previous models. |
| Approach: | They propose a pretrained language model that can be combined to generate Wikipedia-style summaries with sections using 100 reference human-collected surveys. |
| Outcome: | The proposed approach is compared with existing methods with 100 human-collected surveys. |
MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed (2022.lrec-1)
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| Challenge: | Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage, and act on them. |
| Approach: | They compile and release a large-scale dataset that captures location and time for tasks and a BERT-fine-tuned model that predicts task co-occurrence. |
| Outcome: | The proposed framework captures location and time, and predicts task co-occurrence with a BERT fine-tuned model outperforming baselines. |
KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics (2022.lrec-1)
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| Challenge: | Text-to-speech (TTS) is a process of converting written text into speech. |
| Approach: | They present an expanded version of their text-to-speech corpus for Kazakh . they propose to use the corpus to build high-quality TTS systems for the language . |
| Outcome: | The constructed corpus is sufficient to build robust TTS models for Kazakh and other Turkic languages, with a subjective mean opinion score ranging from 3.6 to 4.2 for all the five speakers. |
A Graph-Based Method for Unsupervised Knowledge Discovery from Financial Texts (2022.lrec-1)
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| Challenge: | A financial analyst's work involves manually reviewing lengthy filings and financial news articles in order to extract relevant pieces of information. |
| Approach: | They propose an end-to-end, fully unsupervised method for knowledge discovery from financial texts that integrates existing resources to construct a knowledge graph of companies and related entities. |
| Outcome: | The proposed method calculates the environmental rating for companies in the S&P 500 based on company filings with the SEC and provides an independent assessment of its outputs with an independent MSCI source. |
Leveraging Mental Health Forums for User-level Depression Detection on Social Media (2022.lrec-1)
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| Challenge: | Existing methods to detect depression on social media platforms are limited due to the vastness of social media content and the lack of linguistic features. |
| Approach: | They propose to optimize the performance of user-level depression classification to lessen the burden on computational resources. |
| Outcome: | The proposed system outperforms baselines across standard metrics for the task of depression detection in text. |
Classifying Implant-Bearing Patients via their Medical Histories: a Pre-Study on Swedish EMRs with Semi-Supervised GanBERT (2022.lrec-1)
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Benjamin Danielsson, Marina Santini, Peter Lundberg, Yosef Al-Abasse, Arne Jonsson, Emma Eneling, Magnus Stridsman
| Challenge: | Identifying the presence of implants in certain patients is important for radiologists because some implants are not compatible with MRI scanning. |
| Approach: | They compare the performance of two BERT-based text classifiers whose task is to classify patients as having or not having implant(s) in their body. |
| Outcome: | The proposed classifiers outperform fully-supervised classifier models on annotated data. |
Standardisation of Dialect Comments in Social Networks in View of Sentiment Analysis : Case of Tunisian Dialect (2022.lrec-1)
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| Challenge: | Using the internet, the spoken Arabic dialect language becomes informal languages written in social media . this linguistic situation inhibits mutual understanding and makes computational approaches difficult . we present a pipeline to standardize the written texts in social networks by translating them to MSA . |
| Approach: | They propose a pipeline to standardize Arabic written texts by translating them to MSA . they use a bert-based model to select Tunisian Dialect from MSA and other dialects . |
| Outcome: | The proposed pipeline achieves the best score for the standardization of written texts in social networks . the proposed pipeline includes the translated TD and the original text written in MSA . |
EnsyNet: A Dataset for Encouragement and Sympathy Detection (2022.lrec-1)
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| Challenge: | Existing studies on emotional support detection focus on the presence or absence of emotional support, while the available datasets are limited or scarce in terms of size. |
| Approach: | They propose to use a dataset of 6,500 sentences annotated with encouragement and sympathy to train BERT-based classifiers on this dataset and apply their best BERT model to two large scale experiments. |
| Outcome: | The proposed model improves the emotional state of users while the lack of emotional support negatively impacts patients’ emotional state. |
Preliminary Results on the Evaluation of Computational Tools for the Analysis of Quechua and Aymara (2022.lrec-1)
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| Challenge: | morphological analyzers for Quechua and Aymara have been evaluated for their performance . only a minority of these languages have been provided with adequate computational resources . |
| Approach: | They evaluate existing morphological analyzers for Quechua and Aymara . they also examine how they handle other individual languages of the macrolanguage . |
| Outcome: | The proposed tools perform well in Quechua and Aymara, and they can handle other languages. |
A Tale of Two Regulatory Regimes: Creation and Analysis of a Bilingual Privacy Policy Corpus (2022.lrec-1)
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Siddhant Arora, Henry Hosseini, Christine Utz, Vinayshekhar Bannihatti Kumar, Tristan Dhellemmes, Abhilasha Ravichander, Peter Story, Jasmine Mangat, Rex Chen, Martin Degeling, Thomas Norton, Thomas Hupperich, Shomir Wilson, Norman Sadeh
| Challenge: | With the introduction of new privacy regulations, disclosures made by the same organization are not always the same in different languages. |
| Approach: | They propose a language annotation scheme to capture nuances of two new privacy regulations, namely the EU’s GDPR and California’s CCPA/CPRA. |
| Outcome: | The proposed method captures the nuances of two new privacy regulations and compares them to a corpus of 64 privacy policies in English and 91 in German with manual annotations for 8K and 19K fine-grained data practices. |
MeSHup: Corpus for Full Text Biomedical Document Indexing (2022.lrec-1)
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| Challenge: | Medical Subject Heading (MeSH) indexing is a problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms. |
| Approach: | They train an end-to-end model that combines features from documents and associated labels on MEDLINE corpus and report the new baseline. |
| Outcome: | The proposed system can be used to assign a biomedical document with the most relevant labels from an extremely large set of MeSH terms. |
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding (2022.lrec-1)
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Yanjun Gao, Dmitriy Dligach, Timothy Miller, Samuel Tesch, Ryan Laffin, Matthew M. Churpek, Majid Afshar
| Challenge: | Existing corpus and annotations focus on textual features and relation prediction, but there are no structured corpus models for clinical diagnostic thinking. |
| Approach: | They propose a hierarchical annotation schema with three stages to address clinical diagnostic thinking. |
| Outcome: | The proposed model is based on a large collection of publicly available daily progress notes. |
KC4MT: A High-Quality Corpus for Multilingual Machine Translation (2022.lrec-1)
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Vinh Van Nguyen, Ha Nguyen, Huong Thanh Le, Thai Phuong Nguyen, Tan Van Bui, Luan Nghia Pham, Anh Tuan Phan, Cong Hoang-Minh Nguyen, Viet Hong Tran, Anh Huu Tran
| Challenge: | In machine translation, Vietnamese is a low-resource language, and the quality of the training corpus is very low. |
| Approach: | They propose a method for building high-quality multilingual parallel corpus in news domain . they also publicize a corpus that includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |
| Outcome: | The proposed method improves the quality of multilingual machine translation in Vietnamese, Laos, and Khmer . the public version includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |
Developing A Multilabel Corpus for the Quality Assessment of Online Political Talk (2022.lrec-1)
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| Challenge: | a corpus of political tweets labeled for its deliberative characteristics is presented . the dataset offers a first step in building dictionaries to aid in the measurement of the Discourse Quality Index . |
| Approach: | They present a Twitter Deliberative Politics dataset that measures the quality of political tweets . they propose to use machine learning to analyze tweets and to use it to build dictionaries . |
| Outcome: | The proposed dataset is useful to linguists, political scientists, and social scientists . it offers a first step in building dictionaries for the quality assessment of political talk in english . |
BILinMID: A Spanish-English Corpus of the US Midwest (2022.lrec-1)
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| Challenge: | The Hispanic population in the United States is up to 15-20% of the nation's total population . due to its proximity to the US-Mexico border, Hispanicas have more presence in the Southwest of the country . |
| Approach: | They propose to create a text corpus of the Spanish and English spoken in the US Midwest by different types of bilinguals. |
| Outcome: | The proposed corpus contains short stories narrated in Spanish and in English by 72 speakers representing different types of bilinguals: early simultaneous bilinguals, early sequential bilinguals and late second language learners. |
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents (2022.lrec-1)
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| Challenge: | Existing methods to understand revisions have failed to provide a deeper understanding of the nature of these edits. |
| Approach: | They propose to use a Wikipedia revision history dataset to train a classifier that achieves a 90% accuracy in identifying edit intent and a distantly-supervised model that generates . |
| Outcome: | The proposed model achieves 90% accuracy in identifying edit intent and a best score of 28 ROUGE. |
CTAP for Chinese:A Linguistic Complexity Feature Automatic Calculation Platform (2022.lrec-1)
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| Challenge: | Existing tools to analyze linguistic complexity are limited and different because of different research purposes. |
| Approach: | They propose to integrate Chinese component into CTAP to analyze linguistic complexity . they propose to use 196 linguistic complex indexes to calculate linguistic characteristics . |
| Outcome: | The proposed indexes are compared with three linguistic complexity tools for Chinese . the proposed index sets include four levels of 196 linguistic complex indexe . |
A Corpus for Suggestion Mining of German Peer Feedback (2022.lrec-1)
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| Challenge: | e.g. Massive Open Online Courses (MOOCs) are increasingly important to meet the demand for feedback in large scale classes. |
| Approach: | They propose to use peer feedback to detect suggestions on how to improve the work of students in a german university course. |
| Outcome: | The proposed corpus is the first student peer feedback corpus in germany and has been labelled with a new annotation scheme. |
CLGC: A Corpus for Chinese Literary Grace Evaluation (2022.lrec-1)
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| Challenge: | Literature grace is a key element of the style and quality of articles in China. |
| Approach: | They propose to annotate a Chinese literary grace corpus with 10,000 texts and 1.85 million tokens and build a literary grace evaluation task to assess the literary grace level. |
| Outcome: | The proposed model achieves 79.71% on the weighted average F1-score. |
Anonymising the SAGT Speech Corpus and Treebank (2022.lrec-1)
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| Challenge: | Anonymisation is crucial for dataset creation, authors say . sensitive references can be direct or indirect, but data must be kept as intact . |
| Approach: | They describe the anonymisation process of a Turkish-German code-switching corpus . they used a selective pseudonymisation approach to manually identify sensitive references . |
| Outcome: | The proposed method identifies and neutralises sensitive references and replaces them with surrogate values on the treebank side. |
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction (2022.lrec-1)
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Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita, Taichi Aida, Tosho Hirasawa, Michitaka Nakatsuji, Masato Mita, Mamoru Komachi
| Challenge: | Existing studies on automatic evaluation of grammatical error correction (GEC) have shown that quality estimation models built from manual evaluation can achieve high performance in automatic evaluation in English. |
| Approach: | They used a dataset with manual evaluation to build an automatic evaluation model for Japanese GEC. |
| Outcome: | The proposed model is based on a Japanese dataset with manual evaluation and meta-evaluation. |
Enhanced Distant Supervision with State-Change Information for Relation Extraction (2022.lrec-1)
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| Challenge: | Existing methods for enhancing distant supervision with state-change information for relation extraction are limited. |
| Approach: | They propose a method for enhancing distant supervision with state-change information for relation extraction by adding temporal information to a curation dataset. |
| Outcome: | The proposed method reduces noise when used for static relation extraction and can be used to train a relation-extraction system that detects a change of state in relations. |
The Hebrew Essay Corpus (2022.lrec-1)
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| Challenge: | Annotated corpus of argumentative essays authored by prospective higher-education students . corpus includes essays by native speakers and essays by non-native speakers . |
| Approach: | They propose to use an annotated corpus of Hebrew argumentative essays to analyze non-native language use. |
| Outcome: | The proposed corpus includes essays by native speakers and essays authored by non-native speakers with three different native languages. |
Design and Evaluation of the Corpus of Everyday Japanese Conversation (2022.lrec-1)
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Hanae Koiso, Haruka Amatani, Yasuharu Den, Yuriko Iseki, Yuichi Ishimoto, Wakako Kashino, Yoshiko Kawabata, Ken’ya Nishikawa, Yayoi Tanaka, Yasuyuki Usuda, Yuka Watanabe
| Challenge: | a corpus of everyday conversations that includes video data is a new approach . a large corpus contains 200 hours of speech, 577 conversations, about 2.4 million words . |
| Approach: | They have constructed a corpus of everyday conversations that includes video data . they will publish the corpus in march 2022, and part of it in 2018 on trial basis . |
| Outcome: | The corpus of everyday Japanese conversation (CEJC) contains audio and video data . the study shows that the corpus includes a good balance of adult conversants . |
Developing Language Resources and NLP Tools for the North Korean Language (2022.lrec-1)
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| Challenge: | There are no linguistic sources for the North Korean language, resulting in a lack of a Korean language model. |
| Approach: | They present a large-scale dataset for the North Korean language and annotate a subset of this dataset for a sentiment analysis task. |
| Outcome: | The proposed model performs better than other models for masked language modeling and sentiment analysis tasks. |
Developing a Dataset of Overridden Information in Wikipedia (2022.lrec-1)
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| Challenge: | Existing methods to detect information overridden by the Web are not accurate and require a user's perspective to make the decision. |
| Approach: | They propose a task to detect whether a reference sentence has overridden a target sentence by using sentence pairs from the difference between two versions of Wikipedia. |
| Outcome: | The proposed task is formalized as a binary classification problem to determine whether a reference sentence has overridden a target sentence. |
BRATECA (Brazilian Tertiary Care Dataset): a Clinical Information Dataset for the Portuguese Language (2022.lrec-1)
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Bernardo Consoli, Henrique D. P. dos Santos, Ana Helena D. P. S. Ulbrich, Renata Vieira, Rafael H. Bordini
| Challenge: | Existing clinical datasets are in the English language and were collected in anglophone countries. |
| Approach: | They propose to use a Brazilian clinical dataset with over 2.5 million free-text clinical notes alongside data pertaining to patient information, prescription information, and exam results. |
| Outcome: | The Brazilian Clinical Dataset contains over 70,000 admissions from 10 hospitals in two Brazilian states. |
Universal Grammatical Dependencies for Portuguese with CINTIL Data, LX Processing and CLARIN support (2022.lrec-1)
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| Challenge: | a new collection of quality language resources is presented for the computational processing of the Portuguese language . the framework for the mapping between linguistic form and meaning is centered on the notion of grammatical relation . |
| Approach: | They propose a new set of quality language resources for the computational processing of the Portuguese language under the Universal Dependencies framework. |
| Outcome: | The proposed framework provides for the mapping between linguistic form and meaning representations. |
CWID-hi: A Dataset for Complex Word Identification in Hindi Text (2022.lrec-1)
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| Challenge: | Text simplification is a method for improving the accessibility of text by converting complex sentences into simple sentences. |
| Approach: | They propose to use Hindi knowledge annotators to capture the annotator’s language knowledge to build an automatic complex word classifier using a soft voting approach. |
| Outcome: | The proposed dataset shows that native and non-native annotators perceive complex words differently depending on their language knowledge. |
Automatic Classification of Russian Learner Errors (2022.lrec-1)
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| Challenge: | In the field of Grammatical Error Correction, evaluations of system output are performed overall without taking into account performance on individual error types. |
| Approach: | They propose a tool that automatically classifies errors in Russian learner texts . they compare the performance of two error correction systems with a grammatical error category tool . |
| Outcome: | The proposed tool shows that 93% of errors are judged correct or acceptable . the proposed edits are "reallistic", "realistic", and "a" ) and "are" - "were" |
Annotation of metaphorical expressions in the Basic Corpus of Polish Metaphors (2022.lrec-1)
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| Challenge: | a corpus of Polish texts annotated with metaphorical expressions is composed of two parts of comparable size, selected from two subcorpora of the Polish National Corpus . |
| Approach: | They propose to use a procedure to annotate metaphorical expressions in Polish texts using two different subcorpora of the Polish National Corpus . they propose several features to classify metaphorical Expressions identified in texts. |
| Outcome: | The proposed procedure is based on the MIPVU procedure and focuses on neologistic derivatives that have metaphorical properties. |
ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition (2022.lrec-1)
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| Challenge: | Chinese word segmentation and named entity recognition are important tasks in natural language processing. |
| Approach: | They develop a Chinese medical corpus annotated with Chinese word boundary and medical term information to address this problem. |
| Outcome: | The proposed corpus will be a valuable resource for Chinese word segmentation and named entity recognition research on the medical domain. |
Building a Synthetic Biomedical Research Article Citation Linkage Corpus (2022.lrec-1)
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| Challenge: | citations are used in scientific publications to support the presented results and to demonstrate the previous discoveries. |
| Approach: | They propose a silver standard corpus and a method to find citation linkages in biomedical research papers using deep learning. |
| Outcome: | The proposed model can locate the text spans in a reference article, given a citing statement, based on semantic similarity. |
Dataset Construction for Scientific-Document Writing Support by Extracting Related Work Section and Citations from PDF Papers (2022.lrec-1)
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| Challenge: | To augment datasets used for scientific-document writing support research, we extract texts from “Related Work” sections and citation information in PDF-formatted papers published in English. |
| Approach: | They propose to extract text from “Related Work” sections and citation information from PDF-formatted papers published in English. |
| Outcome: | The proposed dataset is based on a previously constructed dataset using only Tex papers and is compared with the existing one. |
RuPAWS: A Russian Adversarial Dataset for Paraphrase Identification (2022.lrec-1)
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Nikita Martynov, Irina Krotova, Varvara Logacheva, Alexander Panchenko, Olga Kozlova, Nikita Semenov
| Challenge: | Existing datasets for paraphrase identification lack challenging sentence pairs with high word overlap. |
| Approach: | They propose to use a dataset for Russian paraphrase detection that includes examples from PAWS translated to the Russian language and manually annotated by native speakers. |
| Outcome: | The proposed model performs well on both datasets while maintaining accuracy on the ParaPhraser benchmark. |
Atril: an XML Visualization System for Corpus Texts (2022.lrec-1)
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| Challenge: | Existing corpus analysis tools such as SketchEngine, TEITOK and CQPweb do not provide a visualization system for corpus texts with heavy structural annotation. |
| Approach: | They propose to provide researchers with a web-based environment that allows for an easily customizable visualization of corpus texts with heavy structural annotation. |
| Outcome: | The proposed system is designed for the corpus de Audiências project in Coimbra, Brazil. |
MASALA: Modelling and Analysing the Semantics of Adpositions in Linguistic Annotation of Hindi (2022.lrec-1)
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| Challenge: | Existing work on SNACS annotation for a variety of typologically diverse languages focuses on semantic role labelling and upstream applications in related languages. |
| Approach: | They propose to use the multilingual SNACS annotation scheme to attempt automatic labelling of SNAC supersenses in Hindi. |
| Outcome: | The proposed method is competitive with previous work on English and Gujarati. |
Universal Dependencies for Punjabi (2022.lrec-1)
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| Challenge: | UD is a community project that maintains a standard scheme for the annotation of grammar in a cross-lingually consistent manner. |
| Approach: | They propose a Universal Dependencies treebank for Punjabi written in the Gurmukhi script and discuss corpus design and linguistic phenomena encountered in annotation. |
| Outcome: | The proposed treebank covers a variety of genres and has been annotated for POS tags, dependency relations, and graph-based Enhanced Dependencies. |
TeSum: Human-Generated Abstractive Summarization Corpus for Telugu (2022.lrec-1)
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| Challenge: | a number of recent datasets for summarisation, scraped the web-content relying on the assumption that summary is made available with the article by the publishers. |
| Approach: | They propose a pipeline that crowd-sources summarization data and then aggressively filters the content via: automatic and partial expert evaluation. |
| Outcome: | The proposed pipeline can be applied to scraped datasets to extract better quality articles-summaries pairs. |
A Corpus of Simulated Counselling Sessions with Dialog Act Annotation (2022.lrec-1)
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| Challenge: | Consisting of 152K Chinese characters, the corpus labels the dialog act of both client and counsellor utterances and segments each dialog into stages. |
| Approach: | They propose to use a corpus of 152K Chinese characters to model counselling conversations in Cantonese. |
| Outcome: | The proposed corpus can be used for counselling chatbot development and to better understand the characteristics of successful counselling. |
Interactive Evaluation of Dialog Track at DSTC9 (2022.lrec-1)
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| Challenge: | Currently, dialog research is focused on static data, which neglects multiple important properties of dialog, such as consistency, topic depth, adaptation, error recovery and user-centric development. |
| Approach: | They propose to use static dialogs to build strong response generation models and extend them to back-and-forth interactions with real users. |
| Outcome: | The proposed model trains a larger evolved Transformer model on social media data and attains strong performance in interactive settings. |
HADREB: Human Appraisals and (English) Descriptions of Robot Emotional Behaviors (2022.lrec-1)
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| Challenge: | HADREB datasets explore how humans perceive robot emotional states . emotions are a fundamental part of the human language system and are used as scaffolding for language learning . |
| Approach: | They present a dataset of human appraisals and English descriptions of robot emotional behaviors . they use mistyrobotics mist and digital dream labs cozmo robots to analyze the data . |
| Outcome: | The proposed dataset examines how humans perceive robot emotional states and how they relate to human language. |
Dialogue Collection for Recording the Process of Building Common Ground in a Collaborative Task (2022.lrec-1)
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| Challenge: | Existing studies on the process of building common ground have not been well conducted. |
| Approach: | They propose a method for recording the process of building common ground through a dialogue by using the intermediate result of a task. |
| Outcome: | The proposed method can record the building common ground process by using the intermediate result of a task and can be estimated quite accurately. |
Collection and Analysis of Travel Agency Task Dialogues with Age-Diverse Speakers (2022.lrec-1)
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| Challenge: | Using deep neural networks, task-oriented dialogue systems can be used to generate an appropriate response to users' inputs. |
| Approach: | They collected a multimodal dialogue corpus with a wide range of speaker ages and set up a dialogue task based on travel . results suggest adult speakers have more independent opinions, older speakers express opinions more frequently compared with other age groups, and operators expressed a smile more frequently to minor speakers. |
| Outcome: | The results show that adult speakers have more independent opinions, the older speakers express their opinions more frequently compared with other age groups, and the operators expressed a smile more frequently to the minor speakers. |
Strategy-level Entrainment of Dialogue System Users in a Creative Visual Reference Resolution Task (2022.lrec-1)
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| Challenge: | entrainment is a phenomenon in which interlocutors start speaking more similarly to each other. |
| Approach: | They propose to use crowd-sourced data to study entrainment of users playing a creative reference resolution game with an autonomous dialogue system. |
| Outcome: | The proposed system adapts the user's descriptive strategy to one that is simpler to parse for the natural language understanding unit without impinging on their creativity. |
MMChat: Multi-Modal Chat Dataset on Social Media (2022.lrec-1)
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| Challenge: | Incorporating multi-modal contexts in conversation is important for developing engaging dialogue systems. |
| Approach: | They propose a large scale Chinese multi-modal dialogue corpus that contains image-grounded dialogues from real conversations on social media. |
| Outcome: | The proposed model can handle sparsity issues in dialogue generation tasks by incorporating image features. |
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)
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Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He
| Challenge: | Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. |
| Approach: | They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
| Outcome: | The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
SHONGLAP: A Large Bengali Open-Domain Dialogue Corpus (2022.lrec-1)
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| Challenge: | Existing open-domain dialogue systems suffer from data scarcity due to unavailability of high-quality datasets for low-resource languages like Bengali. |
| Approach: | They propose to prepare large-scale open-domain dialogue datasets from podcasts and talk-shows and label them based on weak-supervision techniques. |
| Outcome: | The proposed corpus improves performance of large language models in case of downstream classification tasks during fine-tuning. |
A Comparison of Praising Skills in Face-to-Face and Remote Dialogues (2022.lrec-1)
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Toshiki Onishi, Asahi Ogushi, Yohei Tahara, Ryo Ishii, Atsushi Fukayama, Takao Nakamura, Akihiro Miyata
| Challenge: | praising behavior is considered to be an important method of communication in daily life and social activities. |
| Approach: | They develop corpuses for face-to-face and remote two-party dialogues with ratings of praising skills and evaluate praising skills. |
| Outcome: | The proposed corpus clarifies how to use verbal and nonverbal behaviors for praise. |
Comparing Approaches to Language Understanding for Human-Robot Dialogue: An Error Taxonomy and Analysis (2022.lrec-1)
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| Challenge: | Existing approaches to language understanding for human-robot interaction are limited by domain-specific grammars and domain-level inputs. |
| Approach: | They compare a relevance-based classifier with a GPT-2 model and compare their results . they find that the relevance- and GPT-based models make different errors . |
| Outcome: | The proposed model outperforms the existing model with 2000 examples as training data. |
SPORTSINTERVIEW: A Large-Scale Sports Interview Benchmark for Entity-centric Dialogues (2022.lrec-1)
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| Challenge: | Existing knowledge grounded dialogue datasets only contain external knowledge from one dimension, which limits the diversity of knowledge sources and may contain unwanted bias. |
| Approach: | They propose to use two types of external knowledge sources as knowledge grounding in an interview dataset to model human dialogues. |
| Outcome: | The proposed dataset contains 150K interviews and 34K interviewees . it is larger in size and has more than one dimension of external knowledge linking . however, the performance of the proposed models is far from humans . |
EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues (2022.lrec-1)
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| Challenge: | Existing datasets for emotion recognition in dialogues are in English . existing datasets are limited to a few languages like Hindi . |
| Approach: | They propose a large conversational dataset in Hindi for multi-label emotion and intensity recognition in conversations . they use a Wizard-of-Oz manner to annotate dialogues with 16 emotion labels . |
| Outcome: | The proposed dataset contains 1,814 dialogues with 44,247 utterances in Hindi . it is based on a Wizard-of-Oz manner and can detect emotions in conversation . |
The Project Dialogism Novel Corpus: A Dataset for Quotation Attribution in Literary Texts (2022.lrec-1)
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| Challenge: | Annotated quotations are used to model attribution and coreference of literary texts . authors present project Dialogism Novel Corpus, or PDNC, for 22 novels . |
| Approach: | They present an annotated dataset of quotations for English literary texts . they use natural language processing to model aspects of narrative, events, and characters . |
| Outcome: | The project Dialogism Novel Corpus contains annotations for 35,978 quotations across 22 novels . authors show that NLP can be used to model aspects of narrative, events, and characters . |
Who’s in, who’s out? Predicting the Inclusiveness or Exclusiveness of Personal Pronouns in Parliamentary Debates (2022.lrec-1)
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| Challenge: | clusivity properties of personal pronouns are captured in context, including/excluding audience and/or non-speech act participants. |
| Approach: | They propose a compositional annotation scheme to capture the clusivity properties of personal pronouns in context, which is their ability to construct and manage in-groups and out-group. |
| Outcome: | The proposed schema achieves high inter-annotator agreement with a Cohen’s in the range of 89.7-93.2 and a percentage agreement of > 96%. |
A Language Modelling Approach to Quality Assessment of OCR’ed Historical Text (2022.lrec-1)
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| Challenge: | a language model-based approach is used to score the quality of OCR transcriptions in the British Library Newspapers corpus . a corpus of genre-adjacent texts captures the common and legal parlance of nineteenth-century London . |
| Approach: | They propose a language model-based approach to score the quality of OCR transcriptions in the British Library Newspapers corpus parts 1 and 2 . they aim to link newspapers of crime in nineteenth-century London to the Digital Panopticon . |
| Outcome: | The proposed approach is based on the Proceedings of the Old Bailey Online corpus, which captures the common and legal parlance of nineteenth-century London. |
Identifying Copied Fragments in a 18th Century Dutch Chronicle (2022.lrec-1)
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| Challenge: | We use stylometric methods to identify which fragments of the manuscript represent the author’s own original work and which show signs of external source use. |
| Approach: | They apply computational stylometric techniques to an 18th century Dutch chronicle to determine which fragments represent the author's original work and which show signs of external source use. |
| Outcome: | The proposed method is effective for authorship verification of the Dutch chronicle, but less effective when personal writing style is masked by author independent styles or when applied to paraphrased text. |
A Study of Distant Viewing of ukiyo-e prints (2022.lrec-1)
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| Challenge: | ukiyo-e landscape prints feature diverse place-names, both man-made and natural formations. |
| Approach: | They propose to use a Japanese BERT-based Name Entity Recogniser to analyze a visual dataset that is hosted by the Art Research Center at the Ritsumeikan University, Kyoto. |
| Outcome: | The proposed approach improves the work by fine-tuning and applying a Japanese BERT-based Name Entity Recogniser to a visual dataset hosted by the Art Research Center at the Ritsumeikan University, Kyoto. |
CCTAA: A Reproducible Corpus for Chinese Authorship Attribution Research (2022.lrec-1)
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| Challenge: | a lack of standard, reproducible testbeds for authorship attribution in Chinese language documents impedes progress. |
| Approach: | They propose a Chinese Cross-Topic Authorship Attribution corpus for Chinese prose . it is the first standard testbed for authorship attribution on contemporary Chinese pros. |
| Outcome: | The proposed testbed is the first standard testbed for authorship attribution on Chinese prose. |
An automatic model and Gold Standard for translation alignment of Ancient Greek (2022.lrec-1)
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Tariq Yousef, Chiara Palladino, Farnoosh Shamsian, Anise d’Orange Ferreira, Michel Ferreira dos Reis
| Challenge: | Using a manual annotation tool, we evaluated the performance of various automatic translation alignment models for Ancient Greek. |
| Approach: | They propose a fine-tuning strategy that employs unsupervised training with mono- and bilingual texts and supervised training using manually aligned sentences. |
| Outcome: | The proposed model outperforms the standard on language pairs that were not part of the training data. |
Rhetorical Structure Approach for Online Deception Detection: A Survey (2022.lrec-1)
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| Challenge: | Existing studies on how people use language to inform and misinform are relevant. |
| Approach: | They analyze how discourse structure is applied to fake news detection on the web and social media. |
| Outcome: | The proposed framework is applied to fake news and fake reviews detection on the web and social media. |
TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation (2022.lrec-1)
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Shoichi Naito, Shintaro Sawada, Chihiro Nakagawa, Naoya Inoue, Kenshi Yamaguchi, Iori Shimizu, Farjana Sultana Mim, Keshav Singh, Kentaro Inui
| Challenge: | Argumentation and debate are effective tools for developing critical thinking skills, but it requires a lot of time and effort. |
| Approach: | They propose to automate the process of giving diagnostic comments to students . they define criteria for a template set that can be used to evaluate the model . |
| Outcome: | The proposed model can be used to evaluate arguments and evaluate them in real time. |
Towards Speaker Verification for Crowdsourced Speech Collections (2022.lrec-1)
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| Challenge: | Existing methods to detect low quality work do not address the correctness of the data. |
| Approach: | They propose an unsupervised method for measuring speaker metadata plausibility of a collection, i.e., evaluating the match (or lack thereof) between contributors and speakers. |
| Outcome: | The proposed method shows high precision in automatically classifying contributor alignment (>0.94). |
Align-smatch: A Novel Evaluation Method for Chinese Abstract Meaning Representation Parsing based on Alignment of Concept and Relation (2022.lrec-1)
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| Challenge: | Abstract Meaning Representation abstracts the meaning of sentences into a single-rooted, acyclic and directed graph. |
| Approach: | They propose to use a metric to evaluate concept alignment and relation alignment to improve Chinese AMR parsing evaluation methods. |
| Outcome: | The proposed method is more robust and compatible with concept alignment and relation alignment and more robust in evaluating arcs. |
Dynamic Human Evaluation for Relative Model Comparisons (2022.lrec-1)
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| Challenge: | Automated metrics have reported flaws when applied to measure quality aspects of generated text and have been shown to correlate poorly with human judgements. |
| Approach: | They propose an agent-based framework to measure the required number of human annotations when evaluating generated outputs in relative comparison settings. |
| Outcome: | The proposed model can be compared with a crowdsourced case study and a simulation with simulated human judgements. |
Please, Don’t Forget the Difference and the Confidence Interval when Seeking for the State-of-the-Art Status (2022.lrec-1)
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| Challenge: | comparing NLP systems by performance has become an essential question . comparing systems by performing performance criterion is criticized for allowing chance to determine superiority . |
| Approach: | They propose to use bootstrap confidence intervals instead of state-of-the-art status and statistical significance testing to compare NLP system performance. |
| Outcome: | The bootstrap confidence intervals are used to compare NLP system performance . the bootstrap test is more accurate than state-of-the-art status and statistical significance testing . |
PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English (2022.lrec-1)
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| Challenge: | Existing PCR systems are not reliable in real applications due to weak semantic meanings of pronouns. |
| Approach: | They propose a benchmark and toolbox that evaluates performance of PCR systems from different perspectives. |
| Outcome: | The proposed benchmark and toolbox evaluates the performance of PCR systems from different perspectives. |
Estimating Confidence of Predictions of Individual Classifiers and TheirEnsembles for the Genre Classification Task (2022.lrec-1)
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| Challenge: | Genre identification is a kind of non-topic text classification. genre is defined as a functional space. |
| Approach: | They propose to use SOTA to identify genres in non-topic texts . genres are functional and cannot be expressed just by some keywords . |
| Outcome: | The proposed models show that they perform better than their individual models in large datasets. |
What do we really know about State of the Art NER? (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is a well researched task and widely used in real world NLP scenarios. |
| Approach: | They perform a broad evaluation of Named Entity Recognition using a popular dataset that takes into consideration various text genres and sources constituting the dataset at hand. |
| Outcome: | The proposed models perform on a popular dataset and generate six new adversarial test sets through small perturbations in the original test set, replacing select entities while retaining the context. |
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction (2022.lrec-1)
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| Challenge: | Prior work has shown that QE models of grammatical error correction are biased toward data by learners with relatively high proficiency levels. |
| Approach: | They investigated whether learners' proficiency affects supervised quality estimation models of grammatical error correction (GEC) . they created a QE dataset that includes multiple proficiency levels and explored the necessity of performing proficiency-wise evaluation for QE of GEC. |
| Outcome: | The proposed model is based on multiple proficiency levels and can be performed in real-world scenarios. |
Evaluation of Off-the-shelf Speech Recognizers on Different Accents in a Dialogue Domain (2022.lrec-1)
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| Challenge: | Existing automatic speech recognition systems for non-American accents have a much higher error rate than for general american accents. |
| Approach: | They evaluate automatic speech recognition systems on agent-directed speech . they find that the performance is worse for non-American accents than for General American . |
| Outcome: | The ASR systems perform worse for non-American accents than for General American accents . the results suggest that training on non-native English speakers is needed to narrow the performance gap. |
Sentence Pair Embeddings Based Evaluation Metric for Abstractive and Extractive Summarization (2022.lrec-1)
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| Challenge: | Existing evaluation metrics, such as ROUGE and BLEU, rely on exact word matching and fail to capture semantic similarity. |
| Approach: | They propose to use contextualized word or sentence embeddings to capture semantic similarity between sentences to evaluate text summarization methods. |
| Outcome: | The proposed evaluation metric shows that it performs faster than the current state-of-the-art on the SummEval dataset. |
On “Human Parity” and “Super Human Performance” in Machine Translation Evaluation (2022.lrec-1)
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| Challenge: | In this paper, we reassess claims of human parity and super human performance in machine translation. |
| Approach: | They reassess claims of human parity and super human performance in machine translation . they argue that human translation involves much more than what is embedded in automatic systems . |
| Outcome: | The proposed results show that human translation involves much more than what is embedded in automatic systems. |
Evaluation Benchmarks for Spanish Sentence Representations (2022.lrec-1)
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Vladimir Araujo, Andrés Carvallo, Souvik Kundu, José Cañete, Marcelo Mendoza, Robert E. Mercer, Felipe Bravo-Marquez, Marie-Francine Moens, Alvaro Soto
| Challenge: | Existing and newly constructed datasets address different tasks from various domains. |
| Approach: | They propose to use Spanish SentEval and Spanish DiscoEval to evaluate stand-alone and discourse-aware sentence representations. |
| Outcome: | The proposed benchmarks evaluate the capabilities of stand-alone and discourse-aware sentence representations in Spanish and show that they are more robust and comparable than previous benchmarks. |
UMUTextStats: A linguistic feature extraction tool for Spanish (2022.lrec-1)
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| Challenge: | Feature Engineering is the application of domain knowledge to build efficient machine learning models. |
| Approach: | a team of researchers has developed a linguistic extraction tool for Spanish . the tool uses linguistic features and embeddings to build efficient machine learning models . |
| Outcome: | UMUTextStats is a linguistic extraction tool for Spanish . it has been validated in infodemiology, hate-speech detection, author profiling, authorship verification, humour or irony detection, among others. |
Problem-solving Recognition in Scientific Text (2022.lrec-1)
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| Challenge: | Existing work on problem-solving is not computational, is not adapted to scientific text, or has been narrow in scope. |
| Approach: | They propose an algorithm which can generate virtual instructors from automatically annotated texts. |
| Outcome: | The proposed algorithm can recognise problem-solving expressions in scientific texts with high accuracy. |
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method (2022.lrec-1)
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| Challenge: | Multiple-choice question answering (MCQA) requires a model to understand natural languages and understand textual representations. |
| Approach: | They propose a model that uses human reading comprehension attention to increase accuracy for machine reading comprehension. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Semeval-2018 Task 11 dataset and on the DREAM dataset. |
HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings (2022.lrec-1)
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| Challenge: | Existing methods for hypernym detection rely on word distribution. |
| Approach: | They propose a model to learn box embeddings for hypernym discovery by using a dataset . they compare the performance of their model on medical and music domains . |
| Outcome: | The proposed model outperforms existing methods on most evaluation metrics on medical and music domains. |
Extracting Space Situational Awareness Events from News Text (2022.lrec-1)
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Zhengnan Xie, Alice Saebom Kwak, Enfa George, Laura W. Dozal, Hoang Van, Moriba Jah, Roberto Furfaro, Peter Jansen
| Challenge: | Space situational awareness is the decisionmaking knowledge required to predict, avoid, operate through, or recover from the loss, disruption, or degradation of space services, capabilities, or activities. |
| Approach: | They construct a corpus of 48.5k news articles spanning all known active satellites between 2009 and 2020 that are annotated by humans with 15.9k labels for event slots. |
| Outcome: | The proposed system achieves an overall F1 between 53 and 91 per slot for event extraction in this low-resource, high-impact domain. |
PerCQA: Persian Community Question Answering Dataset (2022.lrec-1)
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| Challenge: | Community Question Answering (CQA) forums provide answers to many real-life questions. |
| Approach: | They propose to make Persian dataset PerCQA public to encourage more research in Persian CQA. |
| Outcome: | The proposed dataset contains 989 questions and 21,915 annotated answers from the most well-known Persian forum. |
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns (2022.lrec-1)
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| Challenge: | a Python library is available for extracting patterns from textual data. |
| Approach: | They propose a Python library for extracting patterns from textual data . it integrates a public implementation of the existing GrASP algorithm . |
| Outcome: | The proposed library integrates a public implementation of the existing GrASP algorithm. |
Recurrent Neural Networks with Mixed Hierarchical Structures and EM Algorithm for Natural Language Processing (2022.lrec-1)
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| Challenge: | A variety of hierarchical RNN models have been proposed to incorporate hierarchically-based hierarchic information in modeling languages in the literature. |
| Approach: | They propose a latent indicator layer approach to identify and learn hierarchical information and develop an EM algorithm to handle the latent indicators layer in training. |
| Outcome: | The proposed approach outperforms other RNN-based models in document classification tasks. |
Korean-Specific Dataset for Table Question Answering (2022.lrec-1)
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| Challenge: | Existing question answering systems mainly focus on text data, but few Korean datasets exist . a dataset for table question answering is written in English, but it lacks Korean-specific datasets . |
| Approach: | They construct Korean-specific datasets for table question answering using crowd-sourced workers . they then fine-tune the model with these datasets and report the evaluation results . |
| Outcome: | The proposed model is based on Korean datasets and is publicly available . the model is evaluated against other datasets from Korean question answering systems . |
GerCCT: An Annotated Corpus for Mining Arguments in German Tweets on Climate Change (2022.lrec-1)
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| Challenge: | Recent work on annotated resources focused on single argument components, i.e., claim or evidence. |
| Approach: | They propose to annotate a German climate change argument corpus using sarcasm and toxic language to facilitate filtering out non-argumentative content. |
| Outcome: | The proposed corpus is the first to be annotated for argumentation, sarcasm and toxic language. |
Budget Argument Mining Dataset Using Japanese Minutes from the National Diet and Local Assemblies (2022.lrec-1)
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| Challenge: | Budget argument mining attempts to identify argumentative components related to a budget item . argument mining is a subtask of QA Lab-PoliInfo-3 in NTCIR-16 . |
| Approach: | They propose a dataset to link budget information to minutes and budget items . they describe the construction of the dataset and the annotation procedure . |
| Outcome: | The proposed dataset is based on a QA Lab-PoliInfo-3 subtask . it identifies argumentative components related to a budget item and classifies them . |
Context-based Virtual Adversarial Training for Text Classification with Noisy Labels (2022.lrec-1)
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| Challenge: | Recent studies show that deep neural networks can memorize noisy labels with limited training time. |
| Approach: | They propose a virtual adversarial training method to prevent a classifier from overfitting to noisy labels. |
| Outcome: | The proposed method performs the adversarial training in the context rather than the inputs. |
FinMath: Injecting a Tree-structured Solver for Question Answering over Financial Reports (2022.lrec-1)
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| Challenge: | Existing models for answering complex questions require multiple-step numerical reasoning. |
| Approach: | They propose a framework that injects a tree-structured neural model into a model to perform multi-step numerical reasoning. |
| Outcome: | The proposed framework improves the previous best model by 8.5% absolute for Exact Match (EM) score and 6.1% absolute for numeracy-focused F1 score. |
HeadlineCause: A Dataset of News Headlines for Detecting Causalities (2022.lrec-1)
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| Challenge: | Existing datasets focus on commonsense causal reasoning or explicit causal relations . authors present dataset for detecting implicit causal relations between news headlines . |
| Approach: | They present a dataset for detecting implicit causal relations between news headlines . they use 5000 headline pairs from English news and 9000 from Russian news . |
| Outcome: | The proposed dataset shows that it is valid and can be used to predict implicit causal relations between headline pairs. |
Incorporating Zoning Information into Argument Mining from Biomedical Literature (2022.lrec-1)
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| Challenge: | Argumentative zoning is a text zonation scheme that is used to segment text into zones that serve distinct functions. |
| Approach: | They propose to use zoning information to incorporate into argument mining tasks . they add zonation labels predicted by an off-the-shelf model to the beginning of each sentence . |
| Outcome: | The proposed models improve argument mining models without additional annotation cost. |
MAKED: Multi-lingual Automatic Keyword Extraction Dataset (2022.lrec-1)
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| Challenge: | a large dataset of news articles spanning 20 languages is lacking for keyword extraction. |
| Approach: | They propose a large-scale multi-lingual keyword extraction dataset for 11 of 20 languages . authors believe it will help advance the field of automatic keyword extraction . |
| Outcome: | The proposed dataset is the first for 11 of 20 languages and is based on 540K+ news articles from the BBC News network. |
From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction (2022.lrec-1)
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Robert Vacareanu, Marco A. Valenzuela-Escárcega, George Caique Gouveia Barbosa, Rebecca Sharp, Gustave Hahn-Powell, Mihai Surdeanu
| Challenge: | a "deep learning tsunami" has brought tremendous improvements in performance to most NLP applications. |
| Approach: | They propose a method for rule synthesis from examples that combines the advantages of deep learning and rule-based methods. |
| Outcome: | The proposed method achieves state-of-the-art on 1-shot task and competitive performance in 5-shot scenario. |
Enhancing Relation Extraction via Adversarial Multi-task Learning (2022.lrec-1)
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| Challenge: | Existing studies have focused on re-modeling the given NEs and thus lead to inferior results when NE is sometimes ambiguous. |
| Approach: | They propose a relation extraction model with two training stages that uses adversarial multi-task learning to recover the given NEs. |
| Outcome: | The proposed model improves on two English benchmark datasets and shows state-of-the-art performance. |
Query Obfuscation by Semantic Decomposition (2022.lrec-1)
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| Challenge: | Existing methods to protect privacy of search engine users by decomposing queries using semantically related and unrelated distractor terms are not available to most web search engines. |
| Approach: | They propose a method to protect the privacy of search engine users by decomposing queries using semantically related and unrelated distractor terms. |
| Outcome: | The proposed method can reconstruct search results relevant to the original query term without compromising the privacy of the search engine users. |
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks (2022.lrec-1)
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| Challenge: | Approximately 1 in 6 Americans (or 48 million people) are sickened by foodborne illness each year. |
| Approach: | They propose to use Twitter's TWEET-FID dataset to create annotated datasets for multiple foodborne illness incident detection tasks. |
| Outcome: | The proposed dataset is the first publicly available annotated dataset for multiple foodborne illness incident detection tasks. |
Named Entity Recognition to Detect Criminal Texts on the Web (2022.lrec-1)
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| Challenge: | Using named-entity extraction techniques, a toolkit that extracts information related to criminal activity from Polish Internet is evaluated on 6240 manually annotated text fragments. |
| Approach: | They propose a toolkit that uses named-entity extraction techniques to identify information related to criminal activity in texts from the Polish Internet. |
| Outcome: | The proposed method is feasible and has potential value for real-life applications in the daily work of border guards. |
Task-Driven and Experience-Based Question Answering Corpus for In-Home Robot Application in the House3D Virtual Environment (2022.lrec-1)
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| Challenge: | Question answering is an important part of natural language processing (NLP) |
| Approach: | They propose to use TEQA to investigate the ability of agent task experience understanding for the long-term household task. |
| Outcome: | The proposed corpus aims to investigate the ability of task experience understanding of agents for the daily question answering scenario on the ALFRED dataset. |
ELRC Action: Covering Confidentiality, Correctness and Cross-linguality (2022.lrec-1)
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Tom Vanallemeersch, Arne Defauw, Sara Szoc, Alina Kramchaninova, Joachim Van den Bogaert, Andrea Lösch
| Challenge: | ELRC aims to reduce language barriers by assessing language technology (LT) specifications . automated anonymisation and multilingual fake news processing are two of the most extensive LT assessments . |
| Approach: | They describe language technology (LT) assessments carried out by the European Commission . they zoom in on two of the most extensive assessments, namely automated anonymisation and multilingual fake news processing. |
| Outcome: | The language technology (LT) assessments carried out by the European Commission are detailed in this paper . they include a consultation round with stakeholders from public organisations, academia and industry . the ELRC action aims to create proof-of-concept environments integrating relevant tools and services . |
RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports (2022.lrec-1)
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| Challenge: | Question answering (QA) is an intuitive means to query text data. |
| Approach: | They propose a radiology question-answer-evidence-pair dataset with 3074 questions posed against radiology reports and annotated with their corresponding answer spans by physicians. |
| Outcome: | The proposed dataset has 3074 questions posed against radiology reports and annotated with their corresponding answer spans by physicians. |
Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain (2022.lrec-1)
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Ankush Agarwal, Raj Gite, Shreya Laddha, Pushpak Bhattacharyya, Satyanarayan Kar, Asif Ekbal, Prabhjit Thind, Rajesh Zele, Ravi Shankar
| Challenge: | Existing Question Answering systems for commercial aviation use a large number of documents . a Knowledge Graph (KG) guided Deep Learning (DL) based system can be used to query the documents based on accident reports . |
| Approach: | They propose a Knowledge Graph (KG) guided Deep Learning (DL) based Question Answering system to cater to these requirements. |
| Outcome: | The proposed system achieves 7% and 40% increase in accuracy over existing systems. |
A Bayesian Topic Model for Human-Evaluated Interpretability (2022.lrec-1)
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| Challenge: | Topic modeling is an effective way to analyze unstructured textual data. |
| Approach: | They propose to combine nonparametric and weakly-supervised topic models to produce interpretable topics. |
| Outcome: | The proposed model outperforms weakly-supervised models in the field of topic modeling. |
A Large Interlinked Knowledge Graph of the Italian Cultural Heritage (2022.lrec-1)
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| Challenge: | Existing efforts to create knowledge bases are limited to relatively small resources, such as entities from libraries, archeological sites and museums. |
| Approach: | They propose to create a large knowledge graph linking Italian cultural heritage entities with concepts defined on well-known knowledge bases. |
| Outcome: | The proposed graph shows that the Italian cultural heritage entities are interlinked with concepts defined on well-known knowledge bases. |
Training on Lexical Resources (2022.lrec-1)
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| Challenge: | In this paper, we fine-tune pretrained deep nets such as BERT and ERNIE . at inference time, these nets can be used to distinguish synonyms from antonyms . |
| Approach: | They propose to use lexical resources to fine-tune pretrained deep nets such as BERT and ERNIE to distinguish synonyms from antonyms. |
| Outcome: | The proposed method can be applied to multiword expressions, out of vocabulary words, morphological variants and more. |
Challenging the Assumption of Structure-based embeddings in Few- and Zero-shot Knowledge Graph Completion (2022.lrec-1)
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| Challenge: | Existing work on Knowledge Graph completion only uses textual descriptive data . knowledge graphs are incomplete because not every relation has been observed at the time of their construction. |
| Approach: | They propose to use textual descriptive data to enrich benchmark data sets for Few- and Zero-shot Knowledge Graph completion tasks. |
| Outcome: | The proposed task improves for Few- and Zero-shot scenarios with up to twofold increase in the Zero- shot setting. |
Open Terminology Management and Sharing Toolkit for Federation of Terminology Databases (2022.lrec-1)
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| Challenge: | Terminology is also needed in AI applications such as machine translation, speech recognition, information extraction, and other natural language processing tools. |
| Approach: | They propose a terminology management solution that facilitates standards-based sharing and management of terminology resources by providing the EuroTermBank Toolkit. |
| Outcome: | The EuroTermBank Toolkit facilitates standards-based sharing and management of terminology resources by participating in federated databases. |
RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification (2022.lrec-1)
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| Challenge: | Existing knowledge resources for sentiment analysis (SA) tasks are either large, common-sense knowledge graphs (KGs) that cover a limited amount of polarities/emotions or they are smaller in size (e.g. lexicons) . however, these resources are limited by the low coverage of e.t. and scalability. |
| Approach: | They propose a new directed KG called ‘RELATE’ which incorporates the benefit of semantics without relying on costly human annotation. |
| Outcome: | The proposed KG overcomes low coverage of emotions and scalability issues . it is the first KG of its size to cover Ekman’s six basic emotions that are directed towards entities. |
Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR (2022.lrec-1)
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| Challenge: | despite natural language variation, automatic speech recognition systems perform worse on non-standardised and marginalised language varieties. |
| Approach: | They propose a re-framing of language resources as (public) infrastructure for speech communities . authors propose rethinking of algorithms to address the origins and harms of bias . |
| Outcome: | The proposed approach aims to understand the origins and harms of algorithmic bias and how it can be mitigated. |
Masader: Metadata Sourcing for Arabic Text and Speech Data Resources (2022.lrec-1)
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| Challenge: | Currently, there is no online catalogue for Arabic datasets with annotated attributes . this paper aims to identify the publicly available Arabic dataset and provide a catalogue of them to researchers. |
| Approach: | They propose to create the largest public catalogue for Arabic NLP datasets with 25 attributes and a metadata annotation strategy that could be extended to other languages. |
| Outcome: | The proposed approach could be extended to other languages and regions. |
Linghub2: Language Resource Discovery Tool for Language Technologies (2022.lrec-1)
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| Challenge: | Linghub is a platform for language resources that can be used to find and retrieve data . the platform is based on a popular open source data management system, DSpace . |
| Approach: | This work describes a rejuvenation and modernisation of the 2015 platform into using a popular open source data management system, DSpace, as foundation. |
| Outcome: | Linghub2 1 aims to help language resources and technology users find and retrieve relevant data . the new platform, Ling hub2, contains updated and extended resources and more languages offered . |
CxLM: A Construction and Context-aware Language Model (2022.lrec-1)
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| Challenge: | Constructions are direct form-meaning pairs with possible schematic slots . however, these slots are constrained by the embedded construction and the context . we propose that a conditional probability distribution could be described but language models cannot capture this distribution. |
| Approach: | They propose that a conditional probability distribution could describe constructions’ schematic slots. |
| Outcome: | The proposed model predicts masked slots more accurately than baselines and generates structurally and semantically plausible word samples. |
The Lexometer: A Shiny Application for Exploratory Analysis and Visualization of Corpus Data (2022.lrec-1)
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| Challenge: | Lexometer is a data science application that integrates data analysis and visualization functions into an easy-to-use graphical user interface. |
| Approach: | They propose a Shiny application that integrates data analysis and visualization functions into an easy-to-use graphical user interface. |
| Outcome: | The Lexometer integrates numerous data analysis and visualization functions into an easy-to-use graphical user interface. |
TallVocabL2Fi: A Tall Dataset of 15 Finnish L2 Learners’ Vocabulary (2022.lrec-1)
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| Challenge: | Existing work on second language knowledge has focused on the knowledge of small numbers of words, often geared towards measuring vocabulary size. |
| Approach: | They propose a “tall” word knowledge response dataset containing information about a few learners’ knowledge of many words. |
| Outcome: | The proposed dataset is based on a self-rating test and translation test and is compared with previous comparable datasets. |
CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts (2022.lrec-1)
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| Challenge: | Social media platforms are important resources for investigating mental health of users. |
| Approach: | They propose a new dataset for Causal Analysis of Mental health in Social media posts (CAMS) they crawl and annotate 3155 Reddit data and reannotate a publicly available SDCNL dataset . |
| Outcome: | The proposed model outperforms existing models on 3155 Reddit posts and 1896 instances of the dataset. |
How Does the Experimental Setting Affect the Conclusions of Neural Encoding Models? (2022.lrec-1)
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| Challenge: | Recent studies have shown that neural encoding models explore brain language processing using naturalistic stimuli. |
| Approach: | They propose a block-wise cross-validation training method and an adequate data size for increasing the performance of neural encoding models. |
| Outcome: | The proposed training method and data size can significantly decrease the performance of neural encoding models in the temporal and frontal lobes. |
SPADE: A Big Five-Mturk Dataset of Argumentative Speech Enriched with Socio-Demographics for Personality Detection (2022.lrec-1)
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| Challenge: | Recent efforts to create such datasets from social media do not include continuous and contextualized language use. |
| Approach: | They propose to use argumentative speech to generate a dataset with continuous arguments labeled with the Big Five personality traits and enriched with socio-demographic data. |
| Outcome: | The proposed model leverages 436 (psycho)linguistic features extracted from transcribed speech and speaker-level metainformation with transformers to investigate which types of features contribute to the prediction of individual personality traits. |
Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut (2022.lrec-1)
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| Challenge: | Using acoustic data, we develop automatic speech recognition systems for three low resource languages. |
| Approach: | They develop automatic speech recognition systems for three low resource languages using acoustic training data from 12 different languages in the hybrid DNN/HMM framework. |
| Outcome: | The proposed models are for three low resource languages: Kurmanji Kurdish, Cree and Inuktut. |
Efficient Entity Candidate Generation for Low-Resource Languages (2022.lrec-1)
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| Challenge: | Existing approaches for cross-lingual entity linking are not suitable for English. |
| Approach: | They propose a candidate generation problem in cross-lingual entity linking with a focus on low-resource languages. |
| Outcome: | The proposed solution outperforms the state-of-the-art approach on 9 real-world datasets and query types. |
What a Creole Wants, What a Creole Needs (2022.lrec-1)
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| Challenge: | Recent efforts to improve the quality of high-resource languages focus on translating existing datasets into other languages, but this approach ignores that different language communities have different needs. |
| Approach: | They examine how things needed from language technology can change dramatically from one language to another. |
| Outcome: | The proposed method ignores that different language communities have different needs. |
Extensions to Brahmic script processing within the Nisaba library: new scripts, languages and utilities (2022.lrec-1)
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| Challenge: | a brahmic script is used to record endangered languages such as Dogri and Bengali for low-resource languages such that do not require visual normalization. |
| Approach: | They propose to extend Brahmic script functionality within the Nisaba library of finite-state script normalization and processing utilities. |
| Outcome: | The proposed extensions extend coverage from the original ten scripts to an additional ten of South Asia and beyond, including some used to record endangered languages such as Dogri. |
Predicting Embedding Reliability in Low-Resource Settings Using Corpus Similarity Measures (2022.lrec-1)
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| Challenge: | a paper aims to evaluate embedding similarity, stability and reliability in low-resource settings . it uses corpus similarity measures before training to predict properties of embeddables . |
| Approach: | They use corpus similarity measures before training to predict properties of embeddings . they then apply the same measures to low-resource settings by modelling reliability . authors hope to use this method to evaluate low-source languages with limited corpus size . |
| Outcome: | The paper shows that it is possible to predict downstream embedding similarity using upstream corpus similarity measures . the main finding is that the measures remain robust on small amounts of training data . |
Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation (2022.lrec-1)
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Idris Abdulmumin, Satya Ranjan Dash, Musa Abdullahi Dawud, Shantipriya Parida, Shamsuddeen Muhammad, Ibrahim Sa’id Ahmad, Subhadarshi Panda, Ondřej Bojar, Bashir Shehu Galadanci, Bello Shehu Bello
| Challenge: | Hausa is considered a low resource language in natural language processing due to lack of resources. |
| Approach: | They propose a dataset that contains the description of an image in Hausa and its equivalent in English. |
| Outcome: | The Hausa Visual Genome is the first dataset of its kind . it can be used for Hausa-English machine translation, multi-modal research, image description . |
A Survey of Machine Translation Tasks on Nigerian Languages (2022.lrec-1)
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| Challenge: | Existing work on machine translation of low-resource African languages is limited . despite advances in machine translation, there is limited work on Nigerian languages . |
| Approach: | They propose to focus on neural machine translation techniques for Nigerian languages . they outline the limitations of machine translation research on the continent . |
| Outcome: | The proposed research on Nigerian languages highlights the limitations of the current state of the art in machine translation. |
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset (2022.lrec-1)
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Tiezheng Yu, Rita Frieske, Peng Xu, Samuel Cahyawijaya, Cheuk Tung Yiu, Holy Lovenia, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language . due to the popularization of deep learning, ASR technology has led to a significant improvement in recognizing many languages. |
| Approach: | They propose to use a dataset to analyze the data available for the Hong Kong Cantonese language . they use zh-HK as a source and a state-of-the-art ASR model to build a powerful model . |
| Outcome: | The proposed model improves on the biggest existing dataset, Common Voice zh-HK. |
Survey on Thai NLP Language Resources and Tools (2022.lrec-1)
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| Challenge: | Thai language is one of the under-resourced languages in the NLP domain, although it is spoken by approximately 70 million people globally. |
| Approach: | They propose to use Thai language as an example to understand how NLP works and how it can be applied to Thai language. |
| Outcome: | The results show that Thai NLP research has progressed over the past three decades, especially on upstream tasks such as tokenisation, but research on downstream tasks such syntactic parsing and semantic analysis is still limited. |
LaoPLM: Pre-trained Language Models for Lao (2022.lrec-1)
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| Challenge: | Pre-trained language models (PLMs) can capture different levels of concepts in context . previous work on Lao has been hampered by the lack of annotated datasets . |
| Approach: | They construct a text classification dataset to alleviate the resource-scarce situation of Lao . they evaluate them on two downstream tasks: part-of-speech tagging and text classification . |
| Outcome: | The proposed model can capture different levels of concepts in context and generate universal language representations. |
The Maaloula Aramaic Speech Corpus (MASC): From Printed Material to a Lemmatized and Time-Aligned Corpus (2022.lrec-1)
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| Challenge: | The corpus contains 64,845 words, including lemmas, tokens, types, lemmas, sentences, narratives, and speakers. |
| Approach: | They present the first electronic speech corpus of Maaloula Aramaic . it is a Western Neo-Aramaic variety spoken in three Syrian villages . |
| Outcome: | The corpus contains transcriptions, lemmatized transcriptions and audio files . it is available in four formats: transcriptions with audio and phonetic transcriptions . |
VIMQA: A Vietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering (2022.lrec-1)
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| Challenge: | Existing Vietnamese Question Answering (QA) datasets do not explore the model’s ability to perform advanced reasoning and provide evidence to explain the answer. |
| Approach: | They propose to use Vietnamese as a question-answer dataset with 10,000 Wikipedia-based multi-hop question-and-answ pairs to test model's ability to reason and explain the answer. |
| Outcome: | The proposed dataset is in Vietnamese, a low-resource language. |
Language Identification for Austronesian Languages (2022.lrec-1)
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| Challenge: | This paper provides language identification models for low- and under-resourced languages in the Pacific region with a focus on previously unavailable Austronesian languages. |
| Approach: | They compare a classifier based on skip-gram embeddings with other methods . they then increase the number of non-Austronesian languages to 800 to evaluate their performance . |
| Outcome: | The proposed model improves on the previous methods for low- and under-resourced languages in the Pacific region. |
A Mapudüngun FST Morphological Analyser and its Web Interface (2022.lrec-1)
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| Challenge: | a computational tool for Mapudüngun language is developed using finite state technology . it is the first of its kind for the language and is available as a web service for free . |
| Approach: | They propose to develop a morphological and phonological machine for Mapudüngun using finite state technology. |
| Outcome: | The proposed system is the first of its kind for the Mapuche language and is available for public use through a web interface. |
Improving Large-scale Language Models and Resources for Filipino (2022.lrec-1)
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| Challenge: | a new large-scale pretraining corpus for Filipino improves existing resources for low-resource languages . a large dataset is too small and too narrow to create robust models that perform well in modern NLP. |
| Approach: | They propose a large-scale pretraining corpus for Filipino and a new RoBERTa pretraining technique to supplant existing models trained with small corpora. |
| Outcome: | The proposed model improves on existing models for the low-resource Filipino language . the model gains 4.47% test accuracy across three classification tasks with varying difficulty . |
Thirumurai: A Large Dataset of Tamil Shaivite Poems and Classification of Tamil Pann (2022.lrec-1)
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Shankar Mahadevan, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Prabakaran Chandran, Ruba Priyadharshini, Sangeetha S, Bharathi Raja Chakravarthi
| Challenge: | Thirumurai is a collection of Tamil Shaivite poems dating back to the Hindu revival period . a large dataset containing all the Thirumuru poems is under-resourced . |
| Approach: | They propose to use transformers to classify the Tamil Pann and author of each poem . they propose to train models on petabytes of data, such as the common crawl data . |
| Outcome: | The proposed dataset contains all the Thirumurai poems and classifies the Pann and author of each poem using transformer based architectures. |
Generating Monolingual Dataset for Low Resource Language Bodo from old books using Google Keep (2022.lrec-1)
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Sanjib Narzary, Maharaj Brahma, Mwnthai Narzary, Gwmsrang Muchahary, Pranav Kumar Singh, Apurbalal Senapati, Sukumar Nandi, Bidisha Som
| Challenge: | Bodo is a scheduled Indian language spoken largely by the Boda community in Assam and other northeastern Indian states. |
| Approach: | They propose to generate a monolingual Bodo corpus from different books using Google Keep for OCR. |
| Outcome: | The proposed method generates a monolingual Bodo corpus from different books using free, accessible, and daily-usable applications. |
AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition (2022.lrec-1)
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| Challenge: | Named entity recognition (NER) is a type of annotation that classifies text into predefined classes such as person, location, organization etc. |
| Approach: | They propose to use a named entity annotation dataset for low resource Assamese language with a baseline NER model. |
| Outcome: | The proposed dataset is likely to be significant resource for deep neural based Assamese language processing. |
GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages (2022.lrec-1)
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| Challenge: | Low-resourced languages with similar typologies are often confused with each other in real-world applications such as machine translation, affecting the user’s experience. |
| Approach: | They propose to build a dataset for five typologically and phylogenetically related low-resourced East African languages using the Ge’ez script as a writing system. |
| Outcome: | The proposed dataset is built automatically from selected data sources, but also performed a manual evaluation to assess its quality. |
Handwritten Paleographic Greek Text Recognition: A Century-Based Approach (2022.lrec-1)
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| Challenge: | achieving high accuracy HTR results for Greek manuscripts is still a major challenge . Optical character recognition software is notoriously difficult to use for handwritten text . |
| Approach: | They propose to use Greek manuscripts as a source for a new model to assess HTR accuracy. |
| Outcome: | The proposed model can be used to improve the recognition rate of Greek manuscripts. |
Quality Control for Crowdsourced Bilingual Dictionary in Low-Resource Languages (2022.lrec-1)
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| Challenge: | a recent study shows that crowdsourcing is becoming mainstream to create bilingual dictionaries . the number of people who can speak multiple low-resource languages is limited and the average ability of workers is low. |
| Approach: | They propose a method to aggregate the answers of evaluation tasks by majority voting . they use hyper questions to evaluate the reliability of workers and task-allocation method to select high-quality workers . |
| Outcome: | The proposed method improves quality of bilingual dictionaries by integrating answers by majority voting. |
An Inflectional Database for Gitksan (2022.lrec-1)
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Bruce Oliver, Clarissa Forbes, Changbing Yang, Farhan Samir, Edith Coates, Garrett Nicolai, Miikka Silfverberg
| Challenge: | In this paper, we build a database of partial inflection tables for Gitksan, a low-resource Indigenous language of Canada. |
| Approach: | They use Gitksan data in interlinear glossed format to build a database of partial inflection tables and enrich it with neural transformer reinflection models. |
| Outcome: | The proposed model improves the performance of the experimental data hallucination and back-translation techniques. |
PyCantonese: Cantonese Linguistics and NLP in Python (2022.lrec-1)
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| Challenge: | a limited number of Cantonese-specific datasets are available for PyCantones. |
| Approach: | They introduce PyCantonese, an open-source Python library for Cantonesi linguistics and natural language processing. |
| Outcome: | The proposed library is open-source and available for free for all purposes, including commercial ones. |
Afaan Oromo Hate Speech Detection and Classification on Social Media (2022.lrec-1)
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| Challenge: | Hate and offensive speech on social media is a global problem that suffers the community especially, for an under-resourced language like Afaan Oromo. |
| Approach: | They develop a model to detect and classify Afaan Oromo hate speech on social media using different machine learning algorithms. |
| Outcome: | The proposed model outperforms existing models in gender, religion, race, and offensive speech on social media. |
Cross-lingual Linking of Automatically Constructed Frames and FrameNet (2022.lrec-1)
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| Challenge: | Existing semantic frame resources have been manually elaborated, but manual development is labor-intensive. |
| Approach: | They propose to link Japanese frames to English FrameNet by using cross-lingual word embeddings and a model that takes only the frame-evoking words into account. |
| Outcome: | The proposed model will facilitate the development of cross-lingual frame resources. |
Aligning the Romanian Reference Treebank and the Valence Lexicon of Romanian Verbs (2022.lrec-1)
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| Challenge: | Among the language resources for Romanian, there are ones that describe the syntactic and semantic aspects of the language. |
| Approach: | They propose to align two language resources for Romanian: the Romanian Reference Treebank and the Valence Lexicon of Romanian Verbs. |
| Outcome: | The proposed alignments identify morpho-syntactic annotation mistakes, incomplete valence frames or missing ones. |
PortiLexicon-UD: a Portuguese Lexical Resource according to Universal Dependencies Model (2022.lrec-1)
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| Challenge: | lexical resource for Brazilian Portuguese with 1,221,218 entries, according to the Universal Dependencies model and guidelines. |
| Approach: | They propose to build a large and freely available lexicon for Portuguese that delivers morphosyntactic information according to the Universal Dependencies model. |
| Outcome: | The proposed lexical resource has high language coverage and good quality data. |
Extended Parallel Corpus for Amharic-English Machine Translation (2022.lrec-1)
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| Challenge: | Existing approaches to automate the complex task of translation are tedious and expensive. |
| Approach: | They describe acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. |
| Outcome: | The proposed corpus outperforms statistical machine translation models by six to seven BLEU points . the results show that the subword models outperformed word-based models by three to four BLUE points compared with the word-base models . |
Low-resource Neural Machine Translation: Benchmarking State-of-the-art Transformer for Wolof<->French (2022.lrec-1)
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| Challenge: | Neural machine translation (NMT) systems can translate between French (FR) 1 and Wolof (WO, ISO 639-3), a lowresource Niger-Congo language mainly spoken in Senegal (Gamble, 1950). |
| Approach: | They propose two neural machine translation systems based on sequence-to-sequence with attention and Transformer architectures to translate between French (FR) 1 and Wolof (WO, ISO 639-3). |
| Outcome: | The proposed models outperform the classic sequence-to-sequence model in all settings and are less sensitive to noise. |
Criteria for Useful Automatic Romanization in South Asian Languages (2022.lrec-1)
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| Challenge: | a number of possible criteria for systems that transliterate South Asian languages are considered . romanization is the special case where the target script is the Latin script. |
| Approach: | They propose a set of criteria for systems that transliterate South Asian languages . criteria include fidelity to human linguistic behavior, processing utility for people, invertibility . they then propose several algorithms that address different criteria . |
| Outcome: | The proposed algorithms address linguistic considerations in the context of Brahmic scripts and languages that use them, such as Hindi and Malayalam. |
BERTology for Machine Translation: What BERT Knows about Linguistic Difficulties for Translation (2022.lrec-1)
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| Challenge: | Pre-trained transformer-based models have shown excellent performance in most benchmark tests, but lack a good understanding of the linguistic knowledge of BERT in Neural Machine Translation (NMT). |
| Approach: | They propose to use QE models to analyze BERT's syntactic dependencies and their impact on machine translation quality. |
| Outcome: | The proposed model is able to model with self-attention in the pre-training phase, which improves generalization ability. |
CVSS Corpus and Massively Multilingual Speech-to-Speech Translation (2022.lrec-1)
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| Challenge: | Existing work on speech-to-speech translation (S2ST) systems rely on text representation, but they are text-centric. |
| Approach: | They introduce a massively multilingual-to-English speech-tospeech translation corpus . they synthesize the translation text from the Common Voice speech corpus and CoVoST 2 into English . |
| Outcome: | The proposed corpus outperforms existing models on CoVoST 2 by 5.8 BLEU . the proposed model outperformed the previous state-of-the-art model without extra data . |
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus (2022.lrec-1)
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| Challenge: | Existing parallel corpora for English-Japanese are limited, limiting the accuracy of machine translation models. |
| Approach: | They propose a web-based English-Japanese parallel corpus with 21 million unique sentence pairs . this is more than twice as many as the previous corpus JParaCrawl v2.0 . |
| Outcome: | The proposed corpus boosts the accuracy of machine translation models on various domains. |
Learning How to Translate North Korean through South Korean (2022.lrec-1)
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| Challenge: | Existing NLP systems cannot properly handle North Korean inputs, despite limited data . Several NLP researchers have been working on the Korean language . |
| Approach: | They propose to manually create evaluation data for automatic alignment and machine translation, and investigate automatic alignment methods suitable for North Korean. |
| Outcome: | The proposed model trained by North Korean bilingual data significantly boosts translation accuracy compared to existing South Korean models in zero-shot settings. |
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)
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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen
| Challenge: | Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains . |
| Approach: | They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone. |
| Outcome: | The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. |
SansTib, a Sanskrit - Tibetan Parallel Corpus and Bilingual Sentence Embedding Model (2022.lrec-1)
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| Challenge: | a large digital monolingual corpus of Sanskrit and Tibetan Buddhist literature has become available. |
| Approach: | They propose to develop a Sanskrit - Classical Tibetan parallel corpus automatically aligned on sentence-level and a bilingual sentence embedding model. |
| Outcome: | The proposed model improves the existing Sanskrit - Classical Tibetan parallel corpus and its bilingual sentence embedding model. |
VISA: An Ambiguous Subtitles Dataset for Visual Scene-aware Machine Translation (2022.lrec-1)
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| Challenge: | Existing multimodal machine translation datasets contain images and video captions or general subtitles which rarely contain linguistic ambiguity. |
| Approach: | They propose a dataset that consists of Japanese-English parallel sentence pairs and corresponding video clips. |
| Outcome: | The proposed dataset is challenging for the latest MMT system and can facilitate MMT research. |
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation (2022.lrec-1)
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Kazuki Tani, Ryoya Yuasa, Kazuki Takikawa, Akihiro Tamura, Tomoyuki Kajiwara, Takashi Ninomiya, Tsuneo Kato
| Challenge: | Existing test datasets for MLCC-MT have three problems: A source language sentence and its simplified target language sentence are not necessarily exactly parallel. |
| Approach: | They propose to use a test dataset to evaluate multi-level complexity-controllable machine translation (MLCC-MT) their results are compared to a standard test dataset constructed from the Newsela corpus . |
| Outcome: | The proposed test dataset is based on the Newsela corpus and is released . it includes automatic filtering, manual check for parallel target language sentences . |
gaHealth: An English–Irish Bilingual Corpus of Health Data (2022.lrec-1)
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| Challenge: | Existing models for low-resource languages often focus on creating the largest possible dataset for generic translation. |
| Approach: | They develop a dataset for the specific domain of health for a low-resource English to Irish language pair and compare it to other similar datasets. |
| Outcome: | The proposed model improved BLEU score by 22.2 points compared with top performing models from the LoResMT2021 Shared Task. |
Translation Memories as Baselines for Low-Resource Machine Translation (2022.lrec-1)
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| Challenge: | low-resource machine translation research often requires building baselines to benchmark progress in translation quality. |
| Approach: | They argue that using available text as a translation memory baseline is simple and effective . they say that if you have parallel text, you have a TM . |
| Outcome: | a new study shows that using available text as a translation memory baseline is simple and effective . low-resource machine translation is often of too low quality to use directly, the authors argue . |
N24News: A New Dataset for Multimodal News Classification (2022.lrec-1)
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| Challenge: | Current news datasets focus on text features and rarely leverage the feature of images. |
| Approach: | They propose a news dataset that uses both images and text to achieve better news classification. |
| Outcome: | The proposed model improves on the existing dataset N24News with text and image information. |
MultiSubs: A Large-scale Multimodal and Multilingual Dataset (2022.lrec-1)
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| Challenge: | a large-scale multimodal and multilingual dataset is used to facilitate research on visual grounding of words to images in their contextual usage in language. |
| Approach: | They propose a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. |
| Outcome: | The proposed dataset will facilitate research on visual grounding of words in their contextual usage in language. |
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition (2022.lrec-1)
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Wenliang Dai, Samuel Cahyawijaya, Tiezheng Yu, Elham J. Barezi, Peng Xu, Cheuk Tung Yiu, Rita Frieske, Holy Lovenia, Genta Winata, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. |
| Approach: | They propose a dataset for in-car command recognition in the cantonese language with both video and audio data. |
| Outcome: | The proposed model can achieve a considerable quality on the clean test set, but the speech recognition quality on noisy data is still inferior. |
Multimodal Negotiation Corpus with Various Subjective Assessments for Social-Psychological Outcome Prediction from Non-Verbal Cues (2022.lrec-1)
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| Challenge: | Existing corpora only include information related to objective outcomes or a single aspect of psychology. |
| Approach: | They investigated social-psychological negotiation-outcome prediction task from negotiation dialogue data. |
| Outcome: | The proposed task is useful because negotiation data only include objective outcomes or a single aspect of psychology. |
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation (2022.lrec-1)
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| Challenge: | Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses. |
| Approach: | They propose to use multimodal directed acyclic graphs to integrate multimodal information and contextual information into a DAG architecture to exploit multimodal contexts. |
| Outcome: | Comparative studies on IEMOCAP and MELD show that the proposed model outperforms state-of-the-art models. |
Automatic Gloss-level Data Augmentation for Sign Language Translation (2022.lrec-1)
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| Challenge: | Existing methods for enhancing sign language text data are insufficient . fewer studies have been performed on text data augmentation compared to video data . |
| Approach: | They propose three methods to augment sign language text data using Korean sign language gloss dictionary. |
| Outcome: | The proposed method improves translation performance by 0.204 and 0.170 compared to the original data. |
Image Description Dataset for Language Learners (2022.lrec-1)
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| Challenge: | Language learners are limited by the number of texts or speech they are asked to answer . automatic assessment of image descriptions requires a system that depends on both the learner's native language and the target language. |
| Approach: | They propose a dataset that consists of images, their descriptions, and assessment annotations . they propose 'automatic error correction' task that encodes multimodal information from a learner sentence with an image and accurately decodes a corrected sentence. |
| Outcome: | The proposed model can revise errors that cannot be revised without an image. |
The Multimodal Annotation Software Tool (MAST) (2022.lrec-1)
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| Challenge: | Existing visual and multimodal annotation systems are limited in scope and applicability. |
| Approach: | They propose a tool that allows users to analyze visual and multimodal documents . they aim to provide a powerful and innovative annotation tool with application across fields . |
| Outcome: | The MAST tool allows users to analyze visual and multimodal documents . it allows annotation theories to be citable, while evolving and being shared . |
A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning (2022.lrec-1)
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| Challenge: | Lip reading is a visual observation of a speaker's lips that can be used for communication problems. |
| Approach: | They present a dataset of 250,000 publicly available videos of speakers of the Hessian Parliament which was processed for word-level lip reading using an automatic pipeline. |
| Outcome: | The proposed dataset GLips (German Lips) is compared with the LRW dataset and shows that it has language-independent features. |
Multimodality for NLP-Centered Applications: Resources, Advances and Frontiers (2022.lrec-1)
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| Challenge: | resurgence of multimodal datasets has attracted significant research interest, but there is no comprehensive survey for this task. |
| Approach: | They present a survey of a multimodal dataset with different modalities according to the applications. |
| Outcome: | The proposed datasets are available online and discuss the new frontier and motivate future researches. |
Cross-lingual and Multilingual CLIP (2022.lrec-1)
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| Challenge: | OpenAI released CLIP, a model that relates the textual and visual domains with unprecedented accuracy. |
| Approach: | They propose to use cross-lingual teacher learning to re-train an English textual encoder using a large dataset of images and captions. |
| Outcome: | The proposed method outperforms baselines on multilingual image-text retrieval while retaining low cost. |
BAN-Cap: A Multi-Purpose English-Bangla Image Descriptions Dataset (2022.lrec-1)
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| Challenge: | BAN-Cap dataset is based on the widely used Flickr8k dataset, which is used to collect captions of images from qualified annotators. |
| Approach: | They propose to use a dataset to collect Bangla captions from qualified annotators and to evaluate the models for the task. |
| Outcome: | The proposed model outperforms state-of-the-art models for Bangla captioning and English-Bangla translation. |
SSR7000: A Synchronized Corpus of Ultrasound Tongue Imaging for End-to-End Silent Speech Recognition (2022.lrec-1)
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| Challenge: | Existing models for silent speech recognition have not been able to capture large datasets. |
| Approach: | They present a corpus of synchronized ultrasound tongue and lip images for silent speech recognition . they use a large dataset to exploit the performance of the end-to-end models . |
| Outcome: | The proposed model outperforms existing models in a large dataset of ultrasound tongue and lip images . the dataset contains more utterances per person than any other based on ultrasound imaging . |
A Simple Yet Effective Corpus Construction Method for Chinese Sentence Compression (2022.lrec-1)
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| Challenge: | Deletion-based sentence compression has made significant progress in the english language . however, there is a lack of large-scale and high-quality parallel corpus for the Chinese language to train an efficient system. |
| Approach: | They propose to construct a Chinese corpus with 151k pairs of sentences and train extractive and generative neural compression models on the constructed corpus. |
| Outcome: | The proposed method generates high-quality compressed sentences on automatic and human evaluation metrics compared with baselines. |
JADE: Corpus for Japanese Definition Modelling (2022.lrec-1)
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| Challenge: | Existing corpus for definition modelling techniques is limited to English . this study aimed to develop a corpus that provides definitions of words and phrases . |
| Approach: | They investigated and released a corpus for Japanese definition modelling . the JADE provides 630k sets of targets, their definitions, and usage examples as contexts . |
| Outcome: | The JADE corpus provides 630k sets of targets, their definitions, and usage examples as contexts for 41k unique targets. |
Unraveling the Mystery of Artifacts in Machine Generated Text (2022.lrec-1)
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| Challenge: | Recent studies show that human-written text is not distinguishable from synthetic text because of semantic errors or logical contradictions. |
| Approach: | They propose to analyze the forms of artifacts left by neural Text Generation Models by corrupting texts and replacing them with linguistic or statistical features. |
| Outcome: | The proposed method replaces text with linguistic or statistical features and improves the accuracy of the model. |
Logic-Guided Message Generation from Raw Real-Time Sensor Data (2022.lrec-1)
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| Challenge: | Developing a natural language generation model to enable human pilots to communicate with drones is challenging because of its redundant nature and diversity. |
| Approach: | They propose a corpus for a specific domain that instantiates these properties by combining sensor data with text. |
| Outcome: | The proposed model can alert the human pilot of the system state and environment in preparation of handover of control. |
The Bull and the Bear: Summarizing Stock Market Discussions (2022.lrec-1)
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| Challenge: | a dataset of 7888 reddit posts and 400 posts is used to summarize stock market topics. |
| Approach: | They curate discussions on social media platforms and construct an abstractive summarization dataset. |
| Outcome: | The proposed dataset consists of 7888 Reddit posts and summaries for 400 posts . it is robustly evaluated and will be made publicly available . |
Combination of Contextualized and Non-Contextualized Layers for Lexical Substitution in French (2022.lrec-1)
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| Challenge: | Lexical substitution task requires to substitute a target word by candidates in a given context. |
| Approach: | They propose a method to find synonyms for a target word and rank them based on the context of the sentence. |
| Outcome: | The proposed method increases the BERT based system on the OOT measure but decreases on the BEST measure in the SemDis 2014 benchmark. |
SuMe: A Dataset Towards Summarizing Biomedical Mechanisms (2022.lrec-1)
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| Challenge: | Biomedical studies often examine how one entity affects another in a biological context. |
| Approach: | They propose a biomedical mechanism summarization task that pairs biomedically relevant texts with their summaries. |
| Outcome: | The proposed task improves performance but produces acceptable outputs in 32% of instances. |
CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset (2022.lrec-1)
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| Challenge: | Existing studies on cross-lingual summarization rely on pseudo-cross-lingual datasets . such an approach would lead to the loss of information in the original document and introduce noise into the summary . |
| Approach: | They present a high-quality cross-lingual long text abstractive summarization dataset . it contains 20,000 parallel news articles and corresponding summaries written by humans . |
| Outcome: | The proposed model outperforms monolingual systems in the cross-lingual task. |
Emotion analysis and detection during COVID-19 (2022.lrec-1)
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| Challenge: | 3,000 English tweets labeled with emotions are used to predict emotions during crises . authors propose semi-supervised learning to bridge this gap . |
| Approach: | They propose to use a dataset of 3,000 English tweets labeled with emotions . they propose semi-supervised learning to bridge this gap by analyzing unlabeled data . |
| Outcome: | The proposed model can be used to predict emotions in the context of COVID-19 . the proposed model performs better than other models using unlabeled data . |
Cross-lingual Emotion Detection (2022.lrec-1)
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| Challenge: | Emotion detection is a useful tool for understanding human behavior, but constructing annotated datasets to train models can be expensive. |
| Approach: | They propose to use English as the source language with Arabic and Spanish as target languages to train models for emotion detection in a target language. |
| Outcome: | The proposed approaches surpass state-of-the-art models in Arabic and Spanish by 4% and 5% respectively. |
DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles (2022.lrec-1)
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| Challenge: | Existing methods to extract and attribute quotations from news data are difficult and require a lot of effort. |
| Approach: | They propose a corpus of 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. |
| Outcome: | The proposed corpus contains 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. |
VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines (2022.lrec-1)
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| Challenge: | VaccineLies can detect misinformation about vaccines on Twitter without using language resources. |
| Approach: | They present a dataset of tweets propagating misinformation about two vaccines . authors propose novel methods to detect misinformation on Twitter and identify stance towards it . |
| Outcome: | VaccineLies can detect misinformation on Twitter and identify the stance towards it. |
Tackling Irony Detection using Ensemble Classifiers (2022.lrec-1)
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| Challenge: | Automated approaches to irony detection still fall short of what one would consider desirable performance. |
| Approach: | They propose to use transformer-based approaches to automate irony detection in social media . they propose to augmentation training data to address the binary and fine-grained problem . |
| Outcome: | The proposed methods improve performance over baselines and are not decisive for good results. |
Automatic Construction of an Annotated Corpus with Implicit Aspects (2022.lrec-1)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a task that involves classifying aspects of products or services described in user reviews. |
| Approach: | They propose a method for constructing a corpus that is automatically annotated with implicit aspects by combining explicit and unlabeled sentences. |
| Outcome: | The proposed method achieves a maximum accuracy of 82% on mobile phone reviews. |
A Multimodal Corpus for Emotion Recognition in Sarcasm (2022.lrec-1)
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| Challenge: | sarcasm and emotion are often used in conversational systems to generate the right response. |
| Approach: | They use a sarcastic expression dataset pre-annotated with 9 emotions to detect emotion . they identify and correct 343 incorrect emotion labels and label each sarkastic utterance with one of four sarcasm types. |
| Outcome: | The proposed model outperforms state-of-the-art sarcasm detection methods by using a multimodal sarcastic detection dataset. |
Annotation of Valence Unfolding in Spoken Personal Narratives (2022.lrec-1)
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Aniruddha Tammewar, Franziska Braun, Gabriel Roccabruna, Sebastian Bayerl, Korbinian Riedhammer, Giuseppe Riccardi
| Challenge: | Personal Narrative (PN) is the recollection of individuals’ life experiences, events, and thoughts along with the associated emotions in the form of a story. |
| Approach: | They annotate a corpus of spoken personal narratives with the emotion valence using discrete values and use a 5-point bipolar scale to measure their agreement. |
| Outcome: | The annotators annotate a corpus of spoken personal narratives with the emotion valence using discrete values on a 5-point bipolar scale ranging from -2 to +2 (0 for neutral). |
A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis (2022.lrec-1)
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| Challenge: | Aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language . there is no standard Japanese dataset available for ABSA task in the language - a paper by cnn. |
| Approach: | They propose to use a Japanese aspect-based sentiment analysis dataset for hotel reviews domain . they propose to include 53,192 review sentences with seven aspect categories and two polarity labels . |
| Outcome: | The proposed dataset contains 53,192 review sentences with seven aspect categories and two polarity labels. |
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain (2022.lrec-1)
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Haruya Suzuki, Yuto Miyauchi, Kazuki Akiyama, Tomoyuki Kajiwara, Takashi Ninomiya, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
| Challenge: | Existing studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. |
| Approach: | They extend the WRIME dataset with basic emotion intensity from both the writer's subjective and reader's perspective to include the Japanese sentiment polarity. |
| Outcome: | The proposed dataset is the first large-scale corpus to annotate both basic emotions and sentiment polarity labels from both the writer’s and reader’s perspectives. |
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis (2022.lrec-1)
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| Challenge: | Existing ABSA models do not pay attention to aspect terms and their contexts . a discriminator is introduced to improve ABSA, allowing for better understanding of aspect terms . |
| Approach: | They propose to improve ABSA by complementary learning of aspect terms . they explicitly recover aspect terms from each input sentence to better understand aspects . |
| Outcome: | The proposed approach improves ABSA on five widely used English benchmark datasets. |
Deep One-Class Hate Speech Detection Model (2022.lrec-1)
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| Challenge: | Existing approaches to hate speech detection neglect distinct attributes of hate speeches from other sentimental types such as “aggressive” and “racist”. |
| Approach: | They propose a one-class model where the detection classifier is trained with hate-class samples only. |
| Outcome: | The proposed model outperforms existing models with four benchmark datasets and shows that it performs better than existing models. |
Opinions in Interactions : New Annotations of the SEMAINE Database (2022.lrec-1)
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| Challenge: | a new method for the detection of opinions in interactions is proposed . a dataset of dyadic interactions is annotated continuously in two affective dimensions related to the emotions . |
| Approach: | They propose to annotate opinions over a multimodal corpus of dyadic interactions . they use a d-acting algorithm to annnotate the opinions of a speaker . |
| Outcome: | The proposed method allows to obtain a precise annotation regarding the opinion of a speaker. |
Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews (2022.lrec-1)
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| Challenge: | Existing systems for sentiment analysis are focused on document and sentence levels, but there are no public datasets on aspect-based sentiment analysis for Farsi. |
| Approach: | They propose to use a manually annotated Farsi dataset to analyze the opinion polarity of reviews . they also use transfer learning to analyze aspects of the review to improve their results . |
| Outcome: | The proposed method performs better than other aspects of the existing system. |
HindiMD: A Multi-domain Corpora for Low-resource Sentiment Analysis (2022.lrec-1)
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| Challenge: | Social media platforms such as Twitter and Facebook are a new channel of information dissemination for many negative groups for recruitment. |
| Approach: | They propose to use a social media sentiment analysis corpus annotated with the sentiment classes positive, negative and neutral to investigate the polarity of user-expressed opinions. |
| Outcome: | The proposed model is based on a set of benchmark datasets for sentiment analysis across a range of domains and languages. |
Sentiment Analysis of Homeric Text: The 1st Book of Iliad (2022.lrec-1)
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| Challenge: | Sentiment analysis studies focus more on online customer reviews and social media texts, but are less on literary studies. |
| Approach: | They propose to model the perceived sentiment of Iliad verses using a deep learning masked language model and a pre-trained model to estimate the sentiment of the poem. |
| Outcome: | The proposed model shows that sentiment estimators can be used as mechanical annotators, thus facilitating the distant reading of Homeric text. |
The Persian Dependency Treebank Made Universal (2022.lrec-1)
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| Challenge: | Existing universal dependency treebanks are lacking sufficient annotated data. |
| Approach: | They propose a method for converting Persian Dependency Treebank to Universal Dependencies using an automatic method. |
| Outcome: | The proposed method is more compatible with Universal Dependencies than the Uppsala Persian Universal Dependency Treebank. |
GujMORPH - A Dataset for Creating Gujarati Morphological Analyzer (2022.lrec-1)
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| Challenge: | morphological analyzers are used to analyze word forms at word level. |
| Approach: | They propose to create an annotated morphological dataset for the Gujarati language that contains 16,527 unique inflected words along with their morphology and grammatical feature tagging information. |
| Outcome: | The proposed dataset contains 16,527 unique inflected words along with their morphological segmentation and grammatical feature tagging information. |
Informal Persian Universal Dependency Treebank (2022.lrec-1)
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| Challenge: | phonological, morphological, and syntactic distinctions between formal and informal Persian are important . formal Persian is not a universally recognized form of language, but is a dialect of informal Persian . |
| Approach: | They develop an open-source treebank for informal Persian to train dependency parsers . they then train dependency lexicographers on existing treebanks and evaluate them on out-of-domain data . |
| Outcome: | The proposed treebanks show that they perform poorly when training on formal and informal Persians. |
Automatic Correction of Syntactic Dependency Annotation Differences (2022.lrec-1)
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| Challenge: | Annotation inconsistencies between data sets can cause problems for low-resource NLP . a simple method for automatically detecting annotation mismatches between corpora is proposed . |
| Approach: | They propose a method for automatically detecting annotation mismatches between dependency parsing corpora . they also propose three related methods for automatically configuring the mismatch . |
| Outcome: | The proposed method improves performance on both converted and unconverted data. |
Building Large-Scale Japanese Pronunciation-Annotated Corpora for Reading Heteronymous Logograms (2022.lrec-1)
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| Challenge: | Especially in Japanese, there are many common heteronyms expressed by logograms (Chinese characters or kanji) that have totally different pronunciations. |
| Approach: | They construct large-scale Japanese corpora that annotate kanji characters with their pronunciations to improve the accuracy of pronunciation prediction models. |
| Outcome: | The proposed models achieve an average accuracy of 0.939 for 203 common heteronyms and a 0.938 for 93 heters. |
StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands (2022.lrec-1)
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| Challenge: | Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner. |
| Approach: | They propose a corpus construction scheme that considers intent and formality of directives in Korean language. |
| Outcome: | The proposed method is validated by a corpus construction scheme on Korean topics. |
Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)
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| Challenge: | Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL . |
| Approach: | They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on two English benchmark datasets. |
HerBERT Based Language Model Detects Quantifiers and Their Semantic Properties in Polish (2022.lrec-1)
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| Challenge: | a tool for automatic marking up of quantifiers is proposed for Polish . it is trained on a recently annotated corpus of Polish quantificational expressions . |
| Approach: | They propose to use a BERT based neural model to mark up quantifiers in text . they analyse a manually annotated corpus of Polish quantificational expressions and compare it to a human annotation model. |
| Outcome: | The proposed model can be used to build semantically annotated quantifier corpora for other languages. |
Lexical Resource Mapping via Translations (2022.lrec-1)
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| Challenge: | a lexical resource associates words with concepts in multiple languages, which makes it difficult to combine information from multiple resources. |
| Approach: | They propose a translation-based approach to mapping lexical resources . they use word-concept pairs to align WordNet/BabelNet to CLICS and OmegaWiki . |
| Outcome: | The proposed method achieves state-of-the-art accuracy without other sources of knowledge . it can be framed as word sense disambiguation, and it can improve on existing methods . |
Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models (2022.lrec-1)
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| Challenge: | Existing methods for creating metaembeddings from static word embeddings have been proposed, but they are not tied to a particular downstream task. |
| Approach: | They propose a sentence-level meta-embedding learning method that takes contextualised word embedding models and learns a phrase embeddable that preserves complementary strengths of the input source NLMs. |
| Outcome: | The proposed method outperforms existing methods on semantic textual similarity benchmarks on a supervised baseline and on token-level embeddings. |
Identification of Fine-Grained Location Mentions in Crisis Tweets (2022.lrec-1)
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| Challenge: | Recent studies have focused on identifying informative tweets by individuals affected by a crisis, without considering their specific types. |
| Approach: | They assemble two tweet crisis datasets and manually annotate them with specific location types to facilitate progress on the fine-grained location identification task. |
| Outcome: | The proposed model performs well in both in-domain and cross-domain settings. |
HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection (2022.lrec-1)
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| Challenge: | In Brazil, hate speech is prohibited, however the regulation is not effective due to the difficulty of identifying, quantifying and classifying this kind of online content. |
| Approach: | They propose to annotate a large corpus of Brazilian Instagram comments manually and to use it to detect hate speech and offensive language. |
| Outcome: | The HateBR corpus was collected from the comment section of Brazilian politicians’ accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement. |
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare (2022.lrec-1)
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| Challenge: | Existing pretrained language models for mental health detection are inadequate . one in four people worldwide suffers from mental disorders . |
| Approach: | They train and release two pretrained masked language models to benefit machine learning for mental healthcare research . they demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks. |
| Outcome: | The proposed models improve mental health detection tasks on several benchmarks and are available for free. |
Leveraging Hashtag Networks for Multimodal Popularity Prediction of Instagram Posts (2022.lrec-1)
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| Challenge: | Existing popularity prediction approaches reduce hashtags to simple features such as hashtag length or number of hashtags in a post. |
| Approach: | They propose a multimodal framework to predict popular influencer posts on Instagram using post captions, image, hashtag network and topic model. |
| Outcome: | The proposed framework outperforms baseline models and unimodal models on popular influencer posts in Taiwan . it uses post captions, image, hashtag network, and topic model to predict popular influence post . |
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis (2022.lrec-1)
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| Challenge: | Social media data such as Twitter messages pose a particular challenge to NLP systems because of their short, noisy nature. |
| Approach: | They create a Twitter-based NER corpus and train Tweet NLP models on it . they annotate named entities in TB2 using Amazon Mechanical Turk . |
| Outcome: | The proposed model outperforms existing models on Twitter and other social media platforms. |
Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show (2022.lrec-1)
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| Challenge: | Prior work identified and summarized scenes associated with a TV show by selecting a few representative social media posts (5 posts) that were published during the timeline of the scenes. |
| Approach: | They propose a method to predict social media posts associated with a TV show from those that are not-indicative. |
| Outcome: | The proposed method can predict posts indicative of what happened in a scene from those that are not-indicative based on high AUC's on social media posts associated with a popular TV show . |
HashSet - A Dataset For Hashtag Segmentation (2022.lrec-1)
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| Challenge: | Hashtag segmentation is the task of breaking a hashtag into constituent tokens . hashtags are often written in unique ways, including spelling variations, and special characters. |
| Approach: | They propose a dataset that breaks hashtags into constituent tokens to train and validate models. |
| Outcome: | The proposed dataset provides an alternate set of hashtags to build and validate hashtag segmentation models. |
Using Convolution Neural Network with BERT for Stance Detection in Vietnamese (2022.lrec-1)
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| Challenge: | Stance detection is a task of automatically eliciting stance information towards a specific claim made by a primary author. |
| Approach: | They propose an architecture using transformers to detect stances in Vietnamese claims . they exploit BERT to extract contextual word embeddings instead of traditional word2vec models . |
| Outcome: | The proposed model outperforms the previous methods on a public dataset. |
Annotation-Scheme Reconstruction for “Fake News” and Japanese Fake News Dataset (2022.lrec-1)
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| Challenge: | Contemporary research focuses on the factuality aspect of the news, but this aspect alone is insufficient to explain “fake news.” |
| Approach: | They propose to use Japanese fake news datasets to classify whether news content is false . they propose to do this by using existing fake news data to investigate fake news . |
| Outcome: | The proposed scheme will provide an in-depth understanding of fake news in Japan and other languages. |
RoBERTuito: a pre-trained language model for social media text in Spanish (2022.lrec-1)
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| Challenge: | Pre-trained language models have been used in many natural language processing tasks . some domain-specific models have shown to improve performance in some domains . however, for languages other than English, such models are not widely available . |
| Approach: | They present a pre-trained language model for user-generated text in Spanish . it is based on 500 million tweets and has some cross-lingual abilities . |
| Outcome: | The model outperforms models trained on over 500 million tweets on a benchmark in spanish and english. |
Construction of Responsive Utterance Corpus for Attentive Listening Response Production (2022.lrec-1)
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| Challenge: | In Japan, the number of single-person households is increasing, reducing opportunities for people to narrate. |
| Approach: | They propose to collect 148,962 responsive utterances by listeners and annotate existing narrative speech with responsive . they also propose to use robots and smart speakers to listen to narratives . |
| Outcome: | The proposed method can be used to annotate existing narrative speech with responsive utterances. |
Speak: A Toolkit Using Amazon Mechanical Turk to Collect and Validate Speech Audio Recordings (2022.lrec-1)
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| Challenge: | Speak is a toolkit that allows researchers to crowdsource speech recordings using Amazon Mechanical Turk (MTurk). |
| Approach: | They propose to use Amazon Mechanical Turk to crowdsource speech recordings . they use various measures to ensure that the recordings are of adequate quality . |
| Outcome: | Speak is an open-source toolkit that allows researchers to crowdsource speech recordings using Amazon Mechanical Turk (MTurk). |
ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation (2022.lrec-1)
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Holy Lovenia, Samuel Cahyawijaya, Genta Winata, Peng Xu, Yan Xu, Zihan Liu, Rita Frieske, Tiezheng Yu, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. |
| Approach: | They propose to collect Mandarin Chinese-English code-switching corpus from read speech rather than spontaneous speech to address this phenomenon. |
| Outcome: | ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. |
A Romanization System and WebMAUS Aligner for Arabic Varieties (2022.lrec-1)
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Jalal Al-Tamimi, Florian Schiel, Ghada Khattab, Navdeep Sokhey, Djegdjiga Amazouz, Abdulrahman Dallak, Hajar Moussa
| Challenge: | The WebMAUS 1 is a suite of webservices that is free for academic users that processes 42 languages and language varieties. |
| Approach: | They propose to develop an Arabic variety-independent romanization system that aims to homogenize and simplify the romanization of the Arabic script. |
| Outcome: | The proposed system is based on the existing Arabic variety-independent WebMAUS services. |
BembaSpeech: A Speech Recognition Corpus for the Bemba Language (2022.lrec-1)
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| Challenge: | Existing speech recognition systems for African languages are very low . lack of resources (speech and text) can be attributed to poor quality of speech. |
| Approach: | They present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting of 24 hours of read speech in the Bemba language. |
| Outcome: | The proposed model achieves a word error rate (WER) of 32.91% on the Bemba language . the 1 billion XLS-R parameter model achieve better performance than the monolingual pre-trained English model on the corpus. |
BehanceCC: A ChitChat Detection Dataset For Livestreaming Video Transcripts (2022.lrec-1)
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| Challenge: | livestreaming videos contain a considerable amount of off-topic content, causing noises and data load to downstream applications. |
| Approach: | They propose a human-annotated benchmark dataset for off-topic detection in livestreaming video transcripts. |
| Outcome: | The proposed dataset reveals the complexity of chitchat detection in livestreaming videos . livestreams tend to be longer than pre-recorded videos and have fewer verbal pauses . |
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection (2022.lrec-1)
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| Challenge: | Currently, researchers focus on how to protect the speaker's identifiable information, represented as voiceprint, contained in the speech. |
| Approach: | They propose a frame-by-frame adversarial speech generation system to protect speech . they build an adversarials-based method that converts adversarially generated speech to human speech. |
| Outcome: | The proposed method can encode and recover any sensitive audio, and it is easy to be conducted with publicly available speech recognition technology. |
A new European Portuguese corpus for the study of Psychosis through speech analysis (2022.lrec-1)
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| Challenge: | Psychosis is a clinical syndrome characterized by symptoms such as hallucinations, delusions, thought disorders and disorganized speech. |
| Approach: | They describe the creation of the first European Portuguese corpus for the identification of the presence of speech characteristics of psychosis. |
| Outcome: | The results show that spontaneous speech presents more identifiable characteristics than read speech to differentiate healthy and patients diagnosed with psychosis. |
Investigating Inter- and Intra-speaker Voice Conversion using Audiobooks (2022.lrec-1)
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| Challenge: | Audiobook readers play with their voices to emphasize some text passages, highlight discourse changes or significant events, or in order to make listening easier and entertaining. |
| Approach: | They propose to modify the narrator’s voice to fit the context of the story, such as the character who is speaking, using voice conversion. |
| Outcome: | The proposed method improves the quality of the voice conversion system and the speaker similarity. |
Multilingual Transfer Learning for Children Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Recent advances in automatic speech recognition (ASR) systems have been criticized for high acoustic variability and limited amount of available training data. |
| Approach: | They propose a two-step training strategy that uses multilingual learning followed by language-specific transfer learning to generalize children's speech. |
| Outcome: | The proposed training strategy outperforms single language training and multilingual and transfer learning alone in English. |
BehanceQA: A New Dataset for Identifying Question-Answer Pairs in Video Transcripts (2022.lrec-1)
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| Challenge: | Question-Answer (QA) is an effective method for storing knowledge . prior QA identification systems have been limited to formal written documents . a large-scale QA dataset annotated by human over 500 hours of video transcripts is a challenge . |
| Approach: | They present a large-scale QA identification dataset annotated by human over 500 hours of video transcripts. |
| Outcome: | The proposed dataset presents unique challenges for existing methods . it shows that the annotated dataset presents challenges for new methods - the results will be released . |
Bidirectional Skeleton-Based Isolated Sign Recognition using Graph Convolutional Networks (2022.lrec-1)
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| Challenge: | a new method for computer-based sign recognition from video is proposed . it involves explicit detection of the start and end frames of isolated signs . |
| Approach: | They propose a skeleton-based method that involves explicit detection of start and end frames of signs . they apply a modified WLASL dataset with corrections to the gloss labeling . |
| Outcome: | The proposed method outperforms state-of-the-art methods on the modified WLASL dataset . it has a success rate of 77.43% and 94.54% for top-5 . |
Deep learning-based end-to-end spoken language identification system for domain-mismatched scenario (2022.lrec-1)
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| Challenge: | Domain mismatch is a critical issue when it comes to spoken language identification. |
| Approach: | They evaluated a set of cross-domain language identification trials using a dataset from the Oriental Language Recognition (OLR) Challenge 2021 . |
| Outcome: | The proposed architectures and deep learning strategies have shown good performance in cross-domain speaker verification tasks. |
Handwritten Character Generation using Y-Autoencoder for Character Recognition Model Training (2022.lrec-1)
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| Challenge: | re-emergence of deep learning since third winter of artificial intelligence has led to mainstreaming of deep-learning systems that use large amounts of data to train a model. |
| Approach: | They propose a Y-Autoencoder-based handwritten character generator to generate Japanese Hiragana characters with a single image to increase the amount of data needed for character recognition. |
| Outcome: | The proposed system generates Japanese Hiragana characters with a single image . the results show that the Y-AE-based generator produces an improved F1 score . |
Attention-Focused Adversarial Training for Robust Temporal Reasoning (2022.lrec-1)
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| Challenge: | Current adversarial training approaches for NLP add adversarials to the embedding layer, ignoring other layers. |
| Approach: | They propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models . they add the adversarials to multiple hidden states or attention representations of the model layers . |
| Outcome: | The proposed model improves performance on several temporal reasoning benchmarks and establishes new state-of-the-art results. |
PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter (2022.lrec-1)
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| Challenge: | Pre-trained domain-specific models are useful for understanding domain-level contexts. |
| Approach: | They propose to use a pre-trained language model to better capture domain-specific contexts. |
| Outcome: | The proposed model outperforms general-purpose models on election-related tasks. |
Modeling the Impact of Syntactic Distance and Surprisal on Cross-Slavic Text Comprehension (2022.lrec-1)
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| Challenge: | Using symmetric measures of insertion, deletion and movement of syntactic units, we investigate phonetic and orthographic asymmetries between selected languages. |
| Approach: | They focus on the syntactic variation and measure syntaktic distances between nine Slavic languages using symmetric measures of insertion, deletion and movement of syntak units in parallel sentences of the fable “The North Wind and the Sun”. |
| Outcome: | The proposed measures are validated on spoken and written cloze tests for Slavic native speakers to determine whether variations in syntax lead to slower or impeded intercomprehension of Slav texts. |
BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification (2022.lrec-1)
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| Challenge: | Large-scale monolingual pre-trained language models have shown promising results for high-resource as well as lowresource languages, especially for text classification. |
| Approach: | They provide a set of recommendations for using pre-trained models for Sinhala text classification and introduce new annotated datasets useful for future research. |
| Outcome: | The proposed models are far superior to existing models for Sinhala and set a strong baseline for text classification when fine-tuned. |
Pre-training and Evaluating Transformer-based Language Models for Icelandic (2022.lrec-1)
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| Challenge: | Pre-trained models obtain state-of-the-art performance on a wide variety of NLP tasks, including Question Answering (QA), Named Entity Recognition (NER), Part-of Speech (POS) tagging and Automatic Text Summarization (ATS). |
| Approach: | They pre-train four types of monolingual ELECTRA and ConvBERT models and compare them to a previously trained monolingual RoBERTa model and multilingual mBERT model. |
| Outcome: | The models outperform a multilingual model on four downstream tasks. |