RECOGNITION

Disambiguatory Signals are Stronger in Word-initial Positions (2021.eacl-main)

Challenge:	Existing studies show that speech recognition systems depend on multiple factors including lexical content, speaker identity and dialect.
Approach:	They propose a method that evaluates the impact of emotion on recognition even when manual transcripts are not available.
Outcome:	The proposed method allows to evaluate the impact of emotion on recognition even when manual transcripts are not available.

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Tiago Pimentel, Ryan Cotterell, Brian Roark

Challenge:	lexical studies of word processing and lexicon access provide evidence of preferred nature of word-initial versus word-final segments . conjecture that languages have evolved to provide more information earlier in words is based on existing methods .
Approach:	They propose to use a new method to assess the informativeness of word-initial versus word-final segments.
Outcome:	The proposed measures avoid the confounds found in existing methods.

NavHint: Vision and Language Navigation Agent with a Hint Generator (2024.findings-eacl)

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Yue Zhang, Quan Guo, Parisa Kordjamshidi

BiomedCurator: Data Curation for Biomedical Literature (2022.aacl-demo)

Challenge:	Existing work on vision and language navigation relies on navigation-related losses to establish the connection between vision and modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment.
Approach:	They propose to provide indirect supervision to the navigation agent through a hint generator that generates visual descriptions during navigation.
Outcome:	The proposed method improves the navigation performance and interpretability of the R2R and R4R datasets.

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Mohammad Golam Sohrab, Khoa N.A. Duong, Ikeda Masami, Goran Topić, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Nogami Itoh, Hiroya Takamura

Information Retrieval

Challenge:	BiomedCurator uses state-of-the-art natural language processing techniques to extract structured data from scientific articles.
Approach:	They propose a web application that extracts structured data from PubMed and ClinicalTrials.gov . the application uses a combination of natural language processing techniques and a pattern-based extraction approach .
Outcome:	The proposed system extracts the structured data from PubMed and ClinicalTrials.gov datasets.

Familiar words but strange voices: Modelling the influence of speech variability on word recognition (2021.eacl-srw)

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Alexandra Mayn, Badr M. Abdullah, Dietrich Klakow

Aspect Extraction Using Coreference Resolution and Unsupervised Filtering (2020.aacl-srw)

Challenge:	Despite the lack of acoustic-phonetic invariance in speech, listeners can reliably recognize spoken words despite the lack aural-phonemic invariancy.
Approach:	They propose a deep neural model which is trained to retrieve the meaning of a word given its spoken form, a task which resembles that faced by a human listener.
Outcome:	The proposed model is more sensitive to dialectical variation than gender variation and more related to related languages.

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Deon Mai, Wei Emma Zhang

Fingerspelling within Sign Language Translation (2025.naacl-long)

Challenge:	Existing approaches to extract aspects from text are supervised and unsupervised . experimental results show that unsupervised approaches are more accurate than supervised ones .
Approach:	They propose to combine a lexical rule-based approach with coreference resolution to improve accuracy.
Outcome:	The proposed approach outperforms baseline methods on two benchmark datasets.

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Garrett Tanzer

Machine Translation

Challenge:	Prior work has studied fingerspelling recognition, but little attention has been paid to how well models understand it in context of entire sentences.
Approach:	They annotate instances of fingerspelling within FLEURS-ASL and use it to evaluate how well translation models understand it.
Outcome:	The proposed model family significantly improves understanding of fingerspelling, but the effect of the mixed model is mixed.

Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature (D19-57)

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Jihang Mao, Wanli Liu

Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties (2024.eacl-long)

Challenge:	BB system is among the top two systems in five of all six subtasks . knowledge about microbial diversity is crucial for the study of microbiome and bacteria .
Approach:	They present a system that uses word embedding and lexical features to perform entities recognition, normalization and relation extraction.
Outcome:	The proposed system achieves state-of-the-art in five of six subtasks and is among the top two in five.

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Ekaterina Artemova, Verena Blaschke, Barbara Plank

Robustness in NLP

Challenge:	Mainstream cross-lingual task-oriented dialogue systems often overlook the transfer to lower-resource colloquial varieties due to limited test data.
Approach:	They propose to train a model for intent recognition and slot-filling in English and apply it to other languages.
Outcome:	The proposed model performs better than existing models on English and other languages.

MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition (2025.acl-srw)

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Shanshan Liu, Noriki Nishida, Rumana Ferdous Munne, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto

Semantic Text Processing

Challenge:	Existing concepts recognition methods that rely on explicit mention identification fail to capture complex concepts not explicitly stated in the text.
Approach:	They propose a framework that reformulates concept recognition as an indexing-recognition task.
Outcome:	The proposed framework reduces computational requirements and improves recognition efficiency in low-resource settings.

SCoNE: a Self-Correcting and Noise-Augmented Method for Complex Biological and Chemical Named Entity Recognition (2026.eacl-long)

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Xingyu Zhu, Claire Nédellec, Balazs Nagy, Laszlo Vidacs, Robert Bossy

Negation Detection in Dutch Spoken Human-Computer Conversations (2022.lrec-1)

Challenge:	Named Entity Recognition (NER) is a fundamental task aimed at identifying entities such as names, locations, and organizations.
Approach:	They propose a self-correcting and noise-augmented method for complex Biological and Chemical Named Entity Recognition that improves learning diversity and confidence.
Outcome:	The proposed method outperforms baseline methods on CHEMDNER and microbial ecology datasets by 1.80 and 2.73 F1-scores.

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Tom Sweers, Iris Hendrickx, Helmer Strik

What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition (2021.tacl-1)

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.

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Piotr Żelasko, Raghavendra Pappagari, Najim Dehak

SoulChat: Improving LLMs’ Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations (2023.findings-emnlp)

Challenge:	Existing punctuation in the transcripts has a massive effect on the models’ performance, and specific label set specificity does not affect dialog act segmentation performance.
Approach:	They apply two pre-trained transformer models to a conversation transcript as a sequence of dialog acts and achieve strong results on Switchboard Dialog Act and Meeting Recorder Dialog Act corpora.
Outcome:	The proposed models achieve 8.4% and 14.2% error rates on the Switchboard Dialog Act and Meeting Recorder Dialog Act corpora.

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Yirong Chen, Xiaofen Xing, Jingkai Lin, Huimin Zheng, Zhenyu Wang, Qi Liu, Xiangmin Xu

LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression (2025.findings-naacl)

Challenge:	Large language models (LLMs) are used in psychological counseling to provide universal advice.
Approach:	They constructed a multi-turn empathetic conversation dataset with 2 million samples . they found that the model's empathy ability is enhanced when finetuning .
Outcome:	Experiments show that large language models can be finetuned to provide empathy . but, when applied to mental health or emotional support conversation, there are three main issues .

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Souvik Kundu, Anahita Bhiwandiwalla, Sungduk Yu, Phillip Howard, Tiep Le, Sharath Nittur Sridhar, David Cobbley, Hao Kang, Vasudev Lal

Time-LlaMA: Adapting Large Language Models for Time Series Modeling via Dynamic Low-rank Adaptation (2025.acl-srw)

Challenge:	LVLMs have been shown to perform well on simple uni-modal benchmarks, but their detailed study on multi-modal models is still lacking.
Approach:	They propose a framework to analyze the impact of compression on LVLMs on multi-modal input driven tasks.
Outcome:	The proposed framework analyzes the impact of compression on generative performance of large vision language models on multi-modal input driven tasks.

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Juyuan Zhang, Jiechao Gao, Wenwen Ouyang, Wei Zhu, Hui Yi Leong

Semantic Text Processing

Challenge:	Recent studies have demonstrated that large language models possess robust pattern recognition and semantic understanding capabilities over time series data.
Approach:	They propose a time series model that converts time series input into token embeddings and aligns time sequence embeddables with text prompts.
Outcome:	The proposed framework achieves the state-of-the-art (SOTA) performance and has potentials for wide industrial usages.

Unified Speech-Text Pre-training for Speech Translation and Recognition (2022.acl-long)

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Yun Tang, Hongyu Gong, Ning Dong, Changhan Wang, Wei-Ning Hsu, Jiatao Gu, Alexei Baevski, Xian Li, Abdelrahman Mohamed, Michael Auli, Juan Pino

Text Generation

Challenge:	Existing methods to pre-train speech and text use unlabeled data to learn universal feature representations.
Approach:	They propose a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition.
Outcome:	The proposed method achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task.

Learning Robust and Multilingual Speech Representations (2020.findings-emnlp)

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Kazuya Kawakami, Luyu Wang, Chris Dyer, Phil Blunsom, Aaron van den Oord

ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction (2025.emnlp-industry)

Challenge:	Unsupervised speech representation learning has shown success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance.
Approach:	They evaluate unsupervised speech representation learning representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages.
Outcome:	The proposed representations improve the recognition performance in 25 phonetically diverse languages and are robust to domain shifts.

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Victor Junqiu Wei, Weicheng Wang, Di Jiang, Yuanfeng Song, Lu Wang

Searching for fingerspelled content in American Sign Language (2022.acl-long)

Challenge:	Automatic Speech Recognition (ASR) systems have a substantial number of erroneous recognition due to environmental noise, ambiguity, etc.
Approach:	They use a benchmark dataset to analyze ASR errors in the Chinese language . they then apply large language models to correct ASR error correction .
Outcome:	The proposed method is based on a dataset of ASR errors in the Chinese language . it shows prompting is not effective for ASR error correction .

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Bowen Shi, Diane Brentari, Greg Shakhnarovich, Karen Livescu

EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models (2024.findings-acl)

Challenge:	Sign language processing is gaining interest in the computer vision and natural language processing communities.
Approach:	They propose a model that jointly detects fingerspelling and matches it to a text sequence.
Outcome:	The proposed model outperforms baseline methods on a large public dataset of ASL fingerspelling in the wild.

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Yuyan Chen, Songzhou Yan, Sijia Liu, Yueze Li, Yanghua Xiao

Discriminative Reasoning for Document-level Relation Extraction (2021.findings-acl)

Challenge:	Existing evaluations of emotional intelligence in large language models (LLMs) focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs’ overall emotional intelligence.
Approach:	They propose a framework for evaluating the emotional intelligence of large language models (LLMs) that includes four distinct tasks: Key Event Recognition, Mixed Event Recognition and Implicit Emotional Recognition.
Outcome:	The proposed framework includes four distinct tasks: Key Event Recognition, Mixed Event Recognition and Implicit Emotional Recognition.

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Wang Xu, Kehai Chen, Tiejun Zhao

Corpora and Baselines for Humour Recognition in Portuguese (2020.lrec-1)

Challenge:	Existing models use graph networks to implicitly model reasoning skills . but it is yet to be seen whether modeling these reasoning skills implicitly is competitive with intuitive reasoning skills between one entity pair in this document.
Approach:	They propose a discriminative reasoning framework to explicitly model the paths of reasoning skills between entity pairs in a document.
Outcome:	The proposed method outperforms the previous state-of-the-art on the large-scale DocRE dataset.

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Hugo Gonçalo Oliveira, André Clemêncio, Ana Alves

Sentiment Analysis

Challenge:	Existing work on the recognition of verbal humour in Portuguese has not been done . humor recognition is a sign of fluency in a language, and is not yet widely used in other languages.
Approach:	They propose to create three corpora covering two styles of humour and four sources of non-humorous text that are used for testing computational models.
Outcome:	The proposed models can be used to train and test models in Portuguese, and may be used as baselines for future projects.

Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning (2021.emnlp-main)

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Da Yin, Liunian Harold Li, Ziniu Hu, Nanyun Peng, Kai-Wei Chang

Reasoning

Challenge:	Generally, commonsense knowledge is correlated with culture and geographic locations and is only shared locally.
Approach:	They construct a Geo-Diverse Visual Commonsense Reasoning dataset to test vision-and-language models’ ability to understand cultural and geo-location-specific commonsense.
Outcome:	The proposed models perform better in non-Western regions including East Asia, South Asia, and Africa than in the Western regions.

Distill Visual Chart Reasoning Ability from LLMs to MLLMs (2025.findings-emnlp)

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Wei He, Zhiheng Xi, Wanxu Zhao, Xiaoran Fan, Yiwen Ding, Zifei Shan, Tao Gui, Qi Zhang, Xuanjing Huang

Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs (C18-1)

Challenge:	a new method for generating chart annotations is proposed to improve visual reasoning in multimodal large language models.
Approach:	They propose a code-as-intermediary translation method for distilling visual reasoning abilities from LLMs to MLLMs.
Outcome:	The proposed method is cost-effective, efficient and scalable.

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Minh Nguyen, Thien Huu Nguyen

Measuring Mechanistic Independence: Can Bias Be Removed Without Erasing Demographics? (2026.eacl-long)

Challenge:	Existing methods to identify police killings from text have not been applied to this problem . et al., 2017: finding names of people killed by police is a critical problem despite public attention .
Approach:	They propose a method to deal with multiple appearances of police names in documents . they propose hierarchical LSTMs to model multiple sentences that contain names of interests .
Outcome:	The proposed method yields state-of-the-art performance for police killing detection . it relies on hierarchical LSTMs to model the multiple sentences that contain the person names of interests .

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Zhengyang Shan, Aaron Mueller

Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling (2024.lrec-main)

Challenge:	Using multi-task evaluation, we examine how independent demographic bias mechanisms are from general demographic recognition in language models.
Approach:	They compare attribution-based and correlation-based methods for locating bias features in language models to find out which features are independent from general demographic recognition.
Outcome:	The proposed method reduces bias without degrading recognition performance.

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Bo Xu, Longjiao Li, Wei Luo, Mehdi Naseriparsa, Zhehuan Zhao, Hongfei Lin, Feng Xia

Language Model is Suitable for Correction of Handwritten Mathematical Expressions Recognition (2023.emnlp-main)

Challenge:	Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers.
Approach:	They propose a method that incorporates both belief and desire to accurately identify emotions by extracting emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations.
Outcome:	The proposed model outperforms existing models on four popular ERC datasets and validates its performance with multiple state-of-the-art models.

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Zui Chen, Jiaqi Han, Chaofan Yang, Yi Zhou

Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language (2020.lrec-1)

Challenge:	Existing approaches to handwritten mathematical expression recognition are limited by CFGs and pre-generated triplet data.
Approach:	They propose an architecture that integrates recognition and language features to output corrected sequences while optimizing with a string decoder recognition model.
Outcome:	The proposed architecture outperforms state-of-the-art methods on CROHME datasets.

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Bernardo Magnini, Alberto Lavelli, Simone Magnolini

Multi-Modal Retrieval For Large Language Model Based Speech Recognition (2024.findings-acl)

Challenge:	Using available datasets, we compare deep learning and traditional machine learning methods for various NLP tasks in Italian.
Approach:	They compare deep learning and traditional machine learning methods for various NLP tasks in Italian.
Outcome:	The proposed methods outperform traditional methods in sequence tagging tasks and classification tasks in Italian.

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Aditya Gourav, Jari Kolehmainen, Prashanth Shivakumar, Yile Gu, Grant Strimel, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko

Automatic rule generation for time expression normalization (2021.findings-emnlp)

Challenge:	kNN-LM and cross-attention techniques are used to extend text based retrieval to other modalities . wide adoption of large language models has driven new application areas leveraging this technology .
Approach:	They propose to use kNN-LM and cross-attention techniques to extend text retrieval methods to other modalities.
Outcome:	The proposed methods outperform text-based retrieval and improve word error rate on a speech recognition dataset.

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Wentao Ding, Jianhao Chen, Jinmao Li, Yuzhong Qu

Challenge:	Existing SOTA methods for normalization rely on expert-designed rules or grammars . current methods are domain sensitive and not sufficient on emerging corpora .
Approach:	They propose a method that generates normalization rules from annotated data without expert intervention.
Outcome:	The proposed method surpasses existing rule-based methods on the Tweets benchmark and on the TempEval-3 benchmark.

Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path (2021.acl-long)

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Yiran Wang, Hiroyuki Shindo, Yuji Matsumoto, Taro Watanabe

MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)

Challenge:	Existing methods for named entity recognition assume entities are not nested within other entities, so-called flat NER.
Approach:	They propose a layered method for nested named entity recognition . they use a set of hidden states to exclude the influence of the best path .
Outcome:	The proposed method performs better on ACE2004, ACE2005, and GENIA datasets.

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Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech (2022.coling-1)

Challenge:	Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture.
Approach:	They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition.
Outcome:	The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module.

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Dawei Zhu, Qiusi Zhan, Zhejian Zhou, Yifan Song, Jiebin Zhang, Sujian Li

GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding (2023.emnlp-main)

Challenge:	Figures of speech often deviate from their literal meanings to express deeper semantic implications.
Approach:	They propose a concept of figurative unit, which is the carrier of a figure, and build a Chinese corpus for Contextualized Figure Recognition.
Outcome:	The proposed model is based on 12 types of figures commonly used in Chinese . it shows that the proposed tasks are challenging for existing models .

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Zekun Li, Wenxuan Zhou, Yao-Yi Chiang, Muhao Chen

Using Semantic Role Labeling to Improve Neural Machine Translation (2022.lrec-1)

Challenge:	Pretrained language models do not utilize valuable geospatial information in large databases, e.g., OpenStreetMap.
Approach:	They propose a geospatially grounded language model that connects linguistic and geospheric contexts.
Outcome:	The proposed model bridges the gap between natural language processing and geospatial sciences.

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Reinhard Rapp

Multilinguality

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.

Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling (2023.emnlp-main)

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Yuanjun Shi, Linzhi Wu, Minglai Shao

M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database (2022.acl-long)

Challenge:	Recent research on slot filling has witnessed considerable improvement with considerable data and label shifts.
Approach:	They propose an adaptive end-to-end metric learning scheme for zero-shot slot filling that uses context-aware soft label representations and slot-level contrastive representation learning to mitigate the data and label shift problems.
Outcome:	The proposed approach outperforms existing methods on public benchmarks and shows that it is simple, efficient and generalizable.

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Jinming Zhao, Tenggan Zhang, Jingwen Hu, Yuchen Liu, Qin Jin, Xinchao Wang, Haizhou Li

DocFusion: A Unified Framework for Document Parsing Tasks (2025.findings-acl)

Challenge:	Existing data resources to support multimodal affective analysis in dialogues are limited in scale and diversity.
Approach:	They propose a multimodal multi-scene multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series.
Outcome:	The proposed dataset contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances.

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Mingxu Chai, Ziyu Shen, Chong Zhang, Yue Zhang, Xiao Wang, Shihan Dou, Jihua Kang, Jiazheng Zhang, Qi Zhang

Challenge:	Existing methods for document parsing often employ multiple models, limiting performance . Existing models often employ discrete tokens, whereas recognition relies on continuous coordinates .
Approach:	They propose a Gaussian-Kernel Cross-Entropy Loss (GK-CEL) that unifies detection and recognition by enabling generative frameworks to handle both tasks simultaneously.
Outcome:	The proposed model performs competitively across four core document parsing tasks.

Construction of Large-scale English Verbal Multiword Expression Annotated Corpus (L18-1)

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Akihiko Kato, Hiroyuki Shindo, Yuji Matsumoto

Syntactic Text Processing

Challenge:	In this paper, we focus on verbal MWEs, whose accurate recognition is challenging because they could be discontinuous.
Approach:	They conduct large-scale annotations of VMWEs on the Wall Street Journal portion of Ontonotes . they first construct a VMwe dictionary based on the english-language Wiktionary .
Outcome:	The proposed resource annotates 7,833 VMWE instances belonging to various categories . the authors hope the results will help to develop models for MWE recognition and dependency parsing .

Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages (2020.lrec-1)

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Astik Biswas, Emre Yilmaz, Febe De Wet, Ewald Van der westhuizen, Thomas Niesler

SCANNER: Knowledge-Enhanced Approach for Robust Multi-modal Named Entity Recognition of Unseen Entities (2024.naacl-long)

Challenge:	Existing models for code-switching between languages are under-resourced and limited by text and acoustic data.
Approach:	They propose to construct four separate bilingual automatic speech recognisers corresponding to four different language pairs between which speakers switch frequently.
Outcome:	The proposed models are compared with a non-batch-wise approach and show that they perform better when used with sparse training data.

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Hyunjong Ok, Taeho Kil, Sukmin Seo, Jaeho Lee

I run as fast as a rabbit, can you? A Multilingual Simile Dialogues Datasets (2023.findings-acl)

Challenge:	Named entity recognition (NER) is a task to identify textual spans that correspond to named entities in the given text.
Approach:	They propose a model that can generalize to entities unseen during training and handle noisy annotations.
Outcome:	The proposed model outperforms existing methods on both MNER and GMNER benchmarks and is robust and accurate.

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Longxuan Ma, Wei-Nan Zhang, Shuhan Zhou, Churui Sun, Changxin Ke, Ting Liu

Natural Language Interfaces

Challenge:	A simile is a figure of speech that compares two different things via shared properties.
Approach:	They propose a multilingual simile dialogue dataset that can be used to study similes in real-life scenarios.
Outcome:	The proposed dataset is the largest manually annotated simile dataset and contains both English and Chinese data.

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

Information Retrieval

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.

Dynamic Spatial-Temporal Aggregation for Skeleton-Aware Sign Language Recognition (2024.lrec-main)

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Lianyu Hu, Liqing Gao, Zekang Liu, Wei Feng

Structured Data in NLP

Challenge:	Current sign language recognition methods use spatial graphs and temporal modules to capture spatial and temporal features, but their spatial graph modules are typically built on fixed graph structures.
Approach:	They propose a new spatial architecture that captures input-sensitive joint relationships and a temporal module to model multi-scale temporal information to capture complex human dynamics.
Outcome:	The proposed method achieves state-of-the-art accuracy on four large-scale SLR benchmarks.

Evaluation Phonemic Transcription of Low-Resource Tonal Languages for Language Documentation (L18-1)

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Oliver Adams, Trevor Cohn, Graham Neubig, Hilaria Cruz, Steven Bird, Alexis Michaud

New Datasets and Controllable Iterative Data Augmentation Method for Code-switching ASR Error Correction (2023.findings-emnlp)

Challenge:	Language documentation involves recording the speech of native speakers.
Approach:	They propose to use a neural network architecture to model phonemes and tones versus modelling them separately.
Outcome:	The proposed method improves efficiency, minimizes typographical errors and maintains transcription faithfulness to acoustic signal while highlighting phonetic and phonemic facts for linguistic consideration.

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Zhaohong Wan, Xiaojun Wan, Wei Peng, Rongjun Li

Can Vision-Language Models Solve Visual Math Equations? (2025.emnlp-main)

Challenge:	In bilingual or multilingual settings, code-switching ASR has greater challenges and research value.
Approach:	They propose a controllable iterative method for improving the performance of mainstream automatic speech recognition systems by using Chinese-English code-switching dialogues.
Outcome:	The proposed method achieves the best performance compared with the rule-based, back-translation-based data augmentation methods and large language model ChatGPT.

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Monjoy Narayan Choudhury, Junling Wang, Yifan Hou, Mrinmaya Sachan

Visual Data in NLP

Challenge:	Vision-Language Models (VLMs) perform well on textual equations, but fail on visually grounded counterparts.
Approach:	They propose to decompose visual equation solving into symbolic equation solving and visual recognition into two core components to understand this gap.
Outcome:	The proposed models perform well on textual equations, but fail on visual grounded ones.

Visualizing the “Dictionary of Regionalisms of France” (DRF) (L18-1)

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Ada Wan

JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language Processing (2023.findings-emnlp)

Challenge:	a corpus of regionalisms, parts of speech and recognition rates is published in the Dictionnaire des Régionalismes de France.
Approach:	They propose to curate and analyze the corpus of regionalisms published in the Dictionnaire des Régionalismes de France.
Outcome:	The corpus contains all entries in the DRF for which recognition rates were recorded . the analysis compares with previous work on regionalalisms and atlas .

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Shester Gueuwou, Sophie Siake, Colin Leong, Mathias Müller

Multilinguality

Challenge:	Existing sign language datasets are limited and skewed towards high-income sign languages, mainly those from high-risk countries.
Approach:	They propose a large and highly multilingual dataset for sign language translation: JWSign.
Outcome:	The proposed dataset consists of 2,530 hours of Bible translations in 98 sign languages, featuring more than 1,500 individual signers.

Improving Classroom Dialogue Act Recognition from Limited Labeled Data with Self-Supervised Contrastive Learning Classifiers (2023.findings-acl)

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Vikram Kumaran, Jonathan Rowe, Bradford Mott, Snigdha Chaturvedi, James Lester

Semantic Text Processing

Challenge:	Recognizing classroom dialogue acts has significant promise for yielding insight into teaching, student learning, and classroom dynamics.
Approach:	They propose to use a contrastive learning-based self-supervised approach to improve classroom dialogue act recognition from limited labeled data by increasing the accuracy of dialogue act recognization and minimizing embedding distance between the same dialogue acts.
Outcome:	The proposed model outperforms baseline models when trained with limited examples per dialogue act and outperformed other few-shot models that require considerably more labeled data.

Rationalizing Medical Relation Prediction from Corpus-level Statistics (2020.acl-main)

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Zhen Wang, Jennifer Lee, Simon Lin, Huan Sun

Linguistic Theories

Challenge:	Existing work on predicting relations based on text corpus has focused on analyzing raw texts mentioning two entities.
Approach:	They propose a framework that can be used to rationalize medical relation prediction . they recall contexts associated with the target entities and recognize relational interactions between them .
Outcome:	The proposed framework can achieve competitive predictive performance against a comprehensive list of neural baseline models, and present rationales to justify its prediction.

An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition (2020.lrec-1)

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Sandrine Tornay, Oya Aran, Mathew Magimai Doss

Challenge:	despite the extensive use of HMMs for sign recognition, determining the HMM structure remains a challenge . despite their success in modeling sequential and multivariate data, establishing the structure remains challenging .
Approach:	They propose a continuous HMM framework for modeling and recognizing isolated signs . they propose to optimize the number of states for each sign separately during recognition .
Outcome:	The proposed model performs better on three different datasets and is competitive with existing models.

Searching Brazilian Twitter for Signs of Mental Health Issues (2020.lrec-1)

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Wesley Santos, Amanda Funabashi, Ivandré Paraboni

Information Retrieval

Challenge:	Existing resources are largely devoted to English NLP, and there is little support for these studies in under resourced languages.
Approach:	They propose to build a corpus in Brazilian Portuguese to support both the recognition of mental health issues and the temporal analysis of these illnesses.
Outcome:	The proposed corpus will support both the recognition of mental health issues and the temporal analysis of these illnesses in the Brazilian Portuguese language.

Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios (2026.findings-acl)

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Xiaomin Li, Tala Wang, Zichen Zhong, Ying Zhang, Zirui Zheng, Takashi Isobe, Dezhuang Li, Huchuan Lu, You He, Xu Jia

Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset (2026.acl-long)

Challenge:	Existing benchmarks focus on evaluating MLLMs’ pre-existing knowledge or perceptual understanding, often neglecting the critical capability of reasoning.
Approach:	They propose a benchmark designed for visual clue-driven reasoning in daily scenarios that combines rigorous grounding in authentic daily activities and challenging query design that necessitates more than surface-level perception.
Outcome:	The proposed benchmark identifies visual clues and their ability to provide robust reasoning in daily scenarios.

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Qian Chen, Xianyin Zhang, Yanzhi Liu, Lifan Guo, Feng Chen, Chi Zhang

MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training (2024.lrec-main)

Challenge:	Large Vision-Language Models (LVLMs) have expanded capabilities beyond text understanding . a novel Chinese financial multimodal evaluation benchmark is used to evaluate LVLM capabilities .
Approach:	They propose a Chinese financial multimodal evaluation benchmark to evaluate LVLMs' capabilities . the model has an overall accuracy of 66.11% and an average score of 77.18 .
Outcome:	The proposed model achieves an overall accuracy of 66.11% on the question answering task and an average score of 77.18 on detection, recognition, and information extraction tasks.

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Xiaojing Du, Hanjie Zhao, Danyan Xing, Yuxiang Jia, Hongying Zan

Does the Emotional Understanding of LVLMs Vary Under High-Stress Environments and Across Different Demographic Attributes? (2025.acl-long)

Challenge:	Experimental evaluations conducted on the CMeEE, a benchmark for Chinese nested medical named entity recognition (NER) model outperforms the compared state-of-the-art (SOTA) models.
Approach:	They propose a model based on machine reading comprehension that uses a task-adaptive pre-training strategy to improve the model’s capability in the medical field.
Outcome:	The proposed model outperforms the compared state-of-the-art models on the CMeEE, a benchmark for Chinese nested medical NER.

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Jaewook Lee, Yeajin Jang, Oh-Woog Kwon, Harksoo Kim

Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview (2024.lrec-main)

Challenge:	According to psychological and neuroscientific research, a high-stress environment can restrict attentional resources and intensify negative affect, thereby impairing the ability to understand emotions.
Approach:	They constructed a large-vision language model that combines race, gender, and age group and used the Pretend prompt technique to induce LVLMs to interpret others’ emotions.
Outcome:	The results suggest that the effects of high-stress and demographic attributes identified in human research may also be reflected in LVLMs.

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Heyang Liu, Yanfeng Wang, Yu Wang

Discourse-Driven Code-Switching: Analyzing the Role of Content and Communicative Function in Spanish-English Bilingual Speech (2025.emnlp-main)

Challenge:	End-to-end (E2E) models are replacing hybrid models for automatic speech recognition tasks.
Approach:	They propose a method to optimize E2E models for automatic speech recognition . they propose MED-IT, a multi-turn consultation speech dataset .
Outcome:	The proposed method improves on subsets of rare words appearing in training speech.

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Debasmita Bhattacharya, Juan Junco, Divya Tadimeti, Julia Hirschberg

Multilinguality

Challenge:	Prior work has shown that a range of speaker and listener attributes affect or correlate with the prevalence of code-switching during conversation.
Approach:	They analyze the names of entities and dialogue acts present in a Spanish-English spontaneous speech corpus and build a predictive model of CSW.
Outcome:	The proposed model is the first to take a discourse-sensitive approach to understanding pragmatic and referential cues of bilingual speech.

Chinese Inertial GAN for Handwriting Signal Generation and Recognition (2025.acl-long)

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Yifeng Wang, Yi Zhao

CogEmp:A Cognitive Empathy-Oriented Dialogue System for Structured Psychological Counseling (2026.findings-acl)

Challenge:	Inertial sensors can measure the acceleration and angular velocity of moving objects and are widely used in electronic devices such as smartphones, smartwatches, and fitness bands.
Approach:	They propose to use Chinese glyph encoding, forced optimal transport, and semantic relevance alignment to acquire unlimited training samples for Chinese inertial writing recognition.
Outcome:	The proposed system improves the performance of six widely used classifiers from 6.7% to 98.4%.

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Yiguo Deng, Xia Lei, Yuan Zhang, Long Ye

CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models (2025.emnlp-main)

Challenge:	Existing models lack accurate modeling of cognitive empathy, especially the ability to understand users’ emotions and their underlying psychological causes.
Approach:	They propose a model tailored for the Chinese cultural context that integrates cognitive empathy into LLMs.
Outcome:	The proposed model outperforms existing models in key evaluation metrics, particularly in empathy, comprehensibility, and professionalism.

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Aneesh Komanduri, Karuna Bhaila, Xintao Wu

InsAT: Instance-aware Semantic Alignment and Transfer from Human–Object Keypoints for Zero-to-Few-shot Action Understanding (2026.acl-long)

Challenge:	Large vision-language models have shown impressive ability in various language tasks, especially with their emergent in-context learning capability.
Approach:	They propose a causal reasoning benchmark for multi-modal in-context learning from large vision-language models that incorporates visual inputs.
Outcome:	The proposed model outperforms existing models on three visual causal reasoning tasks and demonstrates their strengths and weaknesses.

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Kazuki Tsutsukawa

SafetyALFRED: Evaluating Safety-Conscious Planning of Vision Language Models (2026.findings-acl)

Challenge:	Existing zero-shot (ZS) approaches emphasize human motion while underutilizing contextual information, particularly human–object interactions.
Approach:	They propose a framework for ZS recognition and zero-to-few-shot adaptation that leverages instance-level language descriptions.
Outcome:	The proposed framework outperforms keypoint-based ZS methods while remaining data-efficient and robust.

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Josue Torres-Fonseca, Naihao Deng, Yinpei Dai, Shane Storks, Yichi Zhang, Rada Mihalcea, Casey Kennington, Joyce Chai