Papers by Jun Yang

Regret-Now: A Physics-Inspired Regret Framework for Temporal Knowledge Graph Question Answering with LLMs (2026.findings-acl)

Challenge:	On-policy distillation (OPD) requires expensive on-the-fly sampling of the student policy during training, which substantially increases training cost.
Approach:	They propose to use on-policy distillation to sample trajectories from student model . they propose to terminate the sampling early during distillation .
Outcome:	The proposed method matches the performance of full OPD in long reasoning outputs while reducing training FLOP by 2x–40x.

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Danyu Huang, Yao Zhang, Jun Wang, Zhenglu Yang

Structured Data in NLP

Challenge:	Large Language Models have impressive results in general reasoning tasks, but they still exhibit a lack of dynamic error-correction.
Approach:	They propose a temporal reasoning framework that uses the principle of minimum potential energy to model the reasoning process as a dynamic trajectory moving toward a more stable state.
Outcome:	The proposed framework shows consistent gains over strong baselines on two standard TKGQA benchmarks.

Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs (2025.emnlp-main)

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Jun Bai, Minghao Tong, Yang Liu, Zixia Jia, Zilong Zheng

MoNMT: Modularly Leveraging Monolingual and Bilingual Knowledge for Neural Machine Translation (2024.lrec-main)

Challenge:	Context faithfulness is essential for reliable reasoning in context-dependent scenarios.
Approach:	They propose a method that identifies and fine-tunes context-faithful experts . they propose 'context-faither fine- tuning' which selectively fine- tunes them .
Outcome:	The proposed method identifies experts with specialization in context utilization and improves context grounding.

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Jianhui Pang, Baosong Yang, Derek F. Wong, Dayiheng Liu, Xiangpeng Wei, Jun Xie, Lidia S. Chao

Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)

Challenge:	Existing models for multi-domain translation tasks only use monolingual data, whereas bilingual data is indispensable for improving the models.
Approach:	They propose a modular strategy that facilitates the cooperation of monolingual and bilingual knowledge in translation tasks by avoiding catastrophic forgetting.
Outcome:	The proposed model exhibits superior generalization and robustness over the conventional approach.

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Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan

Fantastic Expressions and Where to Find Them: Chinese Simile Generation with Multiple Constraints (2023.acl-long)

Challenge:	Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it .
Approach:	They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers.
Outcome:	The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy .

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Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Xiangpeng Wei, Zhengyuan Liu, Jun Xie

Information Retrieval

Challenge:	Existing attempts to generate similes as context-free tasks are not suitable for simile generation . however, simile generated under such settings might be undesirable, we argue .
Approach:	They propose a model to generate a simile with multiple simile elements . they propose to use a vehicle retrieval module to obtain the explicable comparison .
Outcome:	The proposed model can generate a simile with multiple simile elements, e.g., context and vehicle.

Unified Thinker: A General Reasoning Core for Image Generation (2026.acl-long)

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Sashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang, Yue Cao, Junpeng Ma, Yinchao Ma, Jun Song, Tiezheng Ge, Cheng Yu, Bo Zheng, Zhou Zhao

Context-Interactive Pre-Training for Document Machine Translation (2021.naacl-main)

Challenge:	generative models struggle with logic-intensive instruction following, exposing a persistent reasoning–execution gap.
Approach:	They propose a task-agnostic reasoning architecture for general image generation . they propose pixel-level feedback to ground the Thinker's policy in pixel feedback .
Outcome:	The proposed system significantly improves image reasoning and generation quality.

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Pengcheng Yang, Pei Zhang, Boxing Chen, Jun Xie, Weihua Luo

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models (2025.findings-emnlp)

Challenge:	Document machine translation typically suffers from a lack of document-level bilingual data.
Approach:	They propose a document machine translation model that incorporates contextual information into the training signals by capturing cross-sentence dependency within the target document and cross sentence translation to make better use of contextual information.
Outcome:	The proposed model outperforms baselines on three benchmark datasets and significantly outperformed previous approaches.

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Yuhan Kang, Yang Shi, Mei Wen, Jun He, Jianchao Yang, Zeyu Xue, Jing Feng, Xinwang Liu

Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes (2022.coling-1)

Challenge:	a novel post-training pruning method relies on the Hessian matrix to perform pruning . current pruning methods are computationally intensive and lack performance due to second-order derivative calculations.
Approach:	They propose a Hessian-free weight pruning method that reduces computational burden . they use an Exponentially Weighted Moving Average technique to bypass weight sorting .
Outcome:	The proposed method achieves hardware-efficient model compression by eliminating computational intensive calculations.

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Bin Ji, Shasha Li, Shaoduo Gan, Jie Yu, Jun Ma, Huijun Liu, Jing Yang

Exploring Union and Intersection of Visual Regions for Generating Questions, Answers, and Distractors (2024.emnlp-main)

Challenge:	Existing prototypical networks for named entity recognition suffer from label dependency and tightly distributed prototypes, thus causing misclassifications.
Approach:	They propose an Entity-level Prototypical Network enhanced by dispersedly distributed prototypes to build entity-level prototypes and distribute them dispersionally.
Outcome:	The proposed system outperforms the previous models on two evaluation tasks and the Few-NERD settings in terms of overall performance.

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Wenjian Ding, Yao Zhang, Jun Wang, Adam Jatowt, Zhenglu Yang

Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification (2021.emnlp-main)

Challenge:	Existing efforts to generate image-related questions, correct answers, or challenge distractors are limited.
Approach:	They propose to put the spotlight on different image regions to diversify QADs . they propose a framework that generates each QAD based on a recurrent multimodal encoder .
Outcome:	The proposed framework puts the spotlight on different image regions to diversify QADs.

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Pengfei Cao, Yubo Chen, Yuqing Yang, Kang Liu, Jun Zhao

Challenge:	Existing studies focus on identifying event factuality at sentence level, which leads to conflicts between different mentions of the same event.
Approach:	They propose a document-level event factuality identification model that uses local uncertainty and global structure to model event factuality.
Outcome:	The proposed method outperforms existing models on two widely used datasets.

Tailoring Vaccine Messaging with Common-Ground Opinions (2024.findings-naacl)

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Rickard Stureborg, Sanxing Chen, Roy Xie, Aayushi Patel, Christopher Li, Chloe Zhu, Tingnan Hu, Jun Yang, Bhuwan Dhingra

Challenge:	Vaccine interventions aim to answer concerns expressed about vaccination.
Approach:	They propose a dataset to evaluate how well responses are tailored to a common-ground opinion . they find that GPT-4-Turbo performs significantly better than others .
Outcome:	The proposed dataset outperforms fine tuned LLMs on the task of tailoring vaccine responses to common-ground opinions.

A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech (2021.acl-long)

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Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Nianwen Xue, Ji-Rong Wen

MISP-Meeting: A Real-World Dataset with Multimodal Cues for Long-form Meeting Transcription and Summarization (2025.acl-long)

Challenge:	Existing work regards dropped pronoun recovery and conversational discourse parsing as two separate tasks and tackles them separately.
Approach:	They propose a neural model for dropped pronoun recovery and conversational discourse parsing in Chinese conversational speech.
Outcome:	The proposed model outperforms the state-of-the-art models on a new dataset . the proposed model is based on linguistic and semantic information from Chinese conversational speech .

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HangChen HangChen, Chao-Han Huck Yang, Jia-Chen Gu, Sabato Marco Siniscalchi, Jun Du

Multiple Knowledge-Enhanced Interactive Graph Network for Multimodal Conversational Emotion Recognition (2024.findings-emnlp)

Challenge:	Existing systems that can recognize spoken content, extract key information, and produce concise summaries are lacking in meeting transcription and summarization.
Approach:	They propose a multimodal dataset that integrates information from speech, vision, and text modalities to facilitate automatic meeting transcription and summarization (AMTS).
Outcome:	The proposed dataset reduces the character error rate (CER) by 36.60% to 20.27% and improves speech recognition and large language models.

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Geng Tu, Jun Wang, Zhenyu Li, Shiwei Chen, Bin Liang, Xi Zeng, Min Yang, Ruifeng Xu

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs (2025.findings-acl)

Challenge:	Multimodal Emotion Recognition in Conversations models struggle due to lack of Common Sense Knowledge (CSK).
Approach:	They propose a multimodal approach to integrate multiple knowledge into the edge representations by integrating textual and visual CSK.
Outcome:	The proposed model outperforms state-of-the-art methods on two popular datasets.

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Hongming Yang, Shi Lin, Jun Shao, Changting Lin, Donghai Zhu, Meng Han, Qinglei Kong

On the Robustness of Reading Comprehension Models to Entity Renaming (2022.naacl-main)

Challenge:	Existing prompt optimization methods rely on extensive manual effort or meta-cognitive abilities, making them less effective for LwLLMs.
Approach:	They propose a direct behavior optimization parameter that transforms the optimization of complex prompts into discrete, quantifiable execution sequences using a gradient-free Monte Carlo Tree Search.
Outcome:	The proposed method outperforms current prompt optimization methods on seven challenging tasks where state-of-the-art LLMs excel but LwLLMs generally underperform.

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Jun Yan, Yang Xiao, Sagnik Mukherjee, Bill Yuchen Lin, Robin Jia, Xiang Ren

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

Challenge:	SpanBERT model is more robust than RoBERTa, despite having similar accuracy on unperturbed test data.
Approach:	They propose a pipeline to replace entity names with names from a variety of sources.
Outcome:	The proposed model performs worse when entities are renamed, the authors show . SpanBERT, which is pretrained with span-level masking, is more robust than RoBERTa .

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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

Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources (2021.findings-acl)

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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Taolin Zhang, Chengyu Wang, Minghui Qiu, Bite Yang, Zerui Cai, Xiaofeng He, Jun Huang

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)

Challenge:	MRC is a popular task in NLP, aiming to understand a passage and answer the relevant questions.
Approach:	They propose a multi-target machine learning task for the medical domain that predicts answers to medical questions and corresponding support sentences from medical information sources simultaneously.
Outcome:	The proposed model outperforms baselines by fusing context-aware and knowledge-awful token representations.

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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou

Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning (2025.acl-long)

Challenge:	Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text.
Approach:	They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types.
Outcome:	The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages.

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Yingjie Zhu, Xuefeng Bai, Kehai Chen, Yang Xiang, Jun Yu, Min Zhang

Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension (2022.acl-long)

Challenge:	Existing methods for enhancing understanding and reasoning abilities in graphbased tasks focus on specific graph types or tasks, posing challenges in designing versatile systems suitable for various tasks and graphs across diverse domains.
Approach:	They propose a structure-aware fine-tuning framework to enhance LVLMs with structure learning abilities through three self-supervised learning tasks.
Outcome:	Extensive evaluations on 14 LVLMs reveal that LVLs are weak in basic graph understanding and reasoning tasks, particularly those concerning relational or structurally complex information.

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Huibin Zhang, Zhengkun Zhang, Yao Zhang, Jun Wang, Yufan Li, Ning Jiang, Xin Wei, Zhenglu Yang

Rethinking Multi-Modal Alignment in Multi-Choice VideoQA from Feature and Sample Perspectives (2022.emnlp-main)

Challenge:	Procedural Multimodal Documents organize textual instructions and corresponding images step by step.
Approach:	They propose a novel temporal-modal entity Graph for comprehending PMDs . they propose encoding and reasoning modules to capture textual and visual entities .
Outcome:	The proposed model can capture textual and visual entities and trace their temporal-modal evolution.

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Shaoning Xiao, Long Chen, Kaifeng Gao, Zhao Wang, Yi Yang, Zhimeng Zhang, Jun Xiao

Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs with Refined Values (2025.emnlp-main)

Challenge:	Existing approaches to VideoQA focus on utilizing frame- or object-level visual representations, but they neglect visual-language interactions.
Approach:	They propose to break down video into trajectories and first leverage trajectory feature in VideoQA to enhance alignment between two modalities.
Outcome:	The proposed method outperforms all the state-of-the-art models on the NExT-QA benchmark.

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Hongbo Zhang, Han Cui, Guangsheng Bao, Linyi Yang, Jun Wang, Yue Zhang

Med-SRAF: A Multi-Agent Framework for Medical Reasoning via Semantic Routing and Agentic Fusion (2026.findings-acl)

Challenge:	Existing offline preference optimization methods rely on preference labels to optimize large language models.
Approach:	They propose an offline method for enhancing large language models in reasoning tasks that utilizes value signals at individual reasoning steps.
Outcome:	The proposed framework outperforms offline preference optimization techniques by 4% to 6% on math reasoning, commonsense reasoning, and coding tasks.

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Xiao Li, Zhuo Chen, Jun Xia, Hongxin Xiang, Chao Wang, Wenjie Du, Yang Wang

Information Retrieval

Challenge:	Existing RAG methods suffer from a two-part problem: semantic drift and concatenation fallacy . et al.: rapid development of Large Language Models has led to a paradigm shift in artificial intelligence .
Approach:	They propose a multi-agent retrieval augmentation framework guided by medical domain knowledge to address these challenges.
Outcome:	The proposed framework outperforms existing general RAG baselines on five widely used medical benchmarks.

Harmonizing the Past, Present, and Future: A Null-Space Constrained Region-Specific Method for Continual Learning in LLMs (2026.acl-long)

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Jinhui Chen, Shizhu He, Xingchang Yang, Huanxuan Liao, Yequan Wang, Xiangwen Liao, Wenhao Teng, Kang Liu, Jun Zhao

TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement (2025.findings-naacl)

Challenge:	Existing continual learning paradigms prioritize instant performance through dense updates, leading to catastrophic forgetting and rapid exhaustion of model capacity.
Approach:	They propose a method that preserves previously acquired knowledge and acquires new task-specific skills while preserving sufficient parameter capacity for subsequent adaptation.
Outcome:	The proposed method is based on the brain's functional partitioning and can be used to map tasks between specialized and generalist neurons.

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Zhaopeng Feng, Yan Zhang, Hao Li, Bei Wu, Jiayu Liao, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, Zuozhu Liu

Machine Translation

Challenge:	Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). human evaluations reveal that LLM-generated translations still contain various errors.
Approach:	They propose a LLM-based self-refinement framework that feeds error information back into LLMs to facilitate self-finement, leading to enhanced translation quality.
Outcome:	The proposed framework outperforms internal refinement and feedback methods while ensuring a robust translation quality baseline.

Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (2022.coling-1)

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Xiusheng Huang, Hang Yang, Yubo Chen, Jun Zhao, Kang Liu, Weijian Sun, Zuyu Zhao

Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts (2026.acl-long)

Challenge:	Existing document-level relation extraction methods are sparse in relational entity pairs and the representation of entity pairs is insufficient.
Approach:	They propose a Pair-Aware and Entity-Enhanced(PAEE) model to solve two challenges . they propose predicting potential relational entity pairs and assembling directional entity pairs .
Outcome:	The proposed model can obtain state-of-the-art performance on four benchmark datasets . it can predict potential relational entity pairs and assemble directional entity pairs .

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Xiaowei Yuan, Ziyang Huang, Zhao Yang, Yequan Wang, Jun Zhao, Kang Liu

Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning (2025.findings-emnlp)

Challenge:	Retrieval-Augmented Generation (RAG) has become a standard paradigm for grounding Large Language Models (LLMs) however, performance degrades substantially when faced with noisy, outdated, or conflicting retrieved information.
Approach:	They propose a framework that explicitly elicits the model’s parametric knowledge as prior information to guide reasoning on retrieved documents.
Outcome:	The proposed framework achieves robust performance across varying degrees of external inconsistency and noise.

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Kepu Zhang, Haoyue Yang, Xu Tang, Weijie Yu, Jun Xu

Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (D18-1)

Challenge:	Current legal large language models lack trichotomous reasoning capabilities due to the absence of an appropriate benchmark dataset.
Approach:	They propose a benchmark dataset for Legal Judgment Prediction with Innocent Verdicts that incorporates trichotomous dogmatics into zero-shot prompting and fine-tuning.
Outcome:	The proposed dataset extends three widely-used legal datasets through LLM-based augmentation and manual verification.

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Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia

Syntactic Text Processing

Challenge:	Existing approaches to ACE event detection treat multiple events in one sentence as independent ones and recognize them separately.
Approach:	They propose a hierarchical and bias tagging network framework to detect multiple events in one sentence collectively and a gated multi-level attention mechanism to automatically extract and fuse the sentence-level and document-level information.
Outcome:	The proposed framework outperforms state-of-the-art methods on a 2005 ACE dataset.

WR-One2Set: Towards Well-Calibrated Keyphrase Generation (2022.emnlp-main)

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Binbin Xie, Xiangpeng Wei, Baosong Yang, Huan Lin, Jun Xie, Xiaoli Wang, Min Zhang, Jinsong Su

Text Generation

Challenge:	Experimental results show that keyphrase generation has serious calibration errors . ONE2SET generates short phrases summarizing an input document .
Approach:	They propose a paradigm for keyphrase generation that generates short phrases summarizing an input document.
Outcome:	The proposed model over-estimates tokens and makes it well-calibrated on common datasets.

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)

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Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, Wei Lin

Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning (2026.findings-acl)

Challenge:	Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems.
Approach:	They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Outcome:	The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist.

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Xudong Li, Yuhang Tian, Dandan Song, Zhijing Wu, Shuhao Zhang, Jun Yang, Yongyu Huo, Changzhi Zhou, Xinyu Zhang, Chenhao Li, Huipeng Ma, Luan Zhang, Yan Xu, Qian Liu

Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text Classifications (2022.coling-1)

Challenge:	Existing knowledge editing methods rely on unidirectional, feed-forward pipelines . a minor retrieval error or logical mismatch at an early hop can become a silent failure .
Approach:	They propose a framework for closed-loop post-edit reasoning that uses a Critic agent to verify coherence and step-wise correctness.
Outcome:	Experiments on MQuAKE-2002 and MQuADE-hard show that CARE effectively mitigates error propagation . a minor retrieval error or logical mismatch at an early hop can become a silent failure .

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Heng-yang Lu, Chenyou Fan, Jun Yang, Cong Hu, Wei Fang, Xiao-jun Wu

Robustness in NLP

Challenge:	BackDoor Attack (BDA) study aims to train a poisoned model with clean data and some trigger-embedded instances to perform normally on normal inputs.
Approach:	They propose to train a poisoned model with clean and poisonest inputs . they propose to use triggers to predict those poisonets as target labels .
Outcome:	The proposed model can predict P2P dynamically without human intervention.

A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)

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Jian Zhang, Changlin Yang, Haiping Zhu, Qika Lin, Fangzhi Xu, Jun Liu

Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)

Challenge:	Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document.
Approach:	They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts.
Outcome:	The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets.

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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv

Text Generation

Challenge:	Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build.
Approach:	They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence.
Outcome:	The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence.

Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning (2024.findings-emnlp)

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Tianxiang Hu, Pei Zhang, Baosong Yang, Jun Xie, Derek Wong, Rui Wang

A Diffusion Model for Event Skeleton Generation (2023.findings-acl)

Challenge:	Achieving consistent high-quality machine translation across diverse domains remains a challenge due to limited and imbalanced parallel training data available in various domains.
Approach:	They propose a domain Chain of Thought technique that uses the multi-domain intelligence of LLMs to improve translation performance.
Outcome:	The proposed method achieves significant improvements in translation accuracy and domain robustness over traditional fine-tuning on a small dataset of four domains.

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Fangqi Zhu, Lin Zhang, Jun Gao, Bing Qin, Ruifeng Xu, Haiqin Yang

Structured Data in NLP

Challenge:	Existing methods for event schema generation are noise-sensitive and error-accumulating, e.g., inability to correct errors while generating schema.
Approach:	They propose a novel diffusion event graph model that embeds and roundes event graphs into learnable latent representations and a denoising process to maintain the model's robustness.
Outcome:	The proposed model achieves better results than existing state-of-the-art models on three IED bombing datasets.

DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data (P18-4)

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Hang Yang, Yubo Chen, Kang Liu, Yang Xiao, Jun Zhao

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

Challenge:	Existing methods to extract events from documents are limited due to the high cost of labeling . Experimental results demonstrate the effectiveness of a document-level Chinese financial event extraction system.
Approach:	They propose a document-level Chinese financial event extraction framework which detects event mentions and extracts events from financial news.
Outcome:	The proposed system detects event mentions and extracts events from financial news . it can generate large scale labeled data and extract events from entire document .

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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)

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Jiali Zeng, Jinsong Su, Huating Wen, Yang Liu, Jun Xie, Yongjing Yin, Jianqiang Zhao

Text Generation

Challenge:	Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model.
Approach:	They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains.
Outcome:	The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks.

Dual-Reasoner: Bridging Interleaved Atomicity and Streaming Latency via Thinking-while-Talking (2026.findings-acl)

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Yangzhuo Li, Shengpeng Ji, Yifu Chen, Tianle Liang, Haoyu Yang, null Junboli, Jun Fang, Lin Li, Qingyang Hong

Multi-Granularity Contrasting for Cross-Lingual Pre-Training (2021.findings-acl)

Challenge:	Existing methods to integrate Chain-of-Thought into spoken dialogue models incur prohibitive latency.
Approach:	They propose a Streaming Masking Mechanism to ensure uninterrupted audio streaming . they use a quadruple-constraint system to reconstruct logical atomicity .
Outcome:	Experimental results show that Dual-Reasoner improves speech generation performance with low latency.

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Shicheng Li, Pengcheng Yang, Fuli Luo, Jun Xie

Internalizing Multi-Agent Reasoning for Accurate and Efficient LLM-based Recommendation (2026.findings-acl)

Challenge:	Existing approaches to pre-training focus on embedding alignment, but they neglect the modeling of bidirectional contexts.
Approach:	They propose a framework to learn languageuniversal representations using multi-granularity contrasting framework . they encode semantic equivalents from different languages into similar representations .
Outcome:	The proposed framework can achieve significant performance gains in machine translation and cross-lingual language understanding.

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Yang Wu, Haoze Wang, Qian Li, Jun Zhang, Huan Yu, Jie Jiang

MSIA: Adaptive Medical Multimodal Multi-turn Semantic Jailbreak (2026.findings-acl)

Challenge:	Large Language Models (LLMs) are reshaping recommender systems by leveraging extensive world knowledge and semantic reasoning to interpret user intent.
Approach:	They propose a single-agent Trajectory-Aligned Recommender to integrate reasoning capabilities into a model by a multi-agend teacher system.
Outcome:	The proposed model surpasses its teacher by 8.7% to 39.5% while eliminating iterative latency.

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Zhiheng Han, Yao Zhang, Jun Wang, Zhenglu Yang

Unsupervised Preference-Aware Language Identification (2022.findings-acl)

Challenge:	Medical large language models exhibit high domain specificity and condensed semantics, making them vulnerable to diagnostic errors in real-world clinical settings.
Approach:	They propose a framework for modeling and inducing multi-turn medical semantic jailbreaks in clinical dialogues.
Outcome:	Experiments on chest X-ray-based multimodal medical dialogues show that MSIA outperforms existing jailbreak methods with an average success rate of 76.67%.

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Xingzhang Ren, Baosong Yang, Dayiheng Liu, Haibo Zhang, Xiaoyu Lv, Liang Yao, Jun Xie

Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields (2020.findings-emnlp)

Challenge:	Existing studies do not consider inter-personal variations due to the lack of user annotated training data.
Approach:	They propose to use user preferences to identify ambiguous texts in multilingual applications without user annotated training data to build a preference-aware LID model.
Outcome:	The proposed model significantly outperforms existing LID systems on handling ambiguous texts.

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Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Ji-Rong Wen, Nianwen Xue

OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model (2025.emnlp-demos)

Challenge:	Existing approaches to recover dropped pronouns ignore the dependencies between pronounes in neighboring utterances.
Approach:	They propose a framework that combines Transformer network and General Conditional Random Fields to model the dependencies between pronouns in neighboring utterances.
Outcome:	The proposed framework outperforms state-of-the-art models on three Chinese conversation datasets showing that it captures the dependencies between pronouns in neighboring utterances.

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Chen Wang, Tianyu Peng, Wen Yang, YiNan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang

UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)

Challenge:	Empathetic speech models are increasingly closed off, leaving details about the architecture, data and development opaque to researchers.
Approach:	They propose an open-source empathetic speech-to-text model with a streaming interleaved decoding architecture and a data pipeline to enable end-to end training.
Outcome:	The proposed model is open-source and transparent, with no data or data required to build it.

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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou

Challenge:	Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems.
Approach:	They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets.
Outcome:	The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization.

Improving Zero-shot LLM Re-Ranker with Risk Minimization (2024.emnlp-main)

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Xiaowei Yuan, Zhao Yang, Yequan Wang, Jun Zhao, Kang Liu

ACROSS: An Alignment-based Framework for Low-Resource Many-to-One Cross-Lingual Summarization (2023.findings-acl)

Challenge:	Large Language Models (LLMs) are effective Query Likelihood Models, but their estimation is biased and the model's accuracy is poor.
Approach:	They propose a framework which leverages Bayesian decision theory to quantify and mitigate this bias.
Outcome:	The proposed framework improves re-ranking, especially in improving the Top-1 accuracy.

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Peiyao Li, Zhengkun Zhang, Jun Wang, Liang Li, Adam Jatowt, Zhenglu Yang

Exploiting Contextual Knowledge in LLMs through 𝒱-usable Information based Layer Enhancement (2025.acl-long)

Challenge:	Existing studies ignore data imbalance in multilingual settings and do not utilize monolingual data.
Approach:	They propose a cross-lingual summarization model that aligns cross-linguistic data with high-resource monolingual data via contrastive and consistency loss.
Outcome:	The proposed model outperforms baseline models and consistently dominates on 45 language pairs.

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Xiaowei Yuan, Zhao Yang, Ziyang Huang, Yequan Wang, Siqi Fan, Yiming Ju, Jun Zhao, Kang Liu

Challenge:	Existing approaches to enhance the context-faithfulness of Large Language Models (LLMs) ignore the fundamental mechanism of how contextual information is processed within LLMs’ internal states.
Approach:	They propose a method that enhances the utilization of contextual knowledge within LLMs’ internal representations by employing V-usable information analysis.
Outcome:	The proposed method improves context-faithfulness generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge.

Generating Questions, Answers, and Distractors for Videos: Exploring Semantic Uncertainty of Object Motions (2025.findings-acl)

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Wenjian Ding, Yao Zhang, Jun Wang, Adam Jatowt, Zhenglu Yang

Visual Data in NLP

Challenge:	Existing video QADs are generated using video captions, incurring significant costs and resulting in bias.
Approach:	They propose to use temporal motion to describe video objects to generate diverse QADs focusing on different objects and interactions.
Outcome:	The proposed approach improves consistency and diversity of generated QADs on the NExT-QA and Perception Test benchmarks.

Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems (2022.acl-long)

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Ling.Yu Zhu, Zhengkun Zhang, Jun Wang, Hongbin Wang, Haiying Wu, Zhenglu Yang

RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution (2025.emnlp-main)

Challenge:	Existing work on empathetic dialogues focused on the two-party scenario, but multi-party dialogues are pervasive in reality.
Approach:	They propose a multi-party empathetic dialogue generation task that uses a static-dynamic model to explore emotion and sensibility.
Outcome:	The proposed task is based on a model with static sensibility and dynamic emotion . it achieves state-of-the-art performance in multi-party empathetic dialogue learning .

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Jiahui Li, Lin Li, Tai-Wei Chang, Kun Kuang, Long Chen, Jun Zhou, Cheng Yang

KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)

Challenge:	Experimental results demonstrate the superiority of our approach to aligning large language models with human preferences.
Approach:	They propose a method that evaluates and assigns specific credit to each token using an off-the-shelf reward model.
Outcome:	The proposed method evaluates and assigns specific credit to each token using an off-the-shelf reward model.

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Jun Zhang, Yan Yang, Chencai Chen, Liang He, Zhou Yu

Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks (2021.emnlp-main)

Challenge:	Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity.
Approach:	They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process.
Outcome:	The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation.

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Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, Jun Zhao

DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone Agents (2026.findings-acl)

Challenge:	Existing offline DST models require a fixed dataset to train . Existing domain-lifelong learning methods are impractical in real-world applications .
Approach:	They propose a domain-lifelong learning method to continuously train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains.
Outcome:	The proposed method outperforms state-of-the-art lifelong learning methods by 4.25% and 8.27% on the MultiWOZ and the SGD benchmarks.

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Kai Shi, Jun Yang, Ni Yang, Binqiang Pan, Qingsong Xie, null Zhangchao, Zhenyu Yang, Tianhuang Su, Haonan Lu

M-TRACE: Detecting and Mitigating Time-Anchor Drift via Step-wise Conflict Checking in Temporal Reasoning (2026.findings-acl)

Challenge:	Mobile Phone Agents (MPAs) have attracted huge attention due to their practicability in a multitude of scenarios.
Approach:	They propose a data mixture optimization solution that extrapolates optimal data mixtures from a trainable network.
Outcome:	The proposed model outperforms existing methods on open-source benchmarks and on open source benchmarks.

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Danyu Huang, Jiayuan Jiang, Yao Zhang, Jun Wang, Huijia Li, Zhenglu Yang

Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification (D19-1)

Challenge:	Experimental results show that M-TRACE effectively reduces time-anchor drift . external knowledge may be inaccurate while internal knowledge can become outdated .
Approach:	They propose a multi-agent reasoning framework for temporal knowledge conflicts . they propose 'TimeConfQA' which guides conflict-aware final reasoning .
Outcome:	Experimental results show that M-TRACE reduces time-anchor drift and improves performance on complex temporal question answering tasks.

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Pengcheng Yang, Junyang Lin, Jingjing Xu, Jun Xie, Qi Su, Xu Sun

Sentiment Analysis

Challenge:	Existing methods for unsupervised sentiment modification lack specific information in text generated without parallel data . specificity-driven cascading approach can improve specificity of generated text and content preservation .
Approach:	They propose a specificity-driven cascading approach for unsupervised sentiment modification . the method performs target sentiment addition and content reconstruction independently .
Outcome:	The proposed method outperforms competitive systems by a large margin on Yelp and Amazon datasets.

Augmenting Reasoning Capabilities of LLMs with Graph Structures in Knowledge Base Question Answering (2024.findings-emnlp)

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Hao Wang, Jun Yang, Jing Xu, Ruanmin Cao, HaoYu Wang

Recovering dropped pronouns in Chinese conversations via modeling their referents (N19-1)

Challenge:	Recent work uses Large Language Models (LLMs) for semantic parsing to address Knowledge Base Question Answering tasks.
Approach:	They propose a framework that augments reasoning capabilities of LLMs with Graph Structures in Knowledge Base Question Answering to retrieve question-related graph structures.
Outcome:	The proposed framework outperforms existing methods on GrailQA and WebQSP under the few-shot setting.

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Jingxuan Yang, Jianzhuo Tong, Si Li, Sheng Gao, Jun Guo, Nianwen Xue

Well Begun is Half Done: Generator-agnostic Knowledge Pre-Selection for Knowledge-Grounded Dialogue (2023.emnlp-main)

Challenge:	Pronouns are often dropped in conversational genres as their referents can be easily understood from context.
Approach:	They propose an end-to-end neural network model to recover dropped pronouns in conversational data.
Outcome:	The proposed model improves on three different conversational genres.

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Lang Qin, Yao Zhang, Hongru Liang, Jun Wang, Zhenglu Yang

Reconstructing Event Regions for Event Extraction via Graph Attention Networks (2020.aacl-main)

Challenge:	Existing knowledge selection methods are costly to learn and difficult to interpret when errors arise in the generated responses.
Approach:	They propose a generator-agnostic knowledge selection method to select context-related knowledge among different knowledge structures and variable knowledge requirements.
Outcome:	The proposed method can select knowledge accurately in advance and reduce learning, adjustment, and interpretation burden of later models.

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Pei Chen, Hang Yang, Kang Liu, Ruihong Huang, Yubo Chen, Taifeng Wang, Jun Zhao

Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation (2023.acl-long)

Challenge:	Existing approaches for event extraction focus on sentence-level event extraction, but they lack a broader view of the document context.
Approach:	They build graphs with candidate event filler extractions enriched by sentential embeddings as nodes and use graph attention networks to identify event regions in a document and aggregate event information.
Outcome:	The proposed method performs well on two languages and shows that it is faster than previous methods.

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Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing Chen, Jun Xie

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.findings-emnlp)

Challenge:	Existing work focuses on generating sentences satisfying pre-specified attributes such as topic and sentiment, yet suffers from increases in storage and inference time.
Approach:	They propose a method that uses a pre-trained continuous vector to generate a fixed pre-trainable language model to satisfy a specified attribute.
Outcome:	The proposed model can achieve improvements on eleven attribute-specific generation tasks with 0.08% extra training parameters.

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Lei Geng, Xu Yan, Ziqiang Cao, Juntao Li, Wenjie Li, Sujian Li, Xinjie Zhou, Yang Yang, Jun Zhang

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment (2026.acl-long)

Challenge:	Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability.
Approach:	They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach.
Outcome:	The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios.

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Yuming Yang, Mingyoung Lai, Wanxu Zhao, Xiaoran Fan, Zhiheng Xi, Mingqi Wu, Chiyue Huang, Jun Zhao, Haijun Lv, Jian Tong, Yunhua Zhou, Yicheng Zou, Qipeng Guo, Tao Gui, Qi Zhang, Xuanjing Huang

JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features (C18-1)

Challenge:	Existing methods assess suitability primarily through student likelihood, favoring trajectories that align closely with the student model’s current behavior but overlooking more informative ones.
Approach:	They propose a Rank–Surprisal Ratio metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory.
Outcome:	The proposed metric captures both alignment and informativeness to assess the suitability of a reasoning trajectory.

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Hongru Liang, Haozheng Wang, Jun Wang, Shaodi You, Zhe Sun, Jin-Mao Wei, Zhenglu Yang

Representation Learning

Challenge:	Existing studies on learning social media content focus on single modal or bi-modal learning, but this approach is non-trivial and challenging because content is multi-modal and involves several types of data, including text, audio, and image.
Approach:	They propose to combine textual, acoustic, and visual information to learn social media content by fusing them jointly.
Outcome:	The proposed model outperforms the state-of-the-art approaches on real-world datasets by a large margin.

FrontCoder: Scaling Visual Fidelity in Front-End Code Generation (2026.findings-acl)

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Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu

MGR: Multi-generator Based Rationalization (2023.acl-long)

Challenge:	Existing work on front-end code generation fails to provide visual fidelity and rendering quality for front- end developers.
Approach:	They propose a three-stage pipeline to enhance front-end code generation capabilities in LLMs . they use synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning .
Outcome:	The proposed model achieves competitive performance with frontier models while maintaining generation efficiency.

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Wei Liu, Haozhao Wang, Jun Wang, Ruixuan Li, Xinyang Li, YuanKai Zhang, Yang Qiu

Challenge:	Existing approaches to explain NLP models have two key challenges: spurious correlation and degeneration.
Approach:	They propose a rationalization framework using a generator and a predictor to construct a self-explaining NLP model with spurious correlation and degeneration as key challenges.
Outcome:	The proposed method improves the F1 score by 20.9% compared to state-of-the-art methods.

Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)

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Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su

Machine Translation

Challenge:	Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains.
Approach:	They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore.
Outcome:	The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore.

Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint (2024.findings-acl)

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Xiaowei Yuan, Zhao Yang, Yequan Wang, Shengping Liu, Jun Zhao, Kang Liu

Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators (N18-2)

Challenge:	Existing decoding methods for large language models (LLMs) are specialized in resolving knowledge conflicts and could inadvertently deteriorate performance in absence of conflicts.
Approach:	They propose an adaptive decoding method to discern whether knowledge conflicts occur and resolve them by a contextual information-entropy constraint decoding technique.
Outcome:	The proposed method improves the model’s faithfulness to conflicting context and maintains high performance among non-conflicting contexts.

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Cen Chen, Yinfei Yang, Jun Zhou, Xiaolong Li, Forrest Sheng Bao

Challenge:	Recent studies on review helpfulness prediction require labeled samples for each domain/category of interest.
Approach:	They propose a convolutional neural network based model which leverages word-level and character-based representations to transfer knowledge between domains.
Outcome:	The proposed model outperforms the state-of-the-art on the Amazon product review dataset.

Alignment Rationale for Natural Language Inference (2021.acl-long)

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Zhongtao Jiang, Yuanzhe Zhang, Zhao Yang, Jun Zhao, Kang Liu

Explainability & Interpretability in NLP

Challenge:	Existing explanation methods pick prominent features, but alignments between words or phrases are more enlightening clues to explain the model.
Approach:	They propose a method to generate alignment rationale explanations for co-attention based models in NLI by feature selection.
Outcome:	The proposed method is more faithful and human-readable compared with existing methods.

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

Improving Autoformalization Using Direct Dependency Retrieval (2026.acl-long)

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

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Shaoqi Wang, Lu Yu, Siwei Lou, Feng Yan, Chunjie Yang, Qing Cui, Jun Zhou

Document-level Event Extraction via Parallel Prediction Networks (2021.acl-long)

Challenge:	Existing methods for hallucinate formal dependencies lack scalability and precision to leverage ever-growing public datasets.
Approach:	They propose a retrieval-augmented framework based on Direct Dependency Retrieval to generate formal dependencies from natural-language mathematical descriptions and verify their existence via an efficient Suffix Array Check (SAC).
Outcome:	The proposed framework outperforms state-of-the-art methods in retrieval precision and recall and can be used to validate formal representations in a public dataset.

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Hang Yang, Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang

MoDE-CoTD: Chain-of-Thought Distillation for Complex Reasoning Tasks with Mixture of Decoupled LoRA-Experts (2024.lrec-main)

Challenge:	Document-level event extraction (DEE) is indispensable when events are described throughout a document.
Approach:	They propose a document-level event extraction model that can extract structured events from a text in parallel.
Outcome:	The proposed model outperforms current state-of-the-art methods on a document-level event extraction task.

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Xiang Li, Shizhu He, Jiayu Wu, Zhao Yang, Yao Xu, Yang jun Jun, Haifeng Liu, Kang Liu, Jun Zhao

MM-AVS: A Full-Scale Dataset for Multi-modal Summarization (2021.naacl-main)

Challenge:	Current Chain-of-thought Distillation methods hinder CoT reasoning performance . student models are separately distilled from specific reasoning tasks . parameter update of student models severely harms CoT ability on unseen reasoning tasks.
Approach:	They propose a method which distills Chain-of-thought reasoning ability of large language models to much smaller student models.
Outcome:	The proposed method improves the reasoning ability of large language models on 14 datasets.

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Xiyan Fu, Jun Wang, Zhenglu Yang

MMNMT: Modularizing Multilingual Neural Machine Translation with Flexibly Assembled MoE and Dense Blocks (2023.emnlp-main)

Challenge:	Multimodal summarization materials lacking a holistic organization by integrating resources from various modalities.
Approach:	They propose a multimodal article and video summarization dataset that integrates resources from different modalities.
Outcome:	The proposed dataset validates the important assistance role of external information for multimodal summarization.

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Shangjie Li, Xiangpeng Wei, Shaolin Zhu, Jun Xie, Baosong Yang, Deyi Xiong

Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)

Challenge:	Mixture-of-Experts (MoE) based sparse architectures are prone to overfitting on low-resource language translation.
Approach:	They propose a modularized MNMT framework that flexibly assembles dense and MoE-based sparse modules to achieve the best of both worlds.
Outcome:	The proposed framework outperforms existing models on low-resource language translation and zero-shot translation on benchmark datasets.

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Huan Lin, Baosong Yang, Liang Yao, Dayiheng Liu, Haibo Zhang, Jun Xie, Min Zhang, Jinsong Su

Dynamic Voting for Efficient Reasoning in Large Language Models (2023.findings-emnlp)

Challenge:	Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity .
Approach:	They propose a multi-candidate optimization framework for diverse NMT to deal with this defect.
Outcome:	The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality.

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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Xingzhang Ren, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

Challenge:	Multi-path voting methods generate multiple reasoning paths for each problem, causing factual errors and illusion generation.
Approach:	They propose a multi-path voting technique that effectively reduces the number of reasoning paths during multi-path voting while preserving accuracies.
Outcome:	The proposed method outperforms Self-consistency using 24.7% of the number of paths on the LetterConcat task.

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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning (2024.findings-acl)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Yang Zhao, Li Du, Xiao Ding, Kai Xiong, Zhouhao Sun, Shi Jun, Ting Liu, Bing Qin

Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution (2026.findings-acl)

Challenge:	Existing studies have suggested that the composition of the pretraining corpus exerts a significant impact upon the performance of LLMs.
Approach:	They analyze the impact of 48 datasets from 5 major categories of pretraining data of Large Language Models and measure their impacts on LLMs using benchmarks about nine major categories.
Outcome:	The proposed analysis provides insights into the organization of data to support more efficient pretraining of Large Language Models.

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Yuhua Zhou, Shichao Weng, Changhai Zhou, Yuhan Wu, Qian Qiao, Jun Gao, Fei Yang, Aimin Pan

Our Neural Machine Translation Systems for WAT 2019 (D19-52)

Challenge:	Existing dynamic schemes such as early-exit and layer-drop reduce FLOPs but break batch processing or introduce KV-cache inconsistency.
Approach:	They propose a dynamic low-rank substitution framework that employs a lightweight decision module at each layer to dynamically determine the execution branch for different tokens.
Outcome:	The proposed model reduces computation by approximately 40% compared to the original dense model while outperforming existing baseline methods.

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Wei Yang, Jun Ogata

Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? (2022.emnlp-main)

Challenge:	In the last five years, statistical machine translation is gradually fading out in favor of neural machine translation.
Approach:	They describe a novel Neural Machine Translation (NMT) system for the WAT 2019 translation tasks they focus on.
Outcome:	The proposed system improves translation accuracy while replacing absolute position representations with relative positions.

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Pei Zhang, Baosong Yang, Hao-Ran Wei, Dayiheng Liu, Kai Fan, Luo Si, Jun Xie

Machine Translation

Challenge:	Neural machine translation models are often criticized for failures that happen without competency awareness.
Approach:	They propose a method that extends conventional NMT with a self-estimator to translate a source sentence and estimate its competency.
Outcome:	The proposed method performs on translation tasks intact and on quality estimation tasks better than existing methods.

SafeConf: A Confidence-Calibrated Safety Self-Evaluation Method for Large Language Models (2025.findings-emnlp)

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Bo Zhang, Cong Gao, Linkang Yang, Bingxu Han, Minghao Hu, Zhunchen Luo, Guotong Geng, Xiaoying Bai, Jun Zhang, Wen Yao, Zhong Wang

What Have We Achieved on Text Summarization? (2020.emnlp-main)

Challenge:	Large language models (LLMs) have many advantages but they also pose significant safety risks.
Approach:	They propose a method to enhance the safety self-evaluation capability of LLMs . they perform semantic mutations on the original safety evaluation questions .
Outcome:	The proposed method improves safety self-evaluation accuracy by 5.86% and 7.79% over baseline methods on Chinese and English datasets.

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Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, Yue Zhang

Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing (2026.acl-long)

Challenge:	Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals.
Approach:	They analyze 8 major sources of errors on 10 representative summarization models manually.
Outcome:	Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models.

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Lingkun Long, Yushi Huang, Shihao Bai, Ruihao Gong, Jun Zhang, Ao Zhou, Jianlei Yang

Green & Sustainable NLP

Challenge:	Existing methods for estimating attention importance for tokens are ineffective . dLLMs require bidirectional attention, which limits inference efficiency .
Approach:	They propose a training-free attention sparsification framework for efficient long-context inference . they propose 'sink-aware pruning strategy' to accurately estimate and remove redundant computation .
Outcome:	The proposed approach offers 29 lossless speedup under 32K context length.

Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech (2022.acl-long)

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Yang Li, Cheng Yu, Guangzhi Sun, Hua Jiang, Fanglei Sun, Weiqin Zu, Ying Wen, Yang Yang, Jun Wang

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Experimental results show that the proposed model improves naturalness and prosody diversity with clear margins.
Approach:	They propose a cross-utterance conditional VAE to estimate posterior probability distribution of latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features from past and future sentences.
Outcome:	The proposed model improves naturalness and prosody diversity with clear margins.

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving (2026.acl-long)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Xinyu Zhang, Yuchen Wan, Boxuan Zhang, Zesheng Yang, Lingling Zhang, Bifan Wei, Jun Liu

Text Clustering

Challenge:	Large Language Models (LLMs) struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, hindering effective solution generation.
Approach:	They propose to use memory to leverage historical solutions in a training-free manner to enhance performance by leveraging generalizable guidance knowledge.
Outcome:	The proposed agent achieves an average performance improvement of 11%-21% over previous agents.

Meta Distant Transfer Learning for Pre-trained Language Models (2021.emnlp-main)

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Chengyu Wang, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang, Yin Zhang

M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model (2025.emnlp-main)

Challenge:	Notable PLMs are available for text classification tasks, but performance of PLM on downstream tasks may be limited by the availability of training set.
Approach:	They propose a meta-learning framework to learn the transferable knowledge across tasks using PLMs.
Outcome:	The proposed framework outperforms baselines on seven datasets and is task-agnostic and unbiased.

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Yang Zhou, Pengfei Cao, Yubo Chen, Qingbin Liu, Dianbo Sui, Xi Chen, Kang Liu, Jun Zhao

Dynamic Evil Score-Guided Decoding: An Efficient Decoding Framework For Red-Team Model (2025.findings-acl)

Challenge:	Existing knowledge editing methods for MLLMs lack multi-granularity knowledge . existing knowledge editing approaches lack multimodality knowledge and generalize to multimodal data.
Approach:	They propose a multimodal knowledge editing method which integrates key knowledge layers within MLLMs and collaboratively edits them.
Outcome:	The proposed method improves visual generality performance on knowledge data of different granularities.

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Cong Gao, Bo Zhang, Linkang Yang, Minghao Hu, Zhunchen Luo, Xiaoying Bai, Guotong Geng, Jun Zhang, Yunhua Xue

Making the Best Use of Review Summary for Sentiment Analysis (2020.coling-main)

Challenge:	Existing red-teaming methods require expensive fine-tuning, especially for large LLMs.
Approach:	They propose a red-teaming method that uses an ‘evil score’ to evaluate the potential of tokens to contribute to harmful outputs during decoding.
Outcome:	The proposed method achieves an ASR of 92.83% on the Llama-3.2-3B-Instruct model, compared to 83.48% with adversarial fine-tuning while using less computational resources.

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Sen Yang, Leyang Cui, Jun Xie, Yue Zhang

Sentiment Analysis

Challenge:	Existing methods for sentiment analysis of user reviews are limited to a few examples.
Approach:	They propose a hierarchically-refined attention model that exploits the sentimental distribution of a review and its corresponding summary.
Outcome:	The proposed model can make better use of user-written summaries for review sentiment analysis and is more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.

SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)

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Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang

Logic Traps in Evaluating Attribution Scores (2022.acl-long)

Challenge:	Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences.
Approach:	They propose a framework to mitigate attention dispersion by strategically inserting separator tokens into the model to recalibrat attention to local segments while preserving global context.
Outcome:	The proposed framework improves accuracy and reduces inference token consumption by 16.4% on 9 widely-adopted LLMs.

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Yiming Ju, Yuanzhe Zhang, Zhao Yang, Zhongtao Jiang, Kang Liu, Jun Zhao

HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns (2026.acl-long)

Challenge:	Modern deep learning models are notoriously opaque, which has motivated the development of methods for interpreting how deep models predict.
Approach:	They propose to review existing methods for evaluating attribution scores and summarize the logic traps in these methods.
Outcome:	The proposed methods show that they do not contain logic traps and that they are not reliable.

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Xintao Wang, Jian Yang, Weiyuan Li, Rui Xie, Jen-tse Huang, Jun Gao, Shuai Huang, Yueping Kang, Yuanli Guo, Hongwei Feng, Yanghua Xiao

Linguistics & Cognitive NLP

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs).
Approach:	They propose a framework that treats psychological patterns as interacting causal forces and synthesizes 113 scenarios where 2-5 patterns reinforce, conflict, or modulate each other.
Outcome:	The proposed framework outperforms Qwen3-32B on multi-pattern dynamics despite 4 fewer parameters.

Can We Learn Question, Answer, and Distractors All from an Image? A New Task for Multiple-choice Visual Question Answering (2024.lrec-main)

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Wenjian Ding, Yao Zhang, Jun Wang, Adam Jatowt, Zhenglu Yang

HTCCN: Temporal Causal Convolutional Networks with Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs (2024.naacl-long)

Challenge:	Existing studies focus on generating QADs from image and question, but a novel task is needed to generate meaningful questions, correct answers, and challenging distractors.
Approach:	They propose a task to generate QADs from images and encode images together . they use contrastive learning to ensure consistency of QAD generated and tested .
Outcome:	Empirical evaluations on the benchmark dataset validate the performance of the proposed task.

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Tingxuan Chen, Jun Long, Liu Yang, Zidong Wang, Yongheng Wang, Xiongnan Jin

Knowledge Representation

Challenge:	Temporal knowledge graphs (TKGs) are powerful tools for storing and modeling dynamic facts.
Approach:	They propose a Hawkes process-based temporal causal convolutional network for temporal reasoning under extrapolation settings.
Outcome:	The proposed network is based on Hawkes process-based temporal causal convolutional network and captures the temporal evolution of facts.

OntoGuard: Enforcing Action Admissibility for LLM Agents in Complex Interactive Environments (2026.findings-acl)

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Pengxiang Liu, Tao Ren, Wei Xiong, Tingrui Yang, Junjie Wang, Jun HU

Challenge:	Existing approaches to large language models (LLMs) are limited by their ability to enforce environmental and behavioral admissibility.
Approach:	They propose an ontological framework to guard LLM agents by enforcing environmental and behavioral admissibility.
Outcome:	Experiments on ScienceWorld and VirtualHome show that OntoGuard can enforce environmental and behavioral admissibility while preventing invalid actions.

CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)

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Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang

MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)

Challenge:	Existing knowledge base question answering methods struggle with complex queries.
Approach:	They propose a framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling it to learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation.
Outcome:	The proposed framework achieves state-of-the-art on two benchmark KBQA datasets, WebQSP and CWQ.

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Shuhang Chen, Hangjie Yuan, Yunqiu Xu, Pengwei Liu, Tao Feng, Jun Cen, Zeying Huang, Yi Yang