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Constructing Multimodal Language Learner Texts Using LARA: Experiences with Nine Languages (2020.lrec-1)

Challenge:	Existing studies show that human-like prompts with quotes benefit both CLIP and GPT-3 as implicit word sense disambiguation components.
Approach:	They propose using the GPT-3 as a query generator for the backend of CLIP as an implicit word sense disambiguation component for the SemEval 2023 shared task Visual Word Sense Disambiguation.
Outcome:	The proposed query generator for CLIP is an implicit word sense disambiguation component for the SemEval 2023 shared task Visual Word Sense Disambiguation (VWSD). human-like prompts adapted for WSD with quotes benefit both CLIP and GPT-3, whereas plain phrases or poorly templated prompts give the worst results.

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Elham Akhlaghi, Branislav Bédi, Fatih Bektaş, Harald Berthelsen, Matthias Butterweck, Cathy Chua, Catia Cucchiarin, Gülşen Eryiğit, Johanna Gerlach, Hanieh Habibi, Neasa Ní Chiaráin, Manny Rayner, Steinþór Steingrímsson, Helmer Strik

Multimodality

Challenge:	LARA is an open source project that aims to support easy conversion of plain texts into online versions suitable for use by language learners.
Approach:	They propose to support easy conversion of plain texts into online versions suitable for use by language learners.
Outcome:	The proposed platform is suitable for creating texts in multiple languages via crowdsourcing techniques that can be used for teaching a language via reading and listening.

XDoc: Unified Pre-training for Cross-Format Document Understanding (2022.findings-emnlp)

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Jingye Chen, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei

Restoring Hebrew Diacritics Without a Dictionary (2022.findings-naacl)

Challenge:	Existing pre-trained models target one document format at a time, making it difficult to combine knowledge from multiple document formats.
Approach:	They propose a unified pre-trained model which deals with different document formats in a single model.
Outcome:	The proposed model achieves comparable or even better performance on a variety of downstream tasks compared with the individual pre-trained models.

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Elazar Gershuni, Yuval Pinter

Challenge:	a number of modern Hebrew texts are written in a letter-only version of the Hebrew script, which omits the diacritics present in the full diacritized, or dotted variant.
Approach:	They propose a character-level LSTM that can accurately diacritize Hebrew script without human-curated resources.
Outcome:	The proposed model performs on par with complex curation-dependent systems across a diverse array of modern Hebrew sources.

Towards Robust Real-World Spreadsheet Understanding with Multi-Agent Multi-Format Reasoning (2026.acl-long)

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Houxing Ren, Mingjie Zhan, Zimu Lu, Ke Wang, Yunqiao Yang, Haotian Hou, Hongsheng Li

Robustness in NLP

Challenge:	Spreadsheets are among the most widely used data formats in real-world applications . existing large language models treat tables as plain text, overlooking layout cues and visual semantics.
Approach:	They propose a two-stage multi-agent framework for spreadsheet understanding that adopts a step-by-step reading and reasoning paradigm.
Outcome:	Extensive experiments on two spreadsheet datasets show the proposed framework outperforms existing methods on Spreadsheet Bench.

Announcing the Prague Discourse Treebank 3.0 (2024.lrec-main)

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Pavlína Synková, Jiří Mírovský, Lucie Poláková, Magdaléna Rysová

Teaching Machine Comprehension with Compositional Explanations (2020.findings-emnlp)

Challenge:	PDiT 3.0 contains 21,662 discourse relations (plus 445 list relations) in 49 thousand sentences.
Approach:	They present the Prague Discourse Treebank 3.0, a new version of the annotation of discourse relations marked by primary and secondary discourse connectives in the Prague Dependency Treebank.
Outcome:	The new version of the PDiT 3.0 brings a largely revised annotation of discourse relations and achieves consistency with a Lexicon of Czech Discourse Connectives (CzeDLex) and sense taxonomy.

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Qinyuan Ye, Xiao Huang, Elizabeth Boschee, Xiang Ren

Explainability & Interpretability in NLP

Challenge:	Recent advances in machine reading comprehension rely heavily on large-scale annotated corpora, which are timeconsuming and costly to collect.
Approach:	They propose to use semi-structured explanations to “teach” machines reading comprehension using a small number of semi-structural explanations that explicitly inform machines why answer spans are correct.
Outcome:	The proposed method achieves 70.14% F1 score with supervision from 26 explanations on the SQuAD dataset, comparable to plain supervised learning using 1,100 labeled instances yielding a 12x speed up.

Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation (2020.coling-main)

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Ruizhe Li, Xiao Li, Guanyi Chen, Chenghua Lin

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

Challenge:	Variational Autoencoders (VAEs) have been widely used in text modelling but posterior collapse is a problem when RNN-based models are employed.
Approach:	They propose a timestep-wise regularisation VAE architecture which can effectively avoid posterior collapse when used in text modelling.
Outcome:	The proposed model avoids posterior collapse and can be applied to any RNN-based VAE model.

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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

Multimodality

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.

A Framework for Understanding the Role of Morphology in Universal Dependency Parsing (D18-1)

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Mathieu Dehouck, Pascal Denis

Rethinking Skip Connection with Layer Normalization (2020.coling-main)

Challenge:	a measure of morphological complexity is used to characterize syntactic information in word embeddings.
Approach:	They propose a measure of morphological complexity in terms of governor-dependent preferential attachment that explains parsing performance.
Outcome:	The proposed framework improves parsing performance on morphologically rich languages using morphology as a syntactic marker.

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Fenglin Liu, Xuancheng Ren, Zhiyuan Zhang, Xu Sun, Yuexian Zou

Challenge:	Existing methods to solve the optimization problem of deep neural networks are not linear, but can be used as a modulating mechanism between the input and output.
Approach:	They propose to use skip connection to adjust the scale of the input and output to improve the performance.
Outcome:	The proposed approach improves performance and convergence of deep neural networks and can be applied to machine translation and image classification datasets.

Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based Tasks (2025.findings-acl)

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Yuanjie Lyu, Chao Zhang, Yuhao Chen, Yong Chen, Tong Xu

TextObfuscator: Making Pre-trained Language Model a Privacy Protector via Obfuscating Word Representations (2023.findings-acl)

Challenge:	Recent advances in "Chain of Models" approach increase resource demands as each model must be deployed separately.
Approach:	They propose a prompt-tuning method that enables models to share hidden states . they modify input and attention masks during training to eliminate redundant forward passes .
Outcome:	Empirical results show that FTHSS matches the performance of traditional model chains while improving inference efficiency.

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Xin Zhou, Yi Lu, Ruotian Ma, Tao Gui, Yuran Wang, Yong Ding, Yibo Zhang, Qi Zhang, Xuanjing Huang

A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization (2022.acl-long)

Challenge:	Existing inference services are plagued by privacy concerns, such as sharing sensitive data with service providers.
Approach:	They propose a framework for protecting inference privacy by applying random perturbations to clustered representations.
Outcome:	The proposed framework protects inference privacy by applying random perturbations to clustered representations.

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Jacob Parnell, Inigo Jauregi Unanue, Massimo Piccardi

Text Generation

Challenge:	Multi-document summarization models are limited by limited references and with maximum-likelihood objectives.
Approach:	They propose to fine-tune an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents.
Outcome:	The proposed model improves on the Multi-News and WCEP datasets with a low-variance estimator . the proposed model also improves the coverage of the input documents .

Cost-Effective Discourse Annotation in the Prague Czech–English Dependency Treebank (2024.lrec-main)

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Jiří Mírovský, Pavlína Synková, Lucie Polakova, Marie Paclíková

GEM: Gestalt Enhanced Markup Language Model for Web Understanding via Render Tree (2023.emnlp-main)

Challenge:	a method for obtaining a high-quality annotation of explicit discourse relations is a resource-demanding task.
Approach:	They propose a method for obtaining a high-quality annotation of explicit discourse relations in the Czech part of the Prague Czech–English Dependency Treebank.
Outcome:	The proposed method solves the problem of identifying discrepancies between the annotations in the Czech part of the Penn Treebank.

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Zirui Shao, Feiyu Gao, Zhongda Qi, Hangdi Xing, Jiajun Bu, Zhi Yu, Qi Zheng, Xiaozhong Liu

Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables (2022.naacl-main)

Challenge:	Existing models that use plain HTMLs do not include crucial visual information in the rendered web.
Approach:	They propose a Gestalt Enhanced Markup Language Model for hosting visual information without visual input.
Outcome:	The proposed model can handle multiple downstream tasks without visual input.

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Nan Hu, Zirui Wu, Yuxuan Lai, Xiao Liu, Yansong Feng

Fact & Claim Verification

Challenge:	Existing fact extraction and verification tasks only consider evidence of a single format . Existing models convert evidence into either sentences or tables, thus losing context information .
Approach:	They propose a Dual Channel Unified Format fact verification model which unifies various evidence into parallel streams, i.e., natural language sentences and a global evidence table, simultaneously.
Outcome:	The proposed model outperforms existing models in two formats by a large margin . it makes the most of existing tables and tables to absorb evidence of two formats .

Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning (2020.emnlp-main)

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Wanyun Cui, Guangyu Zheng, Wei Wang

Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (2020.acl-main)

Challenge:	a recent study shows that humans are not supervised by the natural language inference .
Approach:	They propose to solve the natural language inference problem via task-agnostic multimodal pretraining.
Outcome:	The proposed network outperforms fully-supervised BiLSTM and BiLS+ELMO on plain text inference datasets.

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Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, Peter Szolovits

Text Generation

Challenge:	Current summarization systems only produce plain, factual headlines, far from the practical needs for exposure and memorableness of the articles.
Approach:	They propose a task to generate relevant headlines with three style options . they propose combining summarization and reconstruction tasks into a multitasking framework .
Outcome:	The proposed method outperforms the state-of-the-art summarization model by 9.68% . it can generate relevant, fluent headlines with humor, romance and clickbait .

Building the Spanish-Croatian Parallel Corpus (2020.lrec-1)

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Bojana Mikelenić, Marko Tadić

Machine Translation

Challenge:	The first unidirectional parallel corpus spanish-croatia was built at the Faculty of Humanities and Social Sciences of the University of Zagreb.
Approach:	They describe the building of the first Spanish-Croatian unidirectional parallel corpus at the Faculty of Humanities and Social Sciences of the University of Zagreb.
Outcome:	The proposed corpus is a bilingual unidirectional (SpanishCroatian) parallel corpus . it contains 11 Spanish novels and their translations to Croatian done by six translators .

Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation (2020.emnlp-main)

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Tuhin Chakrabarty, Smaranda Muresan, Nanyun Peng

Text Generation

Challenge:	Literary tropes are at the crux of human imagination and communication.
Approach:	They propose to automatically transform similes from reddit to their literal counterparts using common sense knowledge to generate simile models.
Outcome:	The proposed method generates 88% novel similes that do not share properties with training data.

TextFusion: Privacy-Preserving Pre-trained Model Inference via Token Fusion (2022.emnlp-main)

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Xin Zhou, Jinzhu Lu, Tao Gui, Ruotian Ma, Zichu Fei, Yuran Wang, Yong Ding, Yibo Cheung, Qi Zhang, Xuanjing Huang

Responsible & Trustworthy NLP

Challenge:	Existing methods to preserve inference privacy are available as cloud services . however, the risk of privacy leakage remains, according to recent studies .
Approach:	They propose a method to preserve inference privacy by fusing token representations in the cloud.
Outcome:	The proposed method preserves inference privacy without sacrificing performance on different scenarios.

Understanding Faithfulness and Reasoning of Large Language Models on Plain Biomedical Summaries (2024.findings-emnlp)

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Biaoyan Fang, Xiang Dai, Sarvnaz Karimi

Enhancing Extractive Question Answering in Multiparty Dialogues with Logical Inference Memory Network (2025.coling-main)

Challenge:	Generating plain biomedical summaries with Large Language Models (LLMs) can enhance access to biomedically knowledge.
Approach:	They propose a benchmark dataset with expert-annotated Faithfulness and Reasoning on plain biomedical summaries.
Outcome:	The proposed dataset shows that LLMs perform poorly in generating faithful biomedical summaries and that abstractiveness and faithfulness are negatively correlated.

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Shu Zhou, Rui Zhao, Zhengda Zhou, Haohan Yi, Xuhui Zheng, Hao Wang

Natural Language Interfaces

Challenge:	Existing models for multiparty dialogue question answering (QA) do not consider logical inference relations in multiparty dialogs, leading to suboptimal performance.
Approach:	They propose a memory network with logical inference for extractive QA in multiparty dialogues.
Outcome:	The proposed model achieves state-of-the-art on Molweni and FriendsQA benchmarks.

Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation (P19-1)

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Yftah Ziser, Roi Reichart

Low-Resource NLP

Challenge:	Existing approaches to domain adaptation (DA) require labeled data that can be found in only a handful of domains.
Approach:	They propose a task-refinement learning approach to solve pivot detection problems . they propose to train PBLM models with gradually increasing information exposed about each pivot .
Outcome:	The proposed approach achieves state-of-the-art accuracy in six domain adaptation setups for sentiment classification.

Exploring Graph Representations of Logical Forms for Language Modeling (2025.findings-acl)

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Michael Sullivan

ProofInfer: Generating Proof via Iterative Hierarchical Inference (2022.emnlp-main)

Challenge:	Graph-based formal-logical distributional semantics models are more data-efficient than textual counterparts.
Approach:	They propose a pretrained language model over graph representations of logical forms as a proof-of-concept.
Outcome:	The proposed model outperforms textual, transformer LMs on downstream tasks . the model is likely to scale with additional parameters and pretraining data .

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Zichu Fei, Qi Zhang, Xin Zhou, Tao Gui, Xuanjing Huang

Reasoning

Challenge:	Existing proof generation models focus on generating several proof paths instead of a whole tree.
Approach:	They propose a method that generates the proof tree via iterative hierarchical inference . they propose coding the proof as plain text without losing structure information .
Outcome:	The proposed proof generation model significantly improves performance on widely-used datasets.

Exposing Privacy Risks in Graph Retrieval-Augmented Generation (2026.findings-acl)

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Jiale Liu, Jiahao Zhang, Suhang Wang

Responsible & Trustworthy NLP

Challenge:	Large Language Models (LLMs) have limitations such as generating factually incorrect information (hallucinations) Retrieval-Augmented Generation (RAG) is a powerful paradigm for enhancing LLMs with external, up-to-date knowledge.
Approach:	They investigate the data extraction vulnerabilities of Graph RAG systems by executing tailored attacks on them.
Outcome:	The proposed attacks exploit the vulnerability of Graph RAG systems to leak raw text and structured data.

From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation (2026.findings-acl)

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Xueran Han, Yuhan Liu, Mingzhe Li, Wei Liu, Sen Hu, Rui Yan, Zhiqiang xu, Xiuying Chen

LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models (2024.findings-emnlp)

Challenge:	Novels create rich, immersive worlds with intricate plots and distinct styles, captivating readers through complex storytelling.
Approach:	They propose a novel generation system that imitates novel elements by predicting plot developments and writing concrete details using vivid, expressive language.
Outcome:	The novel imitative novel generation system is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence.

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Dustin Wright, Arnav Arora, Nadav Borenstein, Srishti Yadav, Serge Belongie, Isabelle Augenstein

Motion Capture Analysis of Verb and Adjective Types in Austrian Sign Language (ÖGS) (2024.lrec-main)

Challenge:	Existing approaches to evaluate latent values and opinions in large language models suffer from three notable shortcomings.
Approach:	They propose to analyze 156k LLM responses to 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations.
Outcome:	The proposed analysis of 156k LLM responses to the Political Compass Test (PCT) generated by 6 LLMs shows that tropes are recurrent and consistent across prompts.

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Julia Krebs, Evguenia A. Malaia, Isabella Fessl, Hans-Peter Wiesinger, Dietmar Roehm, Ronnie Wilbur, Hermann Schwameder