Papers by Rui Yu

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

Challenge:	Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing.
Approach:	They propose a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows.
Outcome:	The proposed taxonomy emphasizes the role of LLMs and the design of end-to-end workflows.

Copied to clipboard

Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

Grounded Multimodal Named Entity Recognition on Social Media (2023.acl-long)

Copied to clipboard

Jianfei Yu, Ziyan Li, Jieming Wang, Rui Xia

Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning (2025.findings-emnlp)

Challenge:	Existing studies on Multimodal Named Entity Recognition only extract entity-type pairs in text, which is useless for multimodal knowledge graph construction.
Approach:	They propose a task to identify named entities in text and their bounding box groundings in image . they extend four well-known MNER methods to establish a number of baseline systems .
Outcome:	The proposed framework outperforms baseline systems on the GMNER task.

Copied to clipboard

Fanfan Wang, Xiangqing Shen, Jianfei Yu, Rui Xia

FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)

Challenge:	Current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality.
Approach:	They propose a framework that integrates supervised fine-tuning with reinforcement learning to improve ESC models' response quality.
Outcome:	The proposed framework enables models to select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy.

Copied to clipboard

Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev

CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation (2026.acl-long)

Challenge:	Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity.
Approach:	They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models.
Outcome:	The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models.

Copied to clipboard

Sizhe Wang, Zhengren Wang, Dongsheng Ma, Yongan Yu, Rui Ling, Zhiyu li, Feiyu Xiong, Wentao Zhang

CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing (2026.acl-long)

Challenge:	Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes.
Approach:	They propose a benchmark to evaluate LLMs' ability to perform codeflow by reusing existing functions over multiple turns.
Outcome:	The proposed benchmarks show that LLMs perform significantly worse in multi-turn codeflow scenarios and that their performance inversely correlates with dependency complexity.

Copied to clipboard

Rui Wang, Junda Wu, Yu Xia, Tong Yu, Ruiyi Zhang, Ryan A. Rossi, Subrata Mitra, Lina Yao, Julian McAuley

Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling (2022.findings-emnlp)

Challenge:	Existing Large Language Models exhibit critical vulnerability to indirect prompt injection attacks, where instructions injected within in the prompt context can override the user's intent.
Approach:	They propose a neural pruning algorithm that prunes neurons associated with instruction-following during KV cache encoding of the prompt context.
Outcome:	The proposed approach significantly reduces the attack success rate while preserving the model's ability to follow user instructions.

Copied to clipboard

Junda Wu, Rui Wang, Tong Yu, Ruiyi Zhang, Handong Zhao, Shuai Li, Ricardo Henao, Ani Nenkova

“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)

Challenge:	Existing deep learning models for sequence labeling are expensive and time-consuming.
Approach:	They propose an interactive sequence labeling that allows training directly with the user feedback . they identify context and feedback biases by formulating interactive sequence labels via a Structural Causal Model.
Outcome:	The proposed approach can effectively alleviate the biases and can be learnt with the user feedback.

Copied to clipboard

Chuanqi Cheng, Quan Tu, Wei Wu, Shuo Shang, Cunli Mao, Zhengtao Yu, Rui Yan

Cross-Domain Review Generation for Aspect-Based Sentiment Analysis (2021.findings-acl)

Challenge:	Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create.
Approach:	They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles.
Outcome:	The proposed framework improves BLEU and ROUGE scores on three datasets and human evaluations further validate the proposed method.

Copied to clipboard

Jianfei Yu, Chenggong Gong, Rui Xia

PQR: Improving Dense Retrieval via Potential Query Modeling (2025.acl-long)

Challenge:	Existing domain adaptation methods for Aspect-Based Sentiment Analysis lack finegrained labeled data.
Approach:	They propose a new domain adaptation paradigm called cross-domain review generation which aims to generate target-domain reviews with fine-grained annotation based on the labeled source domain.
Outcome:	The proposed approach is superior to state-of-the-art domain adaptation methods.

Copied to clipboard

Junfeng Kang, Rui Li, Qi Liu, Yanjiang Chen, Zheng Zhang, Junzhe Jiang, Heng Yu, Yu Su

P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains (2024.findings-emnlp)

Challenge:	Existing training data is sparse, with each document associated with one or a few labeled queries.
Approach:	They propose a training-free potential query retrieval framework to address this problem . they use a Gaussian mixture distribution to model all potential queries for a document .
Outcome:	The proposed method is able to capture comprehensive semantic information from a document with multiple queries.

Copied to clipboard

Simeng Han, Aaron Yu, Rui Shen, Zhenting Qi, Martin Riddell, Wenfei Zhou, Yujie Qiao, Yilun Zhao, Semih Yavuz, Ye Liu, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Dragomir Radev, Rex Ying, Arman Cohan

An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next (2021.findings-emnlp)

Challenge:	Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales.
Approach:	They propose to annotate a human-annotated dataset consisting of diverse and complex reasoning chains for a set of realistic logical reasoning stories also written by humans.
Outcome:	The proposed model outperforms existing methods on understanding the capabilities of LLMs in logical reasoning by 10% or more.

Copied to clipboard

Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev

DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization (2022.acl-long)

Challenge:	Existing models for dialogue summarization focus on extracting the main events of short conversations, but real-world dialogues are difficult to train.
Approach:	They propose three strategies to deal with the lengthy input problem and locate relevant information using long dialogue datasets.
Outcome:	The retrieve-then-summarize pipeline models yield the best performance on three long dialogue datasets.

Copied to clipboard

Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, Dragomir Radev

SParC: Cross-Domain Semantic Parsing in Context (P19-1)

Challenge:	Existing models struggle with summarizing long text due to high memory complexity of the full self-attention.
Approach:	They propose a dynamic latent extraction approach for abstractive long-input summarization that treats extracted text snippets as latent variables and allows dynamic attention weights during decoding.
Outcome:	The proposed method outperforms existing methods on GovReport, QMSum, and arXiv while yielding strong results on arX.

Copied to clipboard

Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev

Efficient OpAmp Adaptation for Zoom Attention to Golden Contexts (2025.acl-long)

Challenge:	Xu et al., 2017): a dataset for cross-domain semantic parsing in context with 4,298 question sequences.
Approach:	They present a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences.
Outcome:	The proposed dataset demonstrates that it has greater semantic diversity and can be generalized to unseen domains due to its cross-domain nature and the unseened databases at test time.

Copied to clipboard

Haoyuan Wu, Rui Ming, Haisheng Zheng, Zhuolun He, Bei Yu

DART: Open-Domain Structured Data Record to Text Generation (2021.naacl-main)

Challenge:	Large language models have shown significant promise in question-answering tasks . noisy reference documents hinder performance of LLMs, causing disproportionate attention to irrelevant content .
Approach:	They propose an adaptive large language model that allocates disproportionate attention to irrelevant documents . they use transformers to train the model and integrate it into pre-trained Transformer blocks .
Outcome:	The proposed model outperforms state-of-the-art models on noisy-context benchmarks.

Copied to clipboard

Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani

Structured Data in NLP

Challenge:	Data-to-text annotations can be costly when dealing with tables with nontrivial structures.
Approach:	They propose a procedure for extracting semantic triples from tables that encodes their structures by exploiting table headers and table title.
Outcome:	The proposed method exploits the semantic dependencies between table headers and title to extract semantic triples from tables.

TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation (2023.acl-short)

Copied to clipboard

Yiming Ai, Zhiwei He, Kai Yu, Rui Wang

Machine Translation

Challenge:	tense inconsistency is a common problem in machine translation systems.
Approach:	They propose a parallel tense test set, containing French-English 552 utterances, and introduce a benchmark, tence prediction accuracy.
Outcome:	The proposed model can measure the tense consistency performance of machine translation systems for the first time.

Exploiting Contextual Information via Dynamic Memory Network for Event Detection (D18-1)

Copied to clipboard

Shaobo Liu, Rui Cheng, Xiaoming Yu, Xueqi Cheng

Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026.findings-acl)

Challenge:	Existing methods for event detection only process context once . a multi-hop mechanism to capture contextual information improves performance .
Approach:	They propose to use dynamic memory network to capture contextual information . they propose to model event triggers by identifying word or phrase which most represents it .
Outcome:	The proposed model achieves best F1 score compared to the state-of-the-art models.

Copied to clipboard

Yurun Song, Xiangqing Shen, Jianfei Yu, Rui Xia

A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis (2023.findings-emnlp)

Challenge:	Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens.
Approach:	They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability.
Outcome:	The proposed framework allows logical constraints to be dynamically interleaved with graph search while optimizing via reinforcement learning with only final answer feedback eliminates the need for gold program annotations.

Copied to clipboard

Nan Song, Hongjie Cai, Rui Xia, Jianfei Yu, Zhen Wu, Xinyu Dai

UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set (2023.findings-acl)

Challenge:	Aspect-based sentiment analysis (ABSA) has received wide attention in NLP for nearly two decades . previous studies focused on sentence-level ABSA, but document-level research has not received enough attention.
Approach:	They propose a Sequence-to-Structure approach to address the document-level targeted sentiment analysis task, which aims to extract the opinion targets consisting of multi-level entities from a review document and predict their sentiments.
Outcome:	The proposed approach outperforms baselines on six domains on the document-level targeted sentiment analysis task.

Copied to clipboard

Zinong Yang, Feng Xu, Jianfei Yu, Rui Xia

SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (D18-1)

Challenge:	Existing methods decompose the COQE task into multiple subtasks and solve them in a pipeline manner, but ignore the intrinsic connection between subtask and the error propagation among stages.
Approach:	They propose a unified generative model that solves COQE in one shot by concatenating all the comparative tuples into a target output sequence.
Outcome:	The proposed model significantly outperforms the SOTA method on multiple benchmarks and ablation experiments.

Copied to clipboard

Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, Dragomir Radev

ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction (2020.acl-main)

Challenge:	Existing studies in text-to-SQL do not require generating complex SQL queries with multiple clauses or sub-queries.
Approach:	They propose a syntax tree network to address the complex text-to-SQL generation task.
Outcome:	The proposed model outperforms the current state-of-the-art model by 9.5% on a large text-to-SQL corpus.

Copied to clipboard

Zixiang Ding, Rui Xia, Jianfei Yu

Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (2022.coling-1)

Challenge:	a new task, called emotion-cause pair extraction, has emerged in text emotion analysis . a 2D representation scheme is proposed to represent the emotion-case pairs .
Approach:	They propose a 2D approach to represent emotion-cause pairs by a 3D representation scheme.
Outcome:	The proposed approach improves the state-of-the-art on the emotion cause corpus . the proposed approach is based on a two-step framework with flaws .

Copied to clipboard

Guobiao Zhang, Wenpeng Lu, Xueping Peng, Shoujin Wang, Baoshuo Kan, Rui Yu

DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)

Challenge:	Word sense disambiguation (WSD) is one of the most challenging tasks in natural language processing.
Approach:	They propose a method to extract the right sense from a sentence context . they propose to incorporate additional examples and definitions of related senses in WordNet .
Outcome:	The proposed method achieves better performance than baseline models on public benchmark datasets.

Copied to clipboard

Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong

When Generative Adversarial Networks Meet Sequence Labeling Challenges (2024.emnlp-main)

Challenge:	DialogStudio is the largest and most diverse collection of dialogue datasets . existing datasets lack diversity and comprehensiveness, authors say .
Approach:	They introduce DialogStudio: the largest and most diverse collection of dialogue datasets . DialogStuio aggregates more than 80 diverse dialogue dataset .
Outcome:	a new dataset is created to improve the quality and diversity of dialogue datasets . DialogStudio is the largest and most diverse collection of dialogue data .

Copied to clipboard

Yu Tong, Ge Chen, Guokai Zheng, Rui Li, Jiang Dazhi

Robustness in NLP

Challenge:	Existing approaches for sequence labeling use a feature extractor and sequence tagger . a recent study shows that SLGAN is versatile and highly effective .
Approach:	They propose a framework that harnesses the capabilities of Generative Adversarial Networks to address sequence labeling challenges.
Outcome:	The proposed framework exhibits strong adaptability to various sequence labeling tasks.

Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)

Copied to clipboard

Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian McAuley, Shuai Li

Challenge:	Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT).
Approach:	They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions.
Outcome:	The proposed methods are categorised by taxonomies of nodes, i.e., the X in CoX, and application tasks.

CAPE: A Chinese Dataset for Appraisal-based Emotional Generation in Large Language Models (2025.findings-naacl)

Copied to clipboard

June M. Liu, He Cao, Renliang Sun, Rui Wang, Yu Li, Jiaxing Zhang

Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network (2020.coling-main)

Challenge:	Existing LLMs fail to capture the nuances of human emotions, making their interactions seem impersonal or inadequate.
Approach:	They propose a two-stage automatic data generation framework to generate a Chinese dataset called CAPE . their data is a cognitive appraisal theory-based Emotional corpus that accounts for personal and situational factors.
Outcome:	The proposed framework can generate human-like responses in conversation with large language models.

Copied to clipboard

Hongjie Cai, Yaofeng Tu, Xiangsheng Zhou, Jianfei Yu, Rui Xia

Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning (2024.findings-naacl)

Challenge:	Aspect-based sentiment analysis studies focus on identifying sentiment polarities toward explicit aspects but ignore implicit aspects in text.
Approach:	They propose a hierarchy-sentiment hierarchy prediction problem to capture explicit and implicit aspects of aspect-based sentiment analysis.
Outcome:	The proposed model can consistently achieve the best results on four benchmarks.

Copied to clipboard

Rui Wang, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Handong Zhao, Junda Wu, Subrata Mitra, Lina Yao, Ricardo Henao

RePair: Automated Program Repair with Process-based Feedback (2024.findings-acl)

Challenge:	Pretrained language models (PLMs) are used for personalized federated learning . communication costs are high with large PLMs, and local training is expensive .
Approach:	They propose a framework for federated learning with pretrained language models . they propose 'discrete local search' and compression mechanism for local training .
Outcome:	The proposed framework achieves superior performance compared with baselines.

Copied to clipboard

Yuze Zhao, Zhenya Huang, Yixiao Ma, Rui Li, Kai Zhang, Hao Jiang, Qi Liu, Linbo Zhu, Yu Su

Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation (2020.acl-main)

Challenge:	Commercial-scale language models (LMs) have taken APR to unprecedented levels, but they are limited by parameters and humans interact with them through explicit prompts.
Approach:	They propose a method that utilizes process supervision to improve program repair by allowing users to input feedback from compilers and test cases.
Outcome:	The proposed method outperforms large outcome-based generation methods and is inspired by strategies used in programming competitions.

Copied to clipboard

Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang

Emotion Cause Extraction on Social Media without Human Annotation (2023.findings-acl)

Challenge:	Existing methods for dialogue policy optimization do not provide sufficient supervision signals at the end of dialogues.
Approach:	They propose to learn from state-action pairs of an optimal policy to provide turn-by-turn rewards.
Outcome:	The proposed approach outperforms competitive policy learning baselines on a benchmark multi-domain dataset.

Copied to clipboard

Debin Xiao, Rui Xia, Jianfei Yu

MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning (2026.acl-long)

Challenge:	Existing studies have focused on extracting emotion causes from news articles, but lack of fine-grained annotations has limited the ECE task.
Approach:	They propose a new ECE framework that extracts emotion causes from social media data without relying on human annotations.
Outcome:	The proposed framework achieves high extraction performance and generalizability without relying on human annotations.

Copied to clipboard

Weikang Shi, Aldrich Yu, Rongyao Fang, Houxing Ren, Ke Wang, Aojun Zhou, Changyao Tian, Xinyu Fu, Yuxuan Hu, Zimu Lu, Linjiang Huang, Si Liu, Rui Liu, Hongsheng Li

WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)

Challenge:	Existing approaches to visual chain-of-thought are limited by external tools or fail to generate high-fidelity diagrams.
Approach:	They propose a framework to enable large multimodal models with VCoT capabilities . they pre-train a model on a 15.2M-pair corpus and teach it how to leverage visual aids .
Outcome:	The proposed framework unlocks complex, human-like visual reasoning in large language models . it pre-trains the model on a 15.2M-pair corpus and fine-tunes it on MathCanvas-Instruct .

Copied to clipboard

Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (D19-1)

Challenge:	Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research .
Approach:	They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions .
Outcome:	The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench.

Copied to clipboard

Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher, Dragomir Radev

Distilling the Essence, Discarding the Dross: Improving Fairness in Multimodal Large Language Models via Historical Reflection-Guided Prompt Optimization (2026.findings-acl)

Challenge:	Generating SQL queries from user utterances is an important task to help end users acquire information from databases.
Approach:	They propose a context-dependent text-to-SQL generation task that edits previous queries . they use an utterance-table encoder and a table-aware decoder to incorporate context .
Outcome:	The proposed model is flexible to change individual tokens and robust to error propagation.

Copied to clipboard

Juncheng Hu, Jiming Yu, Rui Song, Kedi Lyu, Yingji Li, Zheli Liu

Latent Reasoning for Low-Resource Question Generation (2021.findings-acl)

Challenge:	Existing approaches to debiase MLLMs rely on handcrafted prompts that are brittle and difficult to generalize across tasks and bias types.
Approach:	They propose an adaptive self-debiasing framework that optimizes task-specific debiasers to suppress stereotypical outputs.
Outcome:	The proposed framework suppresses stereotypical outputs while maintaining performance.

Copied to clipboard

Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang

Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction (2022.naacl-main)

Challenge:	Existing approaches to multihop question generation require extensive data collection and decomposition.
Approach:	They propose a generative approach that optimizes the two-phase model without question decomposition data.
Outcome:	The proposed approach outperforms baselines on HOTPOTQA, a benchmark multi-hop question answering dataset.

Copied to clipboard

Junjie Li, Jianfei Yu, Rui Xia

A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023.acl-long)

Challenge:	Existing approaches to perform aspect and opinion co-extraction are difficult due to the lack of fine-grained annotations.
Approach:	They propose a framework to transfer knowledge from a labeled source domain to an unlabeled target domain.
Outcome:	The proposed framework is more effective than previous domain adaptation methods on three datasets.

Copied to clipboard

Wenjie Zheng, Jianfei Yu, Rui Xia, Shijin Wang

Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)

Challenge:	Recent studies have shown the importance of visual information in multi-party conversations due to the complexity of visual scenes.
Approach:	They propose a framework to extract face sequences as visual features from a real speaker's utterance and a pipeline method to extract the face sequence.
Outcome:	The proposed framework extracts face sequences of the real speaker of each utterance and improves emotion prediction on the MELD dataset.

Copied to clipboard

Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

Feature Structure Matching for Multi-source Sentiment Analysis with Efficient Adaptive Tuning (2024.lrec-main)

Challenge:	Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications.
Approach:	They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions.
Outcome:	The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions.

Copied to clipboard

Rui Li, Cheng Liu, Yu Tong, Jiang Dazhi

CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval (2025.findings-acl)

Challenge:	Existing domain matching methods tend to pull all feature instances close, but they are expensive and expensive to update.
Approach:	They propose to extract multi-layer features from a large pre-trained model and propose a dynamic parameter fusion module to exploit them for efficient and adaptive tuning.
Outcome:	The proposed framework is more robust and generalizable in the multi-source scenario.

Copied to clipboard

Heng Yu, Junfeng Kang, Rui Li, Qi Liu, Liyang He, Zhenya Huang, Shuanghong Shen, Junyu Lu

V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models (2026.findings-acl)

Challenge:	Recent techniques such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieleval (GDR) leverage LLMs to enhance retrieval performance but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLM.
Approach:	They propose a Context-Aware Generation-Augmented Retrieval approach which integrates corpus information into their generation process.
Outcome:	Experimental results show that CA-GAR outperforms existing methods on seven tasks and four non-English languages.

Copied to clipboard

Xiangxi Zheng, Linjie Li, Zhengyuan Yang, Ping Yu, Alex Jinpeng Wang, Rui Yan, Yuan Yao, Lijuan Wang

Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning (2025.findings-emnlp)

Challenge:	Existing static image-text benchmarks are insufficient for evaluating multimodal large language models’ dynamic perception and interactive reasoning abilities.
Approach:	They propose a game-based evaluation framework to assess multimodal large language models’ visual reasoning in dynamic, continuous-space environments.
Outcome:	The proposed framework systematically assesses MLLMs’ visual reasoning in dynamic, continuous-space environments.

Copied to clipboard

Huilin Deng, Ding Zou, Xinghao Zhao, Rui Ma, Yanming Guo, Yang Cao, Yu Kang

Multimedia Event Extraction with LLM Knowledge Editing (2025.emnlp-main)

Challenge:	State-of-the-art vision-language models require massive scaling that limits practical deployment.
Approach:	They propose to use supervised fine-tuning to train small-scale vision-language models but face out-of-domain collapse when trained with traditional supervised learning (SFT).
Outcome:	Experiments show that curr-reFT achieves state-of-the-art performance across visual tasks in both in- and out-of domain settings and benchmarks.

Copied to clipboard

Jiaao Yu, Yijing Lin, Zhipeng Gao, Xuesong Qiu, Lanlan Rui

Event Extraction

Challenge:	Existing multimodal event extraction methods focus on weakly aligning features from wellpretrained unimodal encoders, resulting in redundant feature perception.
Approach:	They propose a multimodal event extraction strategy with a redundant feature selection mechanism that enhances event understanding ability of multimodal large language models.
Outcome:	The proposed method outperforms the state-of-the-art (SOTA) baselines on the M2E2 benchmark.

FedMABench: Benchmarking Mobile GUI Agents on Decentralized Heterogeneous User Data (2025.emnlp-main)

Copied to clipboard

WenHao Wang, Zijie Yu, Rui Ye, Jianqing Zhang, Guangyi Liu, Liang Liu, Siheng Chen, Yanfeng Wang

Challenge:	Mobile GUI agents have attracted tremendous research participation recently. traditional approaches to mobile agent training rely on centralized data collection.
Approach:	They propose a benchmark for federated training and evaluation of mobile GUI agents . they find that federation algorithms consistently outperform local training .
Outcome:	The first benchmark for federated training and evaluation of mobile GUI agents is released . it features 6 datasets with 30+ subsets, 8 federation algorithms, 10+ base models, and over 800 apps across 5 categories .

GBT: Generative Boosting Training Approach for Paraphrase Identification (2023.findings-emnlp)

Copied to clipboard

Rui Peng, Zhiling Jin, Yu Hong

Text Generation

Challenge:	Paraphrase Identification (PI) is a fundamental natural language understanding task with non-trivial challenges.
Approach:	They propose a Generative Boosting Training approach for Paraphrase Identification (PI) they use a seq2seq model to perform DA on misclassified instances periodically .
Outcome:	The proposed method outperforms state-of-the-art PI models on English and Chinese PI tasks with good efficiency and effectiveness.

The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)

Copied to clipboard

Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam

Natural Language Interfaces

Challenge:	Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others.
Approach:	They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM.
Outcome:	The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants.

Completing A Systematic Review in Hours instead of Months with Interactive AI Agents (2025.acl-long)

Copied to clipboard

Rui Qiu, Shijie Chen, Yu Su, Po-Yin Yen, Han Wei Shen

Challenge:	Systematic reviews (SRs) are vital for evidence-based practice in high stakes disciplines, such as healthcare.
Approach:	They propose a human-centered interactive AI agent powered by large language models that partitions a large literature corpus based on semantics and employs .
Outcome:	InsightAgent improves quality of synthesized SRs by 27.2%, reaching 79.7% of human-written quality.

Eval-RAR: Evaluation-Driven Retrieval-Augmented Reasoning via Reinforcement Learning (2026.findings-acl)

Copied to clipboard

Heng Yu, Rui Li, Qi Liu, Wenjun Feng, Junfeng Kang, Yi Zhan

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)

Challenge:	Existing methods for retrieval-augmented generation fail to provide explicit supervision for internal reasoning process.
Approach:	They propose an Evaluation-driven Retrieval-Augmented Reasoning framework that uses reinforcement learning and a fine-grained evaluation reward to optimize the process.
Outcome:	Eval-RAR outperforms existing methods on QA benchmarks on seven single-hop and multi-hop tasks.

Copied to clipboard

Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev

Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification (2021.emnlp-main)

Challenge:	Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models.
Approach:	They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries .
Outcome:	The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting.

Copied to clipboard

Hao Chen, Rui Xia, Jianfei Yu

Controllable Contamination Detection for Reliable LLM Evaluation with Statistical Guarantees (2026.acl-long)

Challenge:	Existing approaches to improve generalization ability by augmenting training data with synonymous examples or adding random noises to word embeddings cannot address spurious association problem.
Approach:	They propose an end-to-end reinforcement learning framework which jointly performs counterfactual data generation and dual sentiment classification.
Outcome:	The proposed framework outperforms strong data augmentation baselines on several benchmark sentiment classification datasets.

Copied to clipboard

Zheng Zhang, Qi Liu, Siyuan Liang, Ning Li, Zirui Hu, Weibo Gao, Rui Li, Zhenya Huang, Leszek Rutkowski, Baosheng Yu, Dacheng Tao

Logic-Consistency Text Generation from Semantic Parses (2021.findings-acl)

Challenge:	Existing training data detectors fail to detect clean samples from contaminated test sets . existing methods fail to identify clean samples due to black-box nature of LLMs .
Approach:	They propose a framework that detects and filters contaminated evaluation data . they propose 'failure detection' to reduce the proportion of contaminated samples mistakenly retained .
Outcome:	The proposed framework reduces false discovery rate (FDR) under valid FDR control while maintaining evaluation consistency.

Copied to clipboard

Chang Shu, Yusen Zhang, Xiangyu Dong, Peng Shi, Tao Yu, Rui Zhang

Text Generation

Challenge:	Text generation from semantic parses is challenging due to the complexity of the inner logic and the lack of automatic evaluation metrics for logic consistency.
Approach:	They propose a framework for logic consistent text generation from semantic parses that employs iterative training procedures and quality control.
Outcome:	The proposed framework enhances logic consistency and human evaluation on two benchmark datasets.

Generalizable and Explainable Dialogue Generation via Explicit Action Learning (2020.findings-emnlp)

Copied to clipboard

Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang

Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning (2026.findings-acl)

Challenge:	Conditioned response generation for task-oriented dialogues implicitly optimizes task completion and language quality.
Approach:	They propose to learn natural language actions that represent utterances as a span of words.
Outcome:	The proposed approach outperforms latent action baselines on a multi-domain dataset.

Copied to clipboard

Haolin Liu, Dian Yu, Sidi Lu, Yujun Zhou, Rui Liu, Zhenwen Liang, Haitao Mi, Chen-Yu Wei, Dong Yu

Reflection in the Dark: Exposing and Escaping the Black Box in Reflective Prompt Optimization (2026.acl-srw)

Challenge:	Existing reinforcement learning methods rely on sparse outcome rewards, which fail to credit correct intermediate steps in partially successful solutions.
Approach:	They propose a process reward model that rewards correct steps only when they detect errors . they propose VPPO, which rewards the correct prefix and an erroneous suffix .
Outcome:	a new approach outperforms sparse-reward RL and prior PRM-guided baselines on Pass@1 and Pass@K . a process reward model (PRM) outperformed sparser-rebound RL on multiple reasoning benchmarks .

Copied to clipboard

Shiyan Liu, Qifeng Xia, Qiyun Xia, Yisheng Liu, Xinyu Yu, Rui Qu

Breakthrough from Nuance and Inconsistency: Enhancing Multimodal Sarcasm Detection with Context-Aware Self-Attention Fusion and Word Weight Calculation. (2024.lrec-main)

Challenge:	Automatic prompt optimization (APO) is a powerful paradigm for improving LLM performance without manual prompt engineering.
Approach:	They propose a framework that decouples hypothesis generation from prompt rewriting . they propose VISTA framework that recovers accuracy to 87.57% on same defective seed .
Outcome:	The proposed framework outperforms baselines on GSM8K and AIME2025 on a defective seed.

Copied to clipboard

Hongfei Xue, Linyan Xu, Yu Tong, Rui Li, Jiali Lin, Dazhi Jiang

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)

Challenge:	Existing methods for sarcasm detection rely on feature concatenation to fuse different modalities or model inconsistencies among modalités.
Approach:	They propose to use Context-Aware Self-Attention Fusion to integrate local and momentary multimodal information into specific words to illustrate the inconsistencies between connotation and denotation.
Outcome:	The proposed method achieves an accuracy of 76.9 and an F1 score of 76.1 on the MUStARD dataset, surpassing the current state-of-the-art IWAN model by 1.7 and 1.6 respectively.

Copied to clipboard

Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

A State-independent and Time-evolving Network for Early Rumor Detection in Social Media (2020.emnlp-main)

Challenge:	Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context.
Approach:	They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts.
Outcome:	The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context.

Copied to clipboard

Rui Xia, Kaizhou Xuan, Jianfei Yu

Challenge:	Existing methods to rumor detection ignored dynamical evolution of an event and failed to capture its unique features in different states.
Approach:	They propose a state-independent and time-evolving Network (STN) for rumor detection based on fine-grained event state detection and segmentation.
Outcome:	The proposed framework can significantly improve the rumor detection accuracy in comparison with some strong baseline systems.

PURE: Aligning LLM via Pluggable Query Reformulation for Enhanced Helpfulness (2024.findings-emnlp)

Copied to clipboard

Wenjin Yao, Yidong Wang, Zhuohao Yu, Rui Xie, Shikun Zhang, Wei Ye

Self-Generated Critiques Boost Reward Modeling for Language Models (2025.naacl-long)

Challenge:	Large language models (LLMs) depend on vast amounts of text data sourced from the Internet for their training.
Approach:	They propose a new alignment paradigm that reformulates risky queries into highly relevant yet harmless ones before feeding them into LLMs.
Outcome:	The proposed approach eliminates the high costs of training base LLMs and achieves a promising balance of harmlessness and helpfulness.

Copied to clipboard

Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou

End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning (2020.emnlp-main)

Challenge:	Existing reward models produce scalar scores and struggle to incorporate critiques in a natural language format.
Approach:	They propose a framework that predicts critiques and rewards using self-generated critiques without extra supervision.
Outcome:	The proposed framework improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges.

Copied to clipboard

Zixiang Ding, Rui Xia, Jianfei Yu

Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations (2020.emnlp-main)

Challenge:	Existing methods to extract potential pairs of emotions ignore the fact that the cause and the emotion it triggers are inseparable.
Approach:	They propose two frameworks that combine multi-label learning and multi-labeled learning to extract emotion clauses . they evaluate a benchmark emotion cause corpus and find the best performance .
Outcome:	The proposed frameworks achieve the best performance among all compared systems on the ECPE task.

Copied to clipboard

Jianfei Yu, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu, Rui Xia

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale (2026.acl-industry)

Challenge:	Existing approaches to rumor verification and stance classification fail to exploit intertask dependencies .
Approach:	They propose a Hierarchical Transformer model which uses BERT to obtain thread representations . they propose 'coupled' transformer modules to capture intertask interactions and a post-level attention layer to use predicted stance labels for RV.
Outcome:	The proposed model outperforms existing methods on two benchmark datasets.

Copied to clipboard

Jun Wang, Ziyin Zhang, Rui Wang, Hang Yu, Peng Di, Rui Wang

Challenge:	a 5minute downtime for an incident could result in a loss of 40 million dollars and erosion of user trust.
Approach:	They propose a multi-stage event unification engine that synergizes efficient indexing techniques with Large Language Models (LLMs) to make informed decisions on event merging.
Outcome:	The proposed system outperforms baseline methods in routing accuracy, clustering quality, and Signal-to-Noise Ratio.

Generative Emotion Cause Triplet Extraction in Conversations with Commonsense Knowledge (2023.findings-emnlp)

Copied to clipboard

Fanfan Wang, Jianfei Yu, Rui Xia

Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection (2026.acl-long)

Challenge:	Existing studies on ECTEC focus on Causal Emotion Entailment and Emotion-Cause Pair Extraction in Conversations.
Approach:	They propose to decompose the ECTEC task into multiple subtasks and solve them in a pipeline manner.
Outcome:	The proposed model outperforms competing systems on two benchmark datasets.

Copied to clipboard

Junjun Pan, Yixin Liu, Rui Miao, Kaize Ding, Yu Zheng, Quoc Viet Hung Nguyen, Alan Wee-Chung Liew, Shirui Pan

A Joint Coreference-Aware Approach to Document-Level Target Sentiment Analysis (2024.acl-long)

Challenge:	Existing graph anomaly detection methods rely on coarse sentence-level information and overlook fine-grained lexical cues, limiting their reliability and real-world applicability.
Approach:	They propose an explainable and fine-grained safeguarding framework for detecting malicious agents in multi-agent systems (MAS) to incorporate both coarse and fine lexical information for anomalous agent identification.
Outcome:	Extensive experiments across diverse MAS topologies and attack scenarios demonstrate robust detection performance and strong interpretability of XG-Guard.

Copied to clipboard

Hongjie Cai, Heqing Ma, Jianfei Yu, Rui Xia

Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)

Challenge:	Existing work on aspect-based sentiment analysis (ABSA) focuses on sentence level, document level ABSA is more practical and requires holistic document-level understanding capabilities.
Approach:	They propose a learning framework to jointly model the DTSA task and the coreference resolution task using ChatGPT.
Outcome:	The proposed framework reduces the reliance on annotated coreference information and alleviates evaluation bias caused by missing coreference targets.

Copied to clipboard

Chengkai Huang, Yu Xia, Rui Wang, Kaige Xie, Tong Yu, Julian McAuley, Lina Yao

Comparative Opinion Quintuple Extraction from Product Reviews (2021.emnlp-main)

Challenge:	Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model.
Approach:	They propose to determine whether the model is knowledgeable on a query via inspecting the (contextualized) pre-trained token embeddings of LLMs.
Outcome:	Experiments show that the proposed approach performs better than previous approaches on various benchmarks.

Copied to clipboard

Ziheng Liu, Rui Xia, Jianfei Yu

Beyond Modality Collapse: Taming Guided Modality Entropy for Omni-modal Emotion Reasoning (2026.findings-acl)

Challenge:	Comparative opinion mining is an important task in opinion mining.
Approach:	They propose a task to extract comparative opinion quintuples from product reviews . they propose supplementary annotations and construct three datasets for the task .
Outcome:	The proposed method outperforms baseline systems on three datasets and represents a strong benchmark for COQE.

Copied to clipboard

Xian Zhao, Rui Hu, Yuxiang Zhang, Delai Qiu, Yining Wang, Shengping Liu, Jian Yu, Jitao Sang

MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion (2025.acl-long)

Challenge:	EmoOmni is a data paradigm for omni-modal large language models that can be used for emotion reasoning.
Approach:	They propose a data paradigm that interleaves guided tokens into reasoning traces to enforce structured evidence extraction.
Outcome:	The proposed paradigm over-relys on a dominant modality while neglecting complementary cues.

Copied to clipboard

Qizhi Pei, Lijun Wu, Zhuoshi Pan, Yu Li, Honglin Lin, Chenlin Ming, Xin Gao, Conghui He, Rui Yan

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have shown impressive progress in mathematical problem-solving . current approaches to enhance mathematical reasoning focus on instance-level modifications .
Approach:	They propose a framework that enhances mathematical reasoning through cross-problem instruction synthesis.
Outcome:	The proposed framework boosts mathematical reasoning by 18.0 points while maintaining high data efficiency.

Copied to clipboard

Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Multilinguality

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation (2025.acl-long)

Copied to clipboard

Rui Li, Liyang He, Qi Liu, Zheng Zhang, Heng Yu, Yuyang Ye, Linbo Zhu, Yu Su

Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020.acl-main)

Challenge:	Existing query augmentation methods face knowledge update lag and hallucinations in large language models (LLMs) Existing methods face two key challenges: (1) separation of query augmented and encoding tasks, which hinders information sharing and introduces cumulative errors; (2) difficulty of selecting optimal augmentation strategy for different scenarios.
Approach:	They propose a unified framework for query understanding in RAG that integrates internal and external knowledge to enhance query augmentation and encoding tasks.
Outcome:	The proposed framework outperforms traditional query augmentation methods in five knowledge-intensive benchmark tasks in both closed and open domain question answering.

Copied to clipboard

Jianfei Yu, Jing Jiang, Li Yang, Rui Xia

GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding (2025.acl-long)

Challenge:	Existing methods for named entity recognition ignore visual context bias . NER is a key component of many information extraction tasks .
Approach:	They propose to use a multimodal interaction module to generate word-aware visual representations and leverage purely text-based entity span detection as an auxiliary module to guide the final predictions.
Outcome:	The proposed approach achieves state-of-the-art on two benchmark datasets.

Copied to clipboard

Ziyin Zhang, Hang Yu, Sage Lee, Peng Di, Jianguo Li, Rui Wang

CoMMIT: Coordinated Multimodal Instruction Tuning (2025.emnlp-main)

Challenge:	Programming languages have rich semantics that are represented by graphs and not available from the surface form of source code.
Approach:	They propose to use graph neural networks and cross-modal alignment technologies to inject structural information of code into LLMs as an auxiliary task during finetuning.
Outcome:	The proposed framework improves on five code tasks with six different baseline LLMs, while incurring no cost at inference time.

Copied to clipboard

Xintong Li, Junda Wu, Tong Yu, Rui Wang, Yu Wang, Xiang Chen, Jiuxiang Gu, Lina Yao, Julian McAuley, Jingbo Shang

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)

Challenge:	et al., 2024) show that multimodal instruction tuning is more effective than baselines.
Approach:	They propose a multimodal balance coefficient that enables quantitative measurement of the balance of learning . they propose auxiliary regularization on the gradient to promote updating with larger step sizes .
Outcome:	The proposed method is more effective than baselines in MLLM instruction tuning.

Copied to clipboard

Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu

Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)

Challenge:	Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately .
Approach:	They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes .
Outcome:	The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show .

Copied to clipboard

Chenggong Gong, Jianfei Yu, Rui Xia

Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets (2023.findings-emnlp)

Challenge:	Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task.
Approach:	They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features .
Outcome:	The proposed method improves on the state-of-the-art in two aspects of the ABSA task.

Copied to clipboard

Han Jiang, Rui Wang, Zhihua Wei, Yu Li, Xinpeng Wang

Text Generation

Challenge:	Existing methods for opinion summarization are deficient in epitomizing extensive reviews and offering opinion summaries from various angles.
Approach:	They propose a supervised opinion summarization framework that takes sentiment orientation into account and trains the summarizer to learn from sub-optimal and optimal review subsets.
Outcome:	The proposed framework generates pros, cons, and verdict summaries from hundreds of input reviews.

CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)

Copied to clipboard

Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu, Rui Yan, Yuan Yao, Peng Hou, Anxiang Zeng, Alex Jinpeng Wang

Few-Shot Class-Incremental Learning for Named Entity Recognition (2022.acl-long)

Challenge:	Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data .
Approach:	They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation.
Outcome:	The proposed framework outperforms open-source baselines and is competitive with GPT-5.

Copied to clipboard

Rui Wang, Tong Yu, Handong Zhao, Sungchul Kim, Subrata Mitra, Ruiyi Zhang, Ricardo Henao

How do Visual Attributes Influence Web Agents? A Comprehensive Evaluation of User Interface Design Factors (2026.findings-acl)

Challenge:	Existing models of Named Entity Recognition (NER) are trained on large datasets with predefined entity classes, but data of new classes arrives constantly. Existing work on NER relies on the assumption that there exists abundance of labeled data for the training of new class.
Approach:	They propose a few-shot class-incremental learning problem where NER model is trained with only few labeled samples of the new classes without forgetting knowledge of the old ones.
Outcome:	The proposed model improves over existing baselines by reconstructing training data of old classes and real data from the training set.

Copied to clipboard

Kuai Yu, Naicheng Yu, Han Wang, Rui Yang, Huan Zhang

Cross-Domain Data Augmentation with Domain-Adaptive Language Modeling for Aspect-Based Sentiment Analysis (2023.acl-long)

Challenge:	Existing studies on the effect of environmental variation on web agents have focused on robustness to adversarial attacks with less attention to agents’ preferences in benign scenarios.
Approach:	They propose a controlled evaluation pipeline to quantify how visual attributes influence web-agent decision-making by comparing variants and browsing interactions.
Outcome:	Extensive experiments on 8 variant families, 5 real-world websites and 4 representative web agents show that background color contrast, item size, position, and card clarity have a strong influence on agents’ actions, whereas font styling, text color, and item image clarity exhibit minor effects.

Copied to clipboard

Jianfei Yu, Qiankun Zhao, Rui Xia

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)

Challenge:	Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect-sentiment pairs in sentences from a target domain.
Approach:	They propose a domain-adaptive language model to generate labeled data from a source domain.
Outcome:	The proposed approach outperforms existing methods on ABSA and Aspect Extraction tasks.

Copied to clipboard

Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei

Multi-Domain Dialogue Acts and Response Co-Generation (2020.acl-main)

Challenge:	Existing work shows that pre-trained models can improve in various natural language processing tasks.
Approach:	They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data.
Outcome:	The proposed framework is superior to existing models on speech-to-text processing tasks.

Copied to clipboard

Kai Wang, Junfeng Tian, Rui Wang, Xiaojun Quan, Jianxing Yu

UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models (2025.acl-long)

Challenge:	Existing pipeline approaches for task-oriented dialogue systems tend to predict multiple dialogue acts first and use them to assist response generation.
Approach:	They propose a neural co-generation model that generates dialogue acts and responses concurrently and preserves semantic structures of multi-domain dialogue acts.
Outcome:	The proposed model improves over state-of-the-art models in automatic and human evaluations on a large-scale dataset.

Copied to clipboard

Boyang Xue, Fei Mi, Qi Zhu, Hongru Wang, Rui Wang, Sheng Wang, Erxin Yu, Xuming Hu, Kam-Fai Wong

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)

Challenge:	Large Language Models (LLMs) often struggle to accurately express factual knowledge, especially in cases where the knowledge boundaries are ambiguous.
Approach:	They propose a framework that leverages Uncertainty estimations to represent knowledge boundaries and incorporates these representations into prompts for LLMs to Align with factual knowledge.
Outcome:	The proposed framework significantly improves the LLMs’ capacities to confidently answer known questions and refuse unknown questions on both in-domain and out-of-domain tasks.

Copied to clipboard

Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev

MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)

Challenge:	CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems.
Approach:	They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries .
Outcome:	The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains.

Copied to clipboard

Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu

AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference (2024.findings-emnlp)

Challenge:	Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents .
Approach:	They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning.
Outcome:	The proposed framework outperforms existing methods in performance and inference efficiency.

Copied to clipboard

Yang Han, Yiming Wang, Rui Wang, Lu Chen, Kai Yu

Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets (2023.findings-acl)

Challenge:	Text summarization tasks employ Pre-trained Language Models (PLMs) to fit diverse datasets.
Approach:	They propose a human summarization preference alignment framework to align PLMs with human preferences.
Outcome:	The proposed framework narrows the gap between automatic and human evaluations by integrating three components.

Copied to clipboard

Rui Wang, Tong Yu, Junda Wu, Handong Zhao, Sungchul Kim, Ruiyi Zhang, Subrata Mitra, Ricardo Henao

Sparse Frame Grouping Network with Action Centered for Untrimmed Video Paragraph Captioning (2023.findings-emnlp)

Challenge:	a federated domain adaptation approach is used to learn with NER datasets from multiple platforms while not violating data privacy.
Approach:	They propose to use a distillation approach to facilitate knowledge transfer across platforms.
Outcome:	The proposed model performs better in the clinic domain.

Copied to clipboard

Guorui Yu, Yimin Hu, Yuejie Zhang, Rui Feng, Tao Zhang, Shang Gao

RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training (2023.findings-emnlp)

Challenge:	Existing methods for paragraph captioning videos without event ground truths generate one sentence for each event, but without event labels, it is difficult to locate the transitions between events and minimize repetition.
Approach:	They propose a module that dynamically groups event information with the help of action information for the entire video and excludes redundant frames within pre-defined clips.
Outcome:	The proposed module outperforms the state-of-the-art methods on all metrics.

Copied to clipboard

Jaehyung Kim, Yuning Mao, Rui Hou, Hanchao Yu, Davis Liang, Pascale Fung, Qifan Wang, Fuli Feng, Lifu Huang, Madian Khabsa

Robustness in NLP

Challenge:	Several perspectives of robustness for pre-trained language models have been studied independently, but lacking a unified consideration in multiple perspectives.
Approach:	They propose a technique to enhance the multi-perspective robustness of LMs by introducing adversarial perturbation while the model parameters are selectively updated upon their relative importance.
Outcome:	The proposed technique improves the robustness of LMs by incorporating four perspectives on model robustness.

TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation (N18-2)

Copied to clipboard

Tao Yu, Zifan Li, Zilin Zhang, Rui Zhang, Dragomir Radev

Aspect-Based Sentiment Analysis with Syntax-Opinion-Sentiment Reasoning Chain (2025.coling-main)

Challenge:	Existing systems that can understand natural language questions and generate corresponding SQL queries are not able to do this.
Approach:	They propose a novel approach which formats the problem as a slot filling task in a more reasonable way and utilizes type information to better understand rare entities and numbers in the questions.
Outcome:	The proposed approach outperforms the prior art on the WikiSQL dataset and can reach 82.6% accuracy, a 17.5% improvement compared to the previous content-sensitive model.

Copied to clipboard

Rui Fan, Shu Li, Tingting He, Yu Liu

Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis (2022.acl-long)

Challenge:	Syntactic structures are crucial for capturing aspect-opinion relationships . syntactically based models struggle with linguistic complexities .
Approach:	They propose a syntactic-opinion-sentiment reasoning framework that leverages syntaktic information to improve ABSA performance.
Outcome:	The proposed framework improves ABSA performance, though smaller LLMs exhibit weaker performance.

Copied to clipboard

Yan Ling, Jianfei Yu, Rui Xia

Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions (2021.acl-long)

Challenge:	Existing approaches to multimodal Aspect-Based Sentiment Analysis (MABSA) ignore crossmodalalignment and use pre-trained visual and textual models.
Approach:	They propose a multimodal multimodal encoder-decoder framework for MABSA that uses a unified multimodal decoder architecture for all the pretrainingand downstream tasks.
Outcome:	The proposed framework outperforms state-of-the-art approaches on three MABSA subtasks.

Copied to clipboard

Hongjie Cai, Rui Xia, Jianfei Yu