Papers by Dan Chen

All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)

Challenge:	Word similarity measures cannot capture context-dependent, asymmetrical, polysemous nature of semantic similarity.
Approach:	They propose a new measure of similarity that reframes semantic similarity in terms of feature-based classification confusion.
Outcome:	The proposed model is comparable to cosine similarity in matching human similarity judgments across several datasets and can measure similarity using predetermined features of interest.

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Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun

Multilinguality

Challenge:	Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language.
Approach:	They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking.
Outcome:	The proposed approach mitigates language bias and consistently improves mRAG performance across languages.

Single-dataset Experts for Multi-dataset Question Answering (2021.emnlp-main)

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Dan Friedman, Ben Dodge, Danqi Chen

Natural Language Interfaces

Challenge:	Prior work has focused on training one network on multiple datasets to build a model that performs well on all of the training datasets and generalizes and transfers better to new datasets.
Approach:	They combine multiple reading comprehension datasets to build a multi-dataset question answering model with an ensemble of single-data set experts.
Outcome:	The proposed model outperforms baseline models in in-distribution accuracy and generalization and transfer performance.

Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection (2021.naacl-main)

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Sihao Chen, Fan Zhang, Kazoo Sone, Dan Roth

Text Generation

Challenge:	Recent studies have shown that current models are prone to generating unfaithful summaries . a proposed method is effective in identifying and correcting extrinsic hallucinations .
Approach:	They propose a model-agnostic post-processing technique to correct unfaithful summaries . they generate alternative candidates where names and quantities are replaced with compatible ones .
Outcome:	The proposed method corrects extrinsic hallucinations in unfaithful summaries.

Task-Oriented Dialogue as Dataflow Synthesis (2020.tacl-1)

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Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H. Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, Alexander Zotov

Are All Steps Equally Important? Benchmarking Essentiality Detection in Event Processes (2023.emnlp-main)

Challenge:	Existing approaches to task-oriented dialogue represent dialogue state as a dataflow graph . microsoft's SMCalFlow dataset features complex dialogues about events, weather, places, and people .
Approach:	They propose a dataflow graph-based dialogue agent that maps each user utterance to a program that extends this graph.
Outcome:	The proposed framework improves representability and predictability in natural dialogues . it uses dataflow graphs and metacomputation to map user intents to a program .

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Haoyu Wang, Hongming Zhang, Yueguan Wang, Yuqian Deng, Muhao Chen, Dan Roth

Commonsense Reasoning

Challenge:	Existing models of event processing do not understand the essentiality of step events towards a goal event.
Approach:	They propose to deconstruct a goal event into a discrete representation of finer-grained (step) events, which are not equally important to the goal.
Outcome:	The proposed model can understand the essentiality of different step events towards a goal event.

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen

Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)

Challenge:	Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval.
Approach:	They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs).
Outcome:	The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models.

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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu

Design Challenges for a Multi-Perspective Search Engine (2022.findings-naacl)

Challenge:	Sentence embeddings are typically learned to recognize the semantic relation between two text inputs.
Approach:	They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text.
Outcome:	The proposed model is able to produce contextual embeddings corresponding to different atomic propositions, i.e. semantic equivalence between propositions across different text sequences.

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Sihao Chen, Siyi Liu, Xander Uyttendaele, Yi Zhang, William Bruno, Dan Roth

Information Retrieval

Challenge:	a document retrieval system fails to deliver diverse and direct responses to controversial questions . classical document retrievals provide a ranked list of references to relevant but not necessarily trustworthy web documents .
Approach:	They propose a perspective-oriented document retrieval paradigm to address these challenges . they propose sponses with different perspectives within topically-related web documents .
Outcome:	The proposed system is based on a user survey and a prototype . it will be used to assess the utility and understanding of the system .

MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning (2024.findings-emnlp)

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Jingfan Zhang, Yi Zhao, Dan Chen, Xing Tian, Huanran Zheng, Wei Zhu

Cross Attention Augmented Transducer Networks for Simultaneous Translation (2021.emnlp-main)

Challenge:	Low-rank adaptation and its mixture-of-experts (MOE) methods are highly effective but introduce significant latency in multi-tenant settings due to the LoRA modules and MOE routers added to multiple linear modules.
Approach:	They propose a low-rank adaptation variant that considers each LoRA module as an expert and employs a prompt-aware routing mechanism.
Outcome:	Extensive analysis on commonsense reasoning tasks and math reasoning tasks show that MiLoRA outperforms strong PEFT baselines with comparable tunable parameter budgets.

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Dan Liu, Mengge Du, Xiaoxi Li, Ya Li, Enhong Chen

Text Generation

Challenge:	Existing approaches to simultaneous translation are limited by monotonic constraint . a novel architecture for simultaneous translation is proposed .
Approach:	They propose a cross attention-augmented transducer for simultaneous translation that optimizes both policies and translation models by expanding target sequences with blank symbols.
Outcome:	The proposed architecture achieves better latency-quality trade-offs than state-of-the-art approaches.

Beyond Tokens: Concept-Level Training Objectives for LLMs (2026.eacl-short)

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Laya Iyer, Pranav Somani, Alice Guo, Dan Jurafsky, Chen Shani

Challenge:	Large language models (LLMs) are trained with a surprisingly narrow objective: predicting the next token in a sequence.
Approach:	They propose a shift from token-level to concept-level prediction where concepts group multiple surface forms of the same idea.
Outcome:	The proposed model improves on human-level models on diverse NLP benchmarks.

Event-Centric Natural Language Processing (2021.acl-tutorials)

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Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, Dan Roth

Challenge:	This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations.
Approach:	This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks.
Outcome:	This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks.

Modular Domain Adaptation (2022.findings-acl)

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Junshen Chen, Dallas Card, Dan Jurafsky

Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)

Challenge:	Existing models for sentiment analysis and hate speech detection are difficult to account for domain shift without access to source data.
Approach:	They propose to treat domain adaptation as a modular process that involves separate model producers and model consumers . they demonstrate that they can independently cooperate to facilitate more accurate measurements of text .
Outcome:	The proposed methods improve out-of-domain accuracy on four multi-domain text classification datasets.

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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth

Question Answering

Challenge:	Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive.
Approach:	They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C.
Outcome:	The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness.

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

Visual Data in NLP

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction (2024.findings-naacl)

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Boyi Li, Rodolfo Corona, Karttikeya Mangalam, Catherine Chen, Daniel Flaherty, Serge Belongie, Kilian Weinberger, Jitendra Malik, Trevor Darrell, Dan Klein

VisCGEC: Benchmarking the Visual Chinese Grammatical Error Correction (2025.naacl-long)

Challenge:	Recent studies show multimodal inputs can improve grammar induction, but weak textual baselines are needed for training.
Approach:	They use a fixed grammar family to compare multimodal grammar induction methods . they find multimodal inputs can improve grammar induction by grounding textual inputs to the visual world .
Outcome:	The proposed model outperforms weaker baselines on four benchmark datasets.

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Xiaoman Wang, Dan Yuan, Xin Liu, Yike Zhao, Xiaoxiao Zhang, Xizhi Chen, Yunshi Lan

Visual Data in NLP

Challenge:	Existing studies on Chinese grammatical error correction ignore multi-modality and faked errors, which pushes techniques far away from real-world scenarios.
Approach:	They propose to benchmark Chinese grammatical error correction for Chinese as a foreign language learner (CFL) using a dataset, they propose to use two CGEC frameworks to conduct experiments .
Outcome:	The proposed approach achieves an F 0.5 score of only 28.9%.

SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)

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Dan Qiao, Chenchen Dai, Yuyang Ding, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang

Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)

Challenge:	Existing methods to handle label noise in text classification tasks are limited to visual data.
Approach:	They propose a method to handle label noise in text classification tasks using a Gaussian Mixture Model.
Outcome:	The proposed method outperforms baselines on three types of text classification tasks on visual and textual data.

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Wenpeng Yin, Muhao Chen, Rui Zhang, Ben Zhou, Fei Wang, Dan Roth

Talking Point based Ideological Discourse Analysis in News Events (2025.findings-acl)

Challenge:	general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data.
Approach:	This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks .
Outcome:	This tutorial aims to examine the evolution of general-purpose large language models (LLMs) the authors argue that the evolution is dependent on the availability of training data and the scale of the models.

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Nishanth Sridhar Nakshatri, Nikhil Mehta, Siyi Liu, Sihao Chen, Daniel Hopkins, Dan Roth, Dan Goldwasser

Discourse & Pragmatics

Challenge:	Existing models of ideological discourse analysis fail to capture the key elements that shape real-world narratives and lack the ability to integrate contextual information required for understanding abstract ideological views.
Approach:	They propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.
Outcome:	The proposed framework can generate ideology-specific viewpoints (partisan perspectives) it can be used to generate event snapshots, a visual way of interpreting event discourse.

REaR : Retrieve, Expand and Refine for Effective Multitable Retrieval (2026.acl-long)

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Rishita Agarwal, Himanshu Singhal, Peter Baile Chen, Manan Roy Choudhury, Dan Roth, Vivek Gupta

P-React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized LoRA Experts (2025.findings-acl)

Challenge:	REaR is retriever-agnostic and improves dense/ sparse retrievers on complex table QA datasets.
Approach:	a new framework separates semantic relevance from structural joinability for efficient multi-table retrieval. adam scott and eric liu introduce REaR, a three-stage, LLM-free framework.
Outcome:	a new framework improves retrieval quality and performance on complex table QA datasets . it separates semantic relevance from structural joinability and prunes weakly related candidates . the framework is retriever-agnostic and delivers performance competitive with state-of-the-art LLM-augmented retrieval systems .

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Yuhao Dan, Jie Zhou, Qin Chen, Junfeng Tian, Liang He

Challenge:	Existing studies on personalized large language models focus on modeling explicit character profiles, while ignoring the underlying personality traits that truly shape behaviors and decision-making.
Approach:	They propose a personalized large language model (LLM) that captures implicit Big Five personality traits and integrates a Personality Specialization Loss to capture individual trait expressions.
Outcome:	The proposed model improves on Big Five personality traits and integrates a Personality Specialization Loss (PSL) to capture individual trait expressions.

The Linguistic Connectivities Within Large Language Models (2025.findings-acl)

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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han

Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries (2023.findings-emnlp)

Challenge:	Recent studies have discovered notable disparities in their performance across different languages.
Approach:	They conduct a systematic investigation into the behaviors of large language models across 27 different languages on 3 different scenarios and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations.
Outcome:	The proposed model demonstrates that there are significant disparities in performance across languages across 27 different languages on 3 different scenarios.

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Nishanth Nakshatri, Siyi Liu, Sihao Chen, Dan Roth, Dan Goldwasser, Daniel Hopkins

Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)

Challenge:	Using hierarchical Dirichlet processes, we characterize news articles associated with key events from news streams.
Approach:	They propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention.
Outcome:	The proposed framework produces more coherent clusters based on event summaries . the proposed framework is a first step in a new field of news analysis .

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Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji

Dialogue Systems & Conversational Agents

Challenge:	a framework for constructing dialogue world models for natural language tasks is currently lacking.
Approach:	They propose a framework that can be used to train a dialogue world model.
Outcome:	The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification .

PerspectroScope: A Window to the World of Diverse Perspectives (P19-3)

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Sihao Chen, Daniel Khashabi, Chris Callison-Burch, Dan Roth

Textual Inference

Challenge:	PerspectroScope is a web-based system that lets users query a discussion-worthy natural language claim .
Approach:	They propose a web-based system which lets users query a discussion-worthy natural language claim and extract and visualize various perspectives in support or against the claim.
Outcome:	The proposed system lets users query a discussion-worthy natural language claim and extract and visualize various perspectives in support or against the claim.

🧑‍🍳 Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination (2026.tacl-1)

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Moran Mizrahi, Chen Shani, Gabriel Stanovsky, Dan Jurafsky, Dafna Shahaf

Challenge:	Large Language Models excel at many tasks, yet struggle to generate truly creative ideas.
Approach:	They propose a novel approach that enhances Large Language Models' creativity by manipulating structured representations of existing ideas.
Outcome:	The proposed model outperforms GPT-4o in novelty and diversity and outperformed GPT-0 in creative generation.

VIRT: Improving Representation-based Text Matching via Virtual Interaction (2022.emnlp-main)

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Dan Li, Yang Yang, Hongyin Tang, Jiahao Liu, Qifan Wang, Jingang Wang, Tong Xu, Wei Wu, Enhong Chen

Joint Constrained Learning for Event-Event Relation Extraction (2020.emnlp-main)

Challenge:	Experimental results show that representation-based text matching methods suffer from performance degradation due to the lack of interactions between the pair of texts.
Approach:	They propose a virtual interaction mechanism that enables deep interaction between texts . they propose 'inteRacTion mechanism' that can be integrated into existing methods as plugins .
Outcome:	The proposed method outperforms state-of-the-art models on six text matching benchmarks.

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Haoyu Wang, Muhao Chen, Hongming Zhang, Dan Roth

Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)

Challenge:	Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other.
Approach:	They propose a joint constrained learning framework that enforces logical constraints within and across multiple temporal and subevent relations of events by converting constraints into differentiable learning objectives.
Outcome:	The proposed framework outperforms SOTA methods on benchmarks for temporal relation extraction and event hierarchy construction.

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Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme

Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations (2023.acl-long)

Challenge:	Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing.
Approach:	They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English .
Outcome:	The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data.

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Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He

MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding (2023.emnlp-main)

Challenge:	In-context learning is an important paradigm for adapting large language models to new tasks . but the generalization behavior of ICL remains poorly understood .
Approach:	They characterize the feature biases of large language models by constructing underspecified demonstrations . they find that LLMs exhibit clear feature bias, and they evaluate interventions .
Outcome:	The proposed model prefers the "default" task features over distractor features more often than the base model.

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Steven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks

Learning Constraints and Descriptive Segmentation for Subevent Detection (2021.emnlp-main)

Challenge:	Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study.
Approach:	They propose a Merger Agreement Understanding Dataset with over 39,000 examples and over 47,000 annotations.
Outcome:	The Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study.

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Haoyu Wang, Hongming Zhang, Muhao Chen, Dan Roth

Challenge:	Event mentions in text correspond to real-world events of varying degrees of granularity . task of subevent detection aims to resolve this granulem issue by recognizing membership of events .
Approach:	They propose a task of event-based text segmentation as an auxiliary task to improve learning for subevent detection.
Outcome:	The proposed method outperforms baseline methods on subevent detection, HiEve and IC datasets while achieving decent performance on EventSeg prediction.

Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents (2023.findings-acl)

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Catherine Chen, Zejiang Shen, Dan Klein, Gabriel Stanovsky, Doug Downey, Kyle Lo

On Reference (In-)Determinacy in Natural Language Inference (2025.findings-naacl)

Challenge:	Recent work has shown that infusing layout features into language models improves processing of visually-rich documents such as scientific papers.
Approach:	They propose a method to evaluate layout-infused language models that incorporate layout features into their models to emulate layout distribution shifts.
Outcome:	The proposed model performs better under layout distribution shifts than in-distribution conditions.

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Sihao Chen, Chaitanya Malaviya, Alex Fabrikant, Hagai Taitelbaum, Tal Schuster, Senaka Buthpitiya, Dan Roth

Representing Rule-based Chatbots with Transformers (2025.naacl-long)

Challenge:	Using reference determinacy, models fail to recognize context mismatch in NLI examples .
Approach:	They propose a benchmark to identify reference ambiguity in NLI examples . they propose RD as a possible assumption for natural language inference .
Outcome:	The proposed benchmark identifies reference ambiguity in natural language inference examples . 80% false contradiction and >50% entailment predictions are found .

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Dan Friedman, Abhishek Panigrahi, Danqi Chen

Dialogue Systems & Conversational Agents

Challenge:	Existing work on how Transformers can solve synthetic tasks has not explored how to extend this to a conversational setting.
Approach:	They propose to use ELIZA as a framework for formal mechanistic analysis of Transformers . they propose to model local pattern matching and long-term dialogue state tracking .
Outcome:	The proposed model can be extended to model key aspects of conversation, the authors show . their model favors an induction head mechanism over a more precise copying mechanism .

New Frontiers of Information Extraction (2022.naacl-tutorials)

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Muhao Chen, Lifu Huang, Manling Li, Ben Zhou, Heng Ji, Dan Roth

Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval (2024.acl-long)

Challenge:	Information extraction (IE) is the process of automatically extracting structural information from unstructured or semi-structured data.
Approach:	This tutorial will provide an introduction to recent advances in IE by answering several important research questions.
Outcome:	The tutorial will address several important research questions and outline directions for further investigation.

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Peter Baile Chen, Yi Zhang, Dan Roth

Structured Data in NLP

Challenge:	Existing methods for retrieving relevant tables are not sufficient as many questions require retrieving multiple tables and joining them through a join plan that cannot be discerned from the user query itself.
Approach:	They propose a method that uncovers useful join relations during table retrieval.
Outcome:	The proposed method outperforms the state-of-the-art methods for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up 5.4% in accuracy.

UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)

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Huanqin Wu, Wei Liu, Lei Li, Dan Nie, Tao Chen, Feng Zhang, Di Wang

PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition (2023.findings-acl)

Challenge:	Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity.
Approach:	They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation.
Outcome:	The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction.

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Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, Dan Roth, Tal Schuster

LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval (2025.emnlp-main)

Challenge:	Existing systems for Natural Language Inference (NLI) only recognize textual entailment relations on sentence-level . however, even a simple sentence often contains multiple propositions, i.e. distinct units of meaning conveyed by the sentence .
Approach:	They propose a system to recognize whether one text is textually entailed by another . they use a corpus of over 45K propositions annotated by human raters to study the textual entailment relation of each proposition in a sentence individually.
Outcome:	The proposed dataset can be used to understand the compositionality of NLI labels.

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Yanzhen Shen, Sihao Chen, Xueqiang Xu, Yunyi Zhang, Chaitanya Malaviya, Dan Roth

FCGCL: Fine- and Coarse-Granularity Contrastive Learning for Speech Translation (2022.findings-emnlp)

Challenge:	Current dense retrievers struggle with queries with logical connectives, a use case that is often overlooked but important in downstream applications.
Approach:	They propose a logically-informed contrastive learning objective for dense retrievers that learns to respect the subset and mutually exclusive set relation between query results.
Outcome:	The proposed model improves retrieval performance and consistency on entity retrieval tasks.

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Hao Zhang, Nianwen Si, Yaqi Chen, Zhen Li, Tong Niu, Xukui Yang, Dan Qu

Targeted Exploration via Unified Entropy Control for Reinforcement Learning (2026.findings-acl)

Challenge:	Existing methods to perform implicit knowledge transfer from machine translation to ST model are difficult because of the task complexity and data scarcity.
Approach:	They recommend a method which conducts explicit knowledge transfer from MT to ST model by fine and coarse granularity contrastive learning.
Outcome:	The proposed method improves the performance of the end-to-end speech translation model on all 8 languages.

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Chen Wang, Lai Wei, Yanzhi Zhang, Chenyang Shao, Zedong Dan, Weiran Huang, Ge Lan, Yue Wang

Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method (2025.acl-long)

Challenge:	Existing methods for group relative policy optimization suffer from entropy collapse . Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain stability.
Approach:	They propose a framework that provides targeted mechanisms for exploration and stabilization.
Outcome:	The proposed framework expands search space on difficult prompts while preventing entropy growth uncontrollably.

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Peter Baile Chen, Yi Zhang, Mike Cafarella, Dan Roth

Information Retrieval

Challenge:	Existing RAG solutions address the alignment problem in a limited manner . ARM explores relationships among data objects, enabling a retrieve-all-at-once solution for complex queries .
Approach:	Experimental results show that ARM improves alignment of open-domain questions with available data . ARM explores relationships among data objects, enabling a retrieve-all-at-once solution for complex queries.
Outcome:	Experimental results show that ARM outperforms existing RAG methods on complex open-domain questions.

Analogous Process Structure Induction for Sub-event Sequence Prediction (2020.emnlp-main)

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Hongming Zhang, Muhao Chen, Haoyu Wang, Yangqiu Song, Dan Roth

Challenge:	Existing work on event understanding is focusing on procedural (or horizontal) tasks such as predicting the next event given an observed sequence.
Approach:	They propose an Analogous Process Structure Induction framework which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub- sequence of previously unseen open-domain processes.
Outcome:	The proposed framework can predict the whole sub-event sequence of previously unseen open-domain processes.

AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems (2025.emnlp-demos)

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Hannah Kim, Kushan Mitra, Chen Shen, Dan Zhang, Estevam Hruschka

Challenge:	Large language models (LLMs) are being used for planning in orchestrated multi-agent systems . existing LLMs fall short of human expectations and lack effective mechanisms for users to inspect, understand, and control their behaviors.
Approach:	They propose a system supporting human-in-the-loop planning through conversational and graph-based interfaces.
Outcome:	AIPOM enables users to transparently inspect, refine, and collaboratively guide LLM-generated plans, significantly enhancing user control and trust in multi-agent workflows.

A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily (2024.naacl-long)

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Peng Ding, Jun Kuang, Dan Ma, Xuezhi Cao, Yunsen Xian, Jiajun Chen, Shujian Huang

MultiOpEd: A Corpus of Multi-Perspective News Editorials (2021.naacl-main)

Challenge:	Existing methods for generating 'jailbreaks' suffer from manual design or require optimization on other white-box models, which compromises either generalization or efficiency.
Approach:	They propose a framework that leverages LLMs to generate effective jailbreak prompts and a generalized framework that can be used to generate prompts.
Outcome:	The proposed framework improves the attack success rate while reducing the time cost compared to baselines.

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Siyi Liu, Sihao Chen, Xander Uyttendaele, Dan Roth

Challenge:	a news editorial is a genre of persuasive text where argumentation structure is usually implicit.
Approach:	They propose an open-domain news editorial corpus that supports automatic perspective discovery by identifying and abstracting natural language perspectives from editorials.
Outcome:	The proposed system supports automatic perspective discovery tasks in news editorials.

Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims (N19-1)

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Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, Dan Roth

WinSpot: GUI Grounding Benchmark with Multimodal Large Language Models (2025.acl-short)

Challenge:	a number of fact checking techniques are used to identify and eliminate biases in text data.
Approach:	They propose to use search engines to expand and diversify a dataset of claims, perspectives and evidence to address a selection bias.
Outcome:	The proposed approach outperforms existing methods in a language understanding task.

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Zheng Hui, Yinheng Li, Dan Zhao, Colby Banbury, Tianyi Chen, Kazuhito Koishida

MetaMixSpeech: Meta Task Augmentation for Low-Resource Speech Recognition (2025.findings-emnlp)

Challenge:	Existing GUI grounding data focuses on web-based elements, leaving a gap in real-world GUI interaction data for non-web applications.
Approach:	They propose a framework that leverages Large Language Models to generate large-scale GUI grounding data.
Outcome:	The framework validates and refines 5,000 GUI coordinate-instruction pairs and provides high-quality data for training and evaluating visual GUI agents.

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Yaqi Chen, Hao Zhang, Wenlin Zhang, XuKui Yang, Dan Qu, Yunpeng Liu

Meta-Adapter for Self-Supervised Speech Models: A Solution to Low-Resource Speech Recognition Challenges (2024.lrec-main)

Challenge:	Meta-learning has proven to be a powerful paradigm for improving speech recognition performance . however, multilingual meta learning also faces challenges such as task overfitting and learner overfit .
Approach:	a new method is proposed to augment meta-training tasks with "more data" the method incorporates both support and query augmentations .
Outcome:	The proposed method achieves a 6.35% improvement in the word error rate on FLEURS and Common Voice datasets.

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Yaqi Chen, Hao Zhang, Xukui Yang, Wenlin Zhang, Dan Qu

A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)

Challenge:	Existing self-supervised learning models can learn latent representations from large amounts of unlabeled data, but they are expensive to fine-tune.
Approach:	They develop a meta-adapter to obtain meta-initialized parameters for self-supervised models . meta-Adapters show better generalization and extensibility than traditional pretraining methods .
Outcome:	Experiments on common voice and FLEURS datasets show Meta-Adapter performs better on low-resource languages . authors show it can be used on 12 low-source languages, but it requires huge computational resources .

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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang

Exploring Dual Encoder Architectures for Question Answering (2022.emnlp-main)

Challenge:	Inductive reasoning is an important task for large language models (LLMs).
Approach:	They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation.
Outcome:	The proposed method improves inductive reasoning in large language models.

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Zhe Dong, Jianmo Ni, Dan Bikel, Enrique Alfonseca, Yuan Wang, Chen Qu, Imed Zitouni

Multilingual Multimodal Learning with Machine Translated Text (2022.findings-emnlp)

Challenge:	Dual encoders have been used for question-answering and information retrieval tasks with good results.
Approach:	They propose to use two different versions of dual encoders for QA retrieval tasks . they propose to share parameters in projection layers between two encoder towers .
Outcome:	The proposed architectures outperform SDE and ADE on QA retrieval tasks.

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Chen Qiu, Dan Oneață, Emanuele Bugliarello, Stella Frank, Desmond Elliott

Multilinguality

Challenge:	Currently, most vision-and-language pretraining research focuses on English tasks due to the availability of datasets.
Approach:	They propose a framework for machine translating English multimodal data to improve training data . they propose two metrics to prevent models from learning from low-quality translated text .
Outcome:	The proposed framework can be applied to any multimodal dataset and model.

Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms (N19-1)

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Diyi Yang, Jiaao Chen, Zichao Yang, Dan Jurafsky, Eduard Hovy

Constructing Taxonomies from Pretrained Language Models (2021.naacl-main)

Challenge:	Existing models can't quantify persuasiveness of requests or extract successful persuasive strategies.
Approach:	They propose a semi-supervised hierarchical neural network model to quantify persuasiveness and identify persuasive strategies in advocacy requests.
Outcome:	The proposed method outperforms baseline models and offers increased interpretability of persuasive speech.

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Catherine Chen, Kevin Lin, Dan Klein

Finding Dataset Shortcuts with Grammar Induction (2022.emnlp-main)

Challenge:	A variety of NLP tasks use taxonomic information, including question answering and information retrieval.
Approach:	They propose a method for constructing taxonomic trees using pretrained language models by incorporating web-retrieved glosses into the model.
Outcome:	The proposed model achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best published model on English WordNet.

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Dan Friedman, Alexander Wettig, Danqi Chen

Syntactic Text Processing

Challenge:	Prior work on shortcut detection focused on enumerating features like unigrams or bigrams . prior work relied on post-hoc models that reveal qualitative patterns without a clear statistical interpretation .
Approach:	They propose to use probabilistic grammars to characterize and discover shortcuts in NLP datasets using context-free grammars and synchronous context- free grammars.
Outcome:	The proposed grammars reveal interesting shortcut features in a number of datasets, including simple and high-level features, and automatically identify groups of test examples on which conventional classifiers fail.

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

Factual Probing Is [MASK]: Learning vs. Learning to Recall (2021.naacl-main)

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

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Zexuan Zhong, Dan Friedman, Danqi Chen

Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction (2023.eacl-main)

Challenge:	Existing methods for factual probing can interpret the model’s prediction accuracy as a lower bound on the amount of factual information it encodes.
Approach:	They propose a method which directly optimizes in continuous embedding space and can predict an additional 6.4% of facts in the LAMA benchmark.
Outcome:	The proposed method outperforms the best previous prompt method by 6.4% on the LAMA benchmark.

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Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob Gardner, Dan Roth, Muhao Chen

Cross-lingual Entity Alignment with Incidental Supervision (2021.eacl-main)

Challenge:	Existing models for event temporal relation extraction are based on data-driven machine learning . however, TEMPREL extraction is not accurate under distribution shifts.
Approach:	They propose to conduct counterfactual analysis to attenuate the effects of two types of training biases: the event trigger bias and the frequent label bias.
Outcome:	The proposed model extracts TempRel and timelines more faithfully compared to SOTA methods . it is based on two perspectives: one is to extract genuinely based upon contextual description . the other is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text .

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Muhao Chen, Weijia Shi, Ben Zhou, Dan Roth

Indirectly Supervised Natural Language Processing (2023.acl-tutorials)

Challenge:	Existing methods to match entities in multilingual knowledge graphs are insufficient, resulting in inconsistent seed alignment between KGs.
Approach:	They propose a model that integrates multilingual KGs and monolingual text corpora in a shared embedding scheme and a self-learning based alignment learning process to induce correspondence between entities and lexemes.
Outcome:	The proposed model significantly outperforms state-of-the-art methods on benchmark datasets and significantly outpersts existing methods.

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Wenpeng Yin, Muhao Chen, Ben Zhou, Qiang Ning, Kai-Wei Chang, Dan Roth

Challenge:	a tutorial on indirect supervision addresses challenges in ML for NLP . conventional approaches to NLP use taskspecific labeled examples of a large volume . indirect supervision is useful for a wide range of NLP tasks, but it is not enough for decoders .
Approach:	This tutorial aims to address questions about indirect supervision in machine learning . authors discuss indirect supervision from T′ that handles T with outputs spanning from a moderate size to an open space .
Outcome:	This tutorial aims to answer questions about how to provide supervision for ML tasks . it will discuss indirect supervision from T′ that handles T with outputs spanning from a moderate size to an open space .

ExpertQA: Expert-Curated Questions and Attributed Answers (2024.naacl-long)

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Chaitanya Malaviya, Subin Lee, Sihao Chen, Elizabeth Sieber, Mark Yatskar, Dan Roth