MODELING

SynJax: Structured Probability Distributions for JAX (2023.emnlp-demo)

Challenge:	This project leverages advances in multimodal large language models to build an inclusive collaboration feedback loop for participants developing general collaboration skills.
Approach:	They propose to integrate advances in multimodal large language models into downstream tasks such as the learning analytics feedback loop.
Outcome:	The proposed model will be used to detect, model, and feedback participants developing general collaboration skills.

Copied to clipboard

Miloš Stanojević, Laurent Sartran

Challenge:	a number of deep learning libraries have been developed to account for structured objects . a vectorized implementation of inference algorithms for structured distributions is difficult to implement .
Approach:	SynJax provides vectorized implementations of inference algorithms for structured distributions . authors propose to use a vectorized version of the algorithms to model structure in data . similar structures appear in biology and chemistry .
Outcome:	SynJax provides an efficient vectorized implementation of inference algorithms for structured distributions.

FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary (2021.eacl-main)

Copied to clipboard

Terra Blevins, Mandar Joshi, Luke Zettlemoyer

MENLI: Robust Evaluation Metrics from Natural Language Inference (2023.tacl-1)

Challenge:	Existing models for Word Sense Disambiguation struggle to disambiguate rare senses . current models struggle to learn senses with few training examples .
Approach:	They introduce a low-shot WSD dataset automatically extracted from example sentences in Wiktionary.
Outcome:	The proposed dataset outperforms baseline models on rare senses in existing datasets.

Copied to clipboard

Yanran Chen, Steffen Eger

Responsible & Trustworthy NLP

Challenge:	Recent proposed BERT-based evaluation metrics for text generation are vulnerable to adversarial attacks, e.g., relating to information correctness.
Approach:	They propose to use BERT-based evaluation metrics for text generation to evaluate text for semantic similarity but are vulnerable to adversarial attacks using Natural Language Inference.
Outcome:	The proposed metrics outperform existing summarization metrics but perform below SOTA MT metrics on standard benchmarks.

Measuring the Effect of Influential Messages on Varying Personas (2023.acl-short)

Copied to clipboard

Chenkai Sun, Jinning Li, Hou Pong Chan, ChengXiang Zhai, Heng Ji

Sentiment Analysis

Challenge:	a new task estimates the response a persona might have upon seeing a news message . a first benchmark dataset is used to evaluate the performance of the proposed task .
Approach:	They propose a task to estimate the response a persona might have upon seeing a news message.
Outcome:	The proposed task estimates the response a persona might have upon seeing a news message.

Rigid Formats Controlled Text Generation (2020.acl-main)

Copied to clipboard

Piji Li, Haisong Zhang, Xiaojiang Liu, Shuming Shi

Text Generation

Challenge:	Neural text generation is a challenging task that requires rigid formats to be controlled . a framework called SongNet is designed to tackle this problem .
Approach:	They propose a framework to tackle a task called rigid formats controlled text generation . they propose rhyming schemes and a transformer-based auto-regressive language model to improve the modeling performance .
Outcome:	The proposed framework improves the performance on format, rhyme, and sentence integrity.

A Semi-supervised Scalable Unified Framework for E-commerce Query Classification (2025.acl-industry)

Copied to clipboard

Chunyuan Yuan, Chong Zhang, Zhen Fang, Ming Pang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law

Information Retrieval

Challenge:	Existing query classification methods rely on posterior click behavior to construct training samples, resulting in insufficient prior information for modeling.
Approach:	They propose a semi-supervised scaleable unified framework that integrates enhanced modules to unify query classification tasks.
Outcome:	The proposed framework outperforms the state-of-the-art models in offline and online A/B experiments.

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

Copied to clipboard

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

TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)

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.

Copied to clipboard

Yu Zhang, Ziyue Jiang, Ruiqi Li, Changhao Pan, Jinzheng He, Rongjie Huang, Chuxin Wang, Zhou Zhao

Low-Resource NLP

Challenge:	Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles.
Approach:	They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control.
Outcome:	Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability.

BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic Representation (2025.emnlp-main)

Copied to clipboard

Tianhao Zhang, Zhecheng Sheng, Zhexiao Lin, Chen Jiang, Dongyeop Kang

Representation Learning

Challenge:	Autoregressive generative models are gaining traction in language tasks such as text generation and machine translation.
Approach:	They propose a likelihood-based evaluation metric that fits transformer-based model embeddings into a stochastic process and propose it as a probability-based metric.
Outcome:	The proposed model embeddings induce a "clustered-to-temporal ordered" mapping of language model representations in high-dimensional space, and this structure enhances performance on tasks such as temporal consistency evaluation and AI-generated content detection.

Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts (2024.findings-naacl)

Copied to clipboard

Chenghao Yang, Tuhin Chakrabarty, Karli Hochstatter, Melissa Slavin, Nabila El-Bassel, Smaranda Muresan

Responsible & Trustworthy NLP

Challenge:	Experimental results show that identifying the phases of opioid use disorder is highly contextual and challenging.
Approach:	They analyze 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use . they annotate span-level extractive explanations and critically evaluate state-of-the-art models in a supervised, few-shot, or zero-shot setting.
Outcome:	The proposed models improve classification accuracy and quality of the extracted explanations.

Building Representative Corpora from Illiterate Communities: A Reviewof Challenges and Mitigation Strategies for Developing Countries (2021.eacl-main)

Copied to clipboard

Stephanie Hirmer, Alycia Leonard, Josephine Tumwesige, Costanza Conforti

Responsible & Trustworthy NLP

Challenge:	Existing methods for collecting data from high-income countries (HICs) make implicit assumptions about literacy and internet access, but in low-income and sub-Saharan Africa (SSA) such assumptions may not hold for LICs where the bulk of the population lives.
Approach:	They propose a set of practical mitigation strategies to address the under-representation of illiterate communities in NLP corpora.
Outcome:	The proposed methods address the under-representation of illiterate communities in NLP corpora and propose mitigation strategies to help future work.

Relation Extraction with Type-aware Map Memories of Word Dependencies (2021.findings-acl)

Copied to clipboard

Guimin Chen, Yuanhe Tian, Yan Song, Xiang Wan

Information Extraction & Text Mining

Challenge:	Existing studies focus on the dependency connections between words with limited attention paid to exploiting dependency types.
Approach:	They propose a neural approach for relation extraction with type-aware map memories . they map all associated words along with dependencies among them to memory slots .
Outcome:	The proposed approach achieves state-of-the-art on two English benchmark datasets.

Semantic Role Labeling with Heterogeneous Syntactic Knowledge (2020.coling-main)

Copied to clipboard

Qingrong Xia, Rui Wang, Zhenghua Li, Yue Zhang, Min Zhang

OLIVE: Object Level In-Context Visual Embeddings (2024.acl-long)

Challenge:	Recent work on incorporating syntactic knowledge into neural semantic role labeling has gained much attention . incorporating heterogeneous syntaktic knowledge brings significant improvements over strong baselines .
Approach:	They propose to encode heterogeneous syntactic knowledge for SRL from explicit and implicit representations from heterogenous treebanks.
Outcome:	The proposed approaches improve on two widely-used benchmark datasets.

Copied to clipboard

Timothy Ossowski, Junjie Hu

Speech-to-Speech Translation for a Real-world Unwritten Language (2023.findings-acl)

Challenge:	Existing vision-language models lack fine-grained object-level understanding and grounding . existing models implicitly align text tokens with image patch tokens, which is ineffective for embedding alignment at the same granularity and introduces noisy spurious background features.
Approach:	They propose a method to prompt large language models with in-context visual object vectors . this method allows for controllable object-level reasoning .
Outcome:	The proposed method achieves competitive referring object classification and captioning performance while offering zero-shot generalization and robustness to visually challenging contexts.

Copied to clipboard

Peng-Jen Chen, Kevin Tran, Yilin Yang, Jingfei Du, Justine Kao, Yu-An Chung, Paden Tomasello, Paul-Ambroise Duquenne, Holger Schwenk, Hongyu Gong, Hirofumi Inaguma, Sravya Popuri, Changhan Wang, Juan Pino, Wei-Ning Hsu, Ann Lee

OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)

Challenge:	a new study examines speech-to-speech translation (S2ST) that translates speech from one language into another . the research area for unwritten languages remains a research area with little exploration due to the lack of training data.
Approach:	They propose a system that translates speech from one language into another . they use Taiwanese Hokkien as an example of an unwritten language .
Outcome:	The proposed system can be used to train models in languages without standard writing systems.

Copied to clipboard

Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kübler, Lawrence Moss

Reasoning

Challenge:	Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets.
Approach:	They propose to use a Chinese dataset to generate annotated sentences from native speakers specializing in linguistics to elicit annotations.
Outcome:	The proposed dataset does not rely on automatic translation or non-expert annotation. instead, it elicits annotations from native speakers specializing in linguistics.

Depression Detection in Clinical Interviews with LLM-Empowered Structural Element Graph (2024.naacl-long)

Copied to clipboard

Zhuang Chen, Jiawen Deng, Jinfeng Zhou, Jincenzi Wu, Tieyun Qian, Minlie Huang

Revisiting Character-Based Neural Machine Translation with Capacity and Compression (D18-1)

Challenge:	Existing methods for assessing depression only capture part of relevant elements . scarcity of participant data constrains interview modeling due to privacy concerns .
Approach:	They propose a structural element graph (SEGA) that transforms clinical interviews into an expertise-inspired directed acyclic graph for comprehensive modeling.
Outcome:	The proposed model outperforms baseline methods and powerful LLMs on two real-world clinical datasets.

Copied to clipboard

Colin Cherry, George Foster, Ankur Bapna, Orhan Firat, Wolfgang Macherey

Multilinguality

Challenge:	Translating characters instead of words or word-fragments can simplify the processing pipeline but results in longer sequences .
Approach:	They propose to use sequence-to-sequence architectures of sufficient depth to solve the problem . they also evaluate the performance versus computation time tradeoffs they offer .
Outcome:	The proposed models outperform models operating over word fragments in character-level NMT, the authors show . they also show that the proposed models do not match the performance of their deep character baseline model .

Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments (2020.emnlp-main)

Copied to clipboard

Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Lyle Ungar

Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation (2024.findings-emnlp)

Challenge:	Existing work on dialect prediction is limited to coarse-grained varieties . a new language model, MARBERT, can predict micro-dialects with 9.9% F1, 76 better than a majority class baseline.
Approach:	They propose a new task of Micro-Dialect Identification (MDI) that can predict a fine-grained variety given a single message.
Outcome:	The proposed model predicts micro-dialects with 9.9% F1, 76 better than a majority class baseline.

Copied to clipboard

Xianzhi Li, Ran Zmigrod, Zhiqiang Ma, Xiaomo Liu, Xiaodan Zhu

Visual Modeling of Turkish Morphology (2020.lrec-1)

Challenge:	Language models can memorize detailed information and patterns, but raise privacy concerns . ANADP reduces the performance gap between regular and DP fine-tuning while maintaining the privacy constraints.
Approach:	They propose an algorithm that allocates additive noise based on the importance of model parameters to reduce the performance gap between regular fine-tuning and traditional DP fine- tuning.
Outcome:	The proposed algorithm narrows the performance gap between regular fine-tuning and traditional DP fine- tuning while maintaining privacy constraints.

Copied to clipboard

Berke Özenç, Ercan Solak

Joint Slot Filling and Intent Detection via Capsule Neural Networks (P19-1)

Challenge:	Currently, there are three publicly accessible morphological analyzers for Turkish .
Approach:	They propose to make modeling easier and more maintainable by using diagramming tools and automating much of the code generation.
Outcome:	The proposed model can be easily maintained and the code generation automated.

Copied to clipboard

Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip Yu

Semantic Parsing

Challenge:	Existing models that label slots and detect intent do not preserve hierarchical relationship between words, slots, and intents.
Approach:	They propose a capsule-based neural network model which performs slot filling and intent detection via a dynamic routing-by-agreement schema.
Outcome:	The proposed model performs better than existing models and existing models on real-world datasets.

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows (2026.findings-acl)

Copied to clipboard

Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong, Yilin Cheng, Mingzhe Lu, Adina Yakefu, Shuxin Zheng

CREPE: Open-Domain Question Answering with False Presuppositions (2023.acl-long)

Challenge:	FinWorkBench evaluates real-world enterprise-grade finance and accounting workflows . a human evaluation of GPT 5.1 Pro passes only 38.4% of workflows, a study finds .
Approach:	They propose a workflow construction process that combines LLM-assisted mining and expert annotation to build 172 composite workflows.
Outcome:	The proposed process combines expert annotation with LLM-assisted mining of workflows from authentic enterprise environments.

Copied to clipboard

Xinyan Yu, Sewon Min, Luke Zettlemoyer, Hannaneh Hajishirzi

Natural Language Interfaces

Challenge:	Existing question answering datasets assume all questions have well defined answers.
Approach:	They propose a QA dataset containing a distribution of false presuppositions . they find that 25% of questions contain false presumptions .
Outcome:	The proposed model finds that 25% of questions contain false presuppositions . the model can find presuffpositions moderately well, but struggle when predicting correctness .

Embedding Dynamic Attributed Networks by Modeling the Evolution Processes (2020.coling-main)

Copied to clipboard

Zenan Xu, Zijing Ou, Qinliang Su, Jianxing Yu, Xiaojun Quan, ZhenKun Lin

Representation Learning

Challenge:	Existing methods to embed nodes into low-dimensional vectors focus on static networks, but in practice, many networks are evolving over time and hence are dynamic, e.g., social networks.
Approach:	They propose to extract high-order neighborhood information at each given timestamp and then use an embedding prediction framework to capture the temporal correlations.
Outcome:	Extensive experiments on four real-world datasets show that the proposed method outperforms baseline methods for dynamic link prediction and node classification tasks.

Multimodal Transformer for Unaligned Multimodal Language Sequences (P19-1)

Copied to clipboard

Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, Ruslan Salakhutdinov

A Two-Step Approach for Implicit Event Argument Detection (2020.acl-main)

Challenge:	Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors.
Approach:	They propose a multimodal model that extends the standard Transformer network to learn representations directly from unaligned multimodal streams.
Outcome:	The proposed model outperforms state-of-the-art methods on aligned and non-aligned data.

Copied to clipboard

Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, Eduard Hovy

Event Extraction

Challenge:	et al., 2015) only consider local arguments in the same sentence of the event trigger.
Approach:	They propose to decompose the implicit event argument detection task into two sub-problems . they propose to use argument head-word detection and head-to-span expansion to reduce the number of candidates.
Outcome:	The proposed model achieves better performance than a strong sequence labeling baseline.

Recognizing Semantic Relations by Combining Transformers and Fully Connected Models (2020.lrec-1)

Copied to clipboard

Dmitri Roussinov, Serge Sharoff, Nadezhda Puchnina

Improving Bengali and Hindi Large Language Models (2024.lrec-main)

Challenge:	Current approaches to recognizing semantic relations between words are limited and require a word-path model.
Approach:	They propose a distributional approach that is based on an attention-based transformer and a word path model that combines useful properties of a convolutional network with a fully connected language model.
Outcome:	The proposed model outperforms the state-of-the-art in terms of performance and data sources.

Copied to clipboard

Arif Shahriar, Denilson Barbosa

Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs (2024.findings-emnlp)

Challenge:	Bengali and Hindi are low-resource languages, and the state-of-the-art tokenization methods fail to separate roots from affixes.
Approach:	They used BERT and Wordpiece tokenizers to train a wordpiece tokenization system for Bengali and Hindi to model fine-grained character-level information.
Outcome:	The proposed tokenizers outperform the state-of-the-art and Wordpiece tokenizer for modeling Bengali and Hindi.

Copied to clipboard

Muhammad Arslan Manzoor, Yuxia Wang, Minghan Wang, Preslav Nakov

ModelCitizens: Representing Community Voices in Online Safety (2025.emnlp-main)

Challenge:	Empathy plays a pivotal role in fostering prosocial behavior, often triggered by the sharing of personal experiences through narratives.
Approach:	They propose to use contrastive learning with masked LMs and supervised fine-tuning with large language models to improve empathy understanding in NLP models.
Outcome:	The proposed methods show that there is low agreement among annotators and that cultural differences are a factor in their interpretation of empathy.

Copied to clipboard

Ashima Suvarna, Christina A Chance, Karolina Naranjo, Hamid Palangi, Sophie Hao, Thomas Hartvigsen, Saadia Gabriel

Dialogue Systems & Conversational Agents

Challenge:	Existing toxic language detection models are trained on annotations that collapse diverse perspectives into a single ground truth.
Approach:	They propose to augment social media posts with conversational scenarios to reflect the impact of conversational context on toxicity.
Outcome:	The proposed model outperforms existing models on social media with conversational scenarios.

Analyzing and Modeling LLM Response Lengths with Extreme Value Theory: Anchoring Effects and Hybrid Distributions (2025.emnlp-main)

Copied to clipboard

Liuxuan Jiao, Chen Gao, Yiqian Yang, Chenliang Zhou, YiXian Huang, Xinlei Chen, Yong Li

Making Revisions Understandable: A Survey of Edit Intentions, Methods, and Applications (2026.findings-acl)

Challenge:	Existing approaches treat length as an incidental output property rather than a statistically regular phenomenon worthy of rigorous modeling.
Approach:	They propose a statistical framework for modeling and controlling large language model response lengths using extreme value theory and cross-validation on Qwen and DeepSeek architectures.
Outcome:	The proposed model improves tail fit and generalizability while maintaining generalizzability.

Copied to clipboard

Fangping Lan, Qi Zhang, Eduard Dragut

Challenge:	Text revision is a core process in document creation, capturing how authors iteratively refine, reorganize, and improve written content.
Approach:	They synthesize text revision research through the lens of edit intentions . they review prior work across the revision workflow including corpus construction, edit intention taxonomies, edit intentions, and edit intention identification.
Outcome:	The proposed approach synthesizes datasets, taxonomies, identification methods, and applications and highlights key open research directions.

ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media (2025.emnlp-main)

Copied to clipboard

Aakash Kumar Agarwal, Saprativa Bhattacharjee, Mauli Rastogi, Jemima S. Jacob, Biplab Banerjee, Rashmi Gupta, Pushpak Bhattacharyya

Linguistics & Cognitive NLP

Challenge:	Almost 50% of depression patients face the risk of going into relapse.
Approach:	They propose to validate a social media dataset on depression relapse using cognitive theories of depression.
Outcome:	The first clinically validated social media dataset focused on depression relapse comprises 204 Reddit users annotated by mental health professionals.

FigSIM: A Dataset for Fine-grained Suicide Severity and Figurative Language in Suicide Memes (2026.findings-acl)

Copied to clipboard

Liuliu Chen, Elise Carrotte, Brian E. Chapman, Jo Robinson, Mike Conway