Papers by Chong Li

Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM (2024.findings-acl)

Challenge:	Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization.
Approach:	They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation.
Outcome:	The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts.

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Yang Chen, Chong Yang, Tu Hu, Xinhao Chen, Man Lan, Li Cai, Xinlin Zhuang, Xuan Lin, Xin Lu, Aimin Zhou

MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction (2022.findings-acl)

Challenge:	Existing research has demonstrated that the ability of large language models (LLMs) to generate humorous sentences is limited to producing 25 unique jokes.
Approach:	They propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences by a Chinese Pun dataset.
Outcome:	The proposed method significantly outperforms baseline models on Chinese and English benchmark datasets.

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Linhan Zhang, Qian Chen, Wen Wang, Chong Deng, ShiLiang Zhang, Bing Li, Wei Wang, Xin Cao

Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering (2026.acl-long)

Challenge:	Keyphrase extraction (KPE) extracts phrases in a document that provide a concise summary of the core content.
Approach:	They propose an unsupervised keyphrase extraction method that ranks candidates by similarity between embeddings of source document and masked document.
Outcome:	The proposed method outperforms state-of-the-art methods on six benchmarks . it achieves average 3.53 improvement over the existing method .

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Li Zheng, Xin Zhang, Shuyi He, Fei Li, Chong Teng, Jiang-Ming Yang, Donghong Ji, Zhuang Li

TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models (2024.findings-emnlp)

Challenge:	Existing studies on neurons focus on emotion and rhetoric, neglecting their intrinsic connections.
Approach:	They propose a framework for fine-grained steering of emotion and rhetoric in large language models . they propose 'neuro-based' masking method that integrates multi-dimensional screening .
Outcome:	The proposed method achieves directed induction of non-target sentences and enhancement of emotion tasks via rhetoric neurons.

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Chen Zhang, Chengguang Tang, Dading Chong, Ke Shi, Guohua Tang, Feng Jiang, Haizhou Li

MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)

Challenge:	Mainstream approaches to aligning large language models heavily rely on human preference data.
Approach:	They propose a framework that fine-tunes a policy model using pairwise feedback data automatically mined from its outputs.
Outcome:	The proposed framework outperforms the base model with an average win rate of 69.7% across seven conversational or instruction-following datasets.

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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang

Text Generation

Challenge:	Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain.
Approach:	They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process.
Outcome:	The proposed dataset evaluates the performance of unsupervised methods and advanced large language models.

Zero-Shot Conversational Stance Detection: Dataset and Approaches (2025.findings-acl)

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Yuzhe Ding, Kang He, Bobo Li, Li Zheng, Haijun He, Fei Li, Chong Teng, Donghong Ji

Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge (2025.acl-long)

Challenge:	Existing stance detection datasets are limited to a limited set of specific targets . current models are limited in their ability to detect large numbers of unseen targets based on a large number of unidentified targets.
Approach:	They propose a speaker interaction and target-aware prototypical contrastive learning model that can detect public opinion towards specific targets using social media data.
Outcome:	The proposed model achieves state-of-the-art in zero-shot conversational stance detection with only an F1-macro score of 43.81%.

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Li Zheng, Sihang Wang, Hao Fei, Zuquan Peng, Fei Li, Jianming Fu, Chong Teng, Donghong Ji

X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions (2024.findings-acl)

Challenge:	Existing methods for hyperbole and metaphor detection focus on superficial text features, ignoring the associations of hyperbola and metaphor . Existing frameworks focus on identifying superficial text, focusing on superficial features .
Approach:	They propose an emotion-guided hyperbole and metaphor detection framework based on bidirectional dynamic interaction.
Outcome:	The proposed framework outperforms baseline methods on four datasets.

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Chong Li, Wen Yang, Jiajun Zhang, Jinliang Lu, Shaonan Wang, Chengqing Zong

Multilinguality

Challenge:	Large language models respond well in high-resource languages but struggle in low-resourced languages.
Approach:	They propose a method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages.
Outcome:	The proposed method builds a large-scale cross-lingual instruction tuning dataset on 10 languages.

DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning (2025.findings-acl)

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Kang He, Yuzhe Ding, Haining Wang, Fei Li, Chong Teng, Donghong Ji

MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)

Challenge:	Existing multimodal sentence representation learning methods focus on aligning images and text at a coarse level, resulting in cross-modal misalignment bias and intra-modal semantic divergence.
Approach:	They propose a dual-level alignment learning framework for multimodal sentence representation learning that promotes cross-modal and intra-modal alignment.
Outcome:	The proposed framework outperforms state-of-the-art methods on semantic textual similarity and transfer tasks on semantic similarity, ranking distillation and global intra-modal alignment learning.

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Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao

Natural Language Interfaces

Challenge:	Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions.
Approach:	They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models.
Outcome:	The proposed benchmarks show that the models perform better in open-ended conversations.

Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm (2024.findings-acl)

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Qiang Gao, Zixiang Meng, Bobo Li, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

Information Extraction & Text Mining

Challenge:	Document-level event extraction aims to extract structured information from unstructured text.
Approach:	They propose a cross-document event extraction pipeline that integrates event information from multiple documents and provides a comprehensive perspective on events.
Outcome:	The proposed pipeline achieves about 72% F1 in end-to-end cross-document event extraction, setting up a benchmark for future research.

PEMV: Improving Spatial Distribution for Emotion Recognition in Conversations Using Proximal Emotion Mean Vectors (2025.findings-naacl)

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Chen Lin, Fei Li, Donghong Ji, Chong Teng

Information Retrieval

Challenge:	Existing research focuses on the analysis of contextual structure in dialogue and the interactions between different emotions.
Approach:	They propose a method that generates Proximal Emotion Mean Vectors (PEMVs) based on emotion feature queues to optimize the spatial representation of text features.
Outcome:	The proposed method achieves state-of-the-art performance on three widely used benchmark datasets.

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024.acl-long)

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Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang

PRIM: Towards Practical In-Image Multilingual Machine Translation (2025.emnlp-main)

Challenge:	Mixture-of-Experts (MoE) architectures face challenges in ensuring expert specialization . despite the promising performance, scaling language models to an extremely large scale is associated with exceedingly high computational costs.
Approach:	They propose an architecture that allows for ultimate expert specialization by segmenting experts into mN ones and activating mK from them.
Outcome:	The proposed architecture achieves comparable performance with GShard with 2B parameters and computation.

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Yanzhi Tian, Zeming Liu, Zhengyang Liu, Chong Feng, Xin Li, Heyan Huang, Yuhang Guo

Multilinguality

Challenge:	Current research on in-image machine translation focuses on synthetic data with simple background, single font, fixed text position, and bilingual translation.
Approach:	They propose an end-to-end model to handle the challenge of practical conditions in PRIM . they annotate a real-world one-line text image with complex background, fonts, diverse text positions .
Outcome:	The proposed model improves translation quality and visual effect compared to other models.

DocMEdit: Towards Document-Level Model Editing (2025.findings-acl)

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Li Zeng, Zeming Liu, Chong Feng, Heyan Huang, Yuhang Guo

Think Better, Not Longer: Token-Level Marginal Utility for Efficient Reasoning in Large Reasoning Models (2026.acl-long)

Challenge:	Existing models only output short phrases or sentences, raising doubts about their practical usability.
Approach:	They propose a dataset focused on document-level model editing that aims to correct errors and outdated knowledge in Large language models (LLMs) they propose to use document-based model editing to improve model capabilities in real-world scenarios.
Outcome:	The proposed model editing task improves model capabilities in real-world scenarios and reduces the cost of retraining.

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Jiawei Li, Yang Gao, Huashan Sun, Chong Feng

Reasoning

Challenge:	Large reasoning models (LRMs) generate explicit Chain-of-Thought rationales, but often suffer from "overthinking".
Approach:	They propose a unified training framework to synthesize concise reasoning chains by identifying tokens that reduce the model’s likelihood of the correct answer.
Outcome:	Experiments on deepSeek-R1-Distill-Qwen backbones show that MUTO yields better efficiency-accuracy Pareto frontier.

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)

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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu

TokAlign: Efficient Vocabulary Adaptation via Token Alignment (2025.acl-long)

Challenge:	RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts.
Approach:	They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Outcome:	The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness.

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Chong Li, Jiajun Zhang, Chengqing Zong

Green & Sustainable NLP

Challenge:	Tokenization is a foundational step for Large Language Models (LLMs) but low compression rate of vanilla tokenizers decelerates training and inference process.
Approach:	They propose a method to replace the vocabulary of Large Language Models (LLMs) by learning a one-to-one mapping matrix for token IDs.
Outcome:	The proposed method significantly improves multilingual text compression rates and vocabulary initialization for Large Language Models.

Discovering Semantic Subdimensions through Disentangled Conceptual Representations (2025.findings-emnlp)

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Yunhao Zhang, Shaonan Wang, Nan Lin, Xinyi Dong, Chong Li, Chengqing Zong

Explainability & Interpretability in NLP

Challenge:	Existing approaches focus on predefined dimensions that overlook finer conceptual distinctions . a new framework is proposed to investigate the subdimensions underlying coarse-grained semantic dimensions .
Approach:	They propose a framework that decomposes word embeddings into multiple sub-embeddings . they propose to map these subdimensions to brain activation to assess their plausibility .
Outcome:	The proposed framework decomposes word embeddings from large language models into sub-embeddings, each encoding specific semantic information.

From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs (2025.findings-acl)

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Shenshen Li, Wenxin Meng, Lei Wang, Hao Yang, Chong Peng, Peng Yan, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xing Xu

Low-Resource NLP

Challenge:	Existing methods that adapt LVLMs to egocentric tasks overlook critical agent-environment interactions, limiting their ability to perform egoic reasoning.
Approach:	They propose a zero-shot paradigm to enhance egocentric reasoning by simulating human causal reasoning by formalizing ego-centric reasoning using a structural causal model.
Outcome:	The proposed method improves egocentric reasoning abilities on six tasks.

LitVISTA: A Benchmark for Narrative Orchestration in Literary Text (2026.acl-long)

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Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li

Challenge:	Existing large language models focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives.
Approach:	They propose a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space.
Outcome:	The proposed framework unifies human and model perspectives while jointly characterizing narrative function and structure in a common space.

Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model (2025.findings-acl)

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Chong Li, Yingzhuo Deng, Jiajun Zhang, Chengqing Zong

RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought (2025.acl-long)

Challenge:	Experimental results show that language group specialization on experts improves multilingual performance.
Approach:	They propose to dynamically group and scale up parameters of multilingual Large Language Models while boosting positive transfer among similar languages.
Outcome:	The proposed method reduces negative transfer between languages and boosts performance on 18 to 128 languages.

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Yi Lu, Jiawang Cao, Yongliang Wu, Bozheng Li, Licheng Tang, Yangguang Ji, Chong Wu, Jay Wu, Wenbo Zhu

Responsible & Trustworthy NLP

Challenge:	Recent advances in multi-modal learning have enhanced MLLMs' ability to reason about visual content.
Approach:	They propose a framework that unifies multi-step multimodal reasoning with grounded visual understanding.
Outcome:	The proposed framework surpasses state-of-the-art methods by +6.5 gIoU and +9.2 cIou on ReasonSeg and achieves 49.7 mAP on SegInW under zero-shot settings.

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning (2026.acl-long)

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Chuanrui Hu, Xingze Gao, Zuyi Zhou, Dannong Xu, Yi Bai, Xintong Li, Hui Zhang, Tong Li, Chong Zhang, Lidong Bing, Yafeng Deng

Reasoning

Challenge:	Existing memory systems for LLMs store isolated records and retrieve fragments . Existing systems store isolated data and fragments, limiting their ability to consolidate evolving experience and resolve conflicts.
Approach:	They propose an engram-inspired memory operating system that implements an 'engram'-inspired lifecycle for computational memory.
Outcome:	Experiments on LoCoMo, LongMemEval, and PersonaMeM-v2 show that EverMemeOS outperforms state-of-the-art methods on memory-augmented reasoning tasks.

Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)

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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang

Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (2022.acl-short)

Challenge:	Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications.
Approach:	They propose a reliable strategy for domains to choose more robust LLMs for real-world applications.
Outcome:	The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications.

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Runxin Sun, Shizhu He, Chong Zhu, Yaohan He, Jinlong Li, Jun Zhao, Kang Liu

Programming Languages in NLP

Challenge:	Text-to-SQL parsing aims to parse natural language questions into SQL queries . current attention-based approaches can only model alignments at the token level .
Approach:	They propose a method to leverage explicit lexico-logical alignments by identifying possible phrase-level alignments and injecting them as additional contexts into the parsing procedure.
Outcome:	The proposed approach improves performance by 3.4% on Squall.

Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models (2021.acl-short)

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Chong Li, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang

Robustness in NLP

Challenge:	Experimental results show that a sequence-to-sequence learning framework with neural networks can be effective for Chinese Spelling Correction (CSC)
Approach:	They propose a sequence-to-sequence learning framework with neural networks that generates more valuable training instances and adds task-specific examples to enhance the model.
Outcome:	The proposed method improves generalization and robustness of multiple CSC models across three datasets.

Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)

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Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, Wentao Zhang

Structured Data in NLP

Challenge:	Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination .
Approach:	They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality .
Outcome:	The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs.

Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction (2025.emnlp-main)

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Mengying Yuan, WenHao Wang, Zixuan Wang, Yujie Huang, Kangli Wei, Fei Li, Chong Teng, Donghong Ji

Explainability & Interpretability in NLP

Challenge:	Despite the development of many subdirections, Cross-Document Cross-Lingual NLI remains largely unexplored.
Approach:	They propose a novel paradigm that extends traditional NLI capabilities to multi-document, multilingual scenarios by integrating RST-enhanced graph fusion with interpretability-aware prediction.
Outcome:	The proposed method improves on existing models and document-level NLI to multi-document, multilingual scenarios.

Theory of Mind for Multi-Agent Collaboration via Large Language Models (2023.emnlp-main)

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Huao Li, Yu Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Charles Lewis, Katia Sycara

Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization on Multi-party Conversation (2025.acl-long)

Challenge:	Recent large language models (LLMs) have demonstrated impressive accomplishments in reasoning and planning, but their abilities in multi-agent collaborations remain unexplored.
Approach:	They propose to use explicit belief state representations to enhance task performance and the accuracy of ToM inferences for LLM-based agents.
Outcome:	The proposed model improves performance and accuracy of ToM inferences for LLM-based agents.

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Luyao Cheng, Hui Wang, Chong Deng, Siqi Zheng, Yafeng Chen, Rongjie Huang, Qinglin Zhang, Qian Chen, Xihao Li, Wen Wang

TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis (2025.acl-long)

Challenge:	Mainstream speaker diarization systems rely only on acoustic information, making it challenging in complex aural environments.
Approach:	They propose a multimodal approach that integrates audio, visual, and semantic cues to enhance speaker diarization.
Outcome:	The proposed approach outperforms state-of-the-art methods on multi-party conversations . it integrates audio-visual-semantic cues into the clustering process for acoustic speaker embeddings .

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Xiaorui Wu, Xiaofeng Mao, Fei Li, Xin Zhang, Xuanhong Li, Chong Teng, Donghong Ji, Zhuang Li

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) excel in natural language processing tasks but are vulnerable to harmful content and being exploited for malicious purposes.
Approach:	They propose a framework to measure the risk coverage of alignment datasets across three dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics.
Outcome:	The proposed framework measures risk coverage across Lexical Diversity, Malicious Intent, and Jailbreak Tactics.

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Shaoqing Lin, Chong Teng, Fei Li, Donghong Ji, Lizhen Qu, Zhuang Li

Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing (2024.acl-long)

Challenge:	Current approaches typically merge sentence-level parsing outputs for discourse input, resulting in fragmented graphs and degraded downstream performance.
Approach:	They propose a task for discourse-level text scene graph parsing that merges sentence-level outputs for discourse input and propose 'DiscoSG' a dataset of 400 expert-annotated and 8,430 synthesised multi-sentence caption-graph pairs is used to test the new task.
Outcome:	The proposed task improves SPICE by 30% over the baseline while achieving 86 faster inference than existing models.

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Chengjie Zhou, Bobo Li, Hao Fei, Fei Li, Chong Teng, Donghong Ji

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling (2026.acl-long)

Challenge:	Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency graph parsing due to the internal structures of spans neglected.
Approach:	They propose to use latent spans as latent subtrees to model internal structures of spans and leverage TreeCRFs to extract the complete opinion tuple from a sentence.
Outcome:	The proposed method performs significantly better than all previous bi-lexical methods, achieving new state-of-the-art.

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Hao-Xiang Xu, Chong Deng, Jiaqing Liu, Wen Wang, Qian Chen, Lujia Bao, Xiangang Li, Zhen-Hua Ling

Challenge:	Large Language Models (LLMs) extend their capabilities through function-calling (FC) however, obtaining and annotating real function-called data is challenging, and synthetic data from existing pipelines suffers from unreliable APIs, limited tool scalability, insufficient diversity, and weak quality control.
Approach:	They propose a pipeline for generating FC training data using reliable tools and a multi-agent framework that supports a dialogue generation system that produces conversations spanning diverse scenarios.
Outcome:	The proposed pipeline outperforms open-source models in in-domain FC performance and out-of-domain generalization while reaching FC capabilities comparable to some of the latest API-based models.

Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning (2023.emnlp-main)

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Chong Li, Shaonan Wang, Yunhao Zhang, Jiajun Zhang, Chengqing Zong

Responsible & Trustworthy NLP

Challenge:	Experimental results show that multi-head attention module evolves functional specialization after multi-task training.
Approach:	They propose a method to quantify the degree of functional specialization in multi-head attention . they propose 'multi-task training' method to increase functional specialisation and mitigate negative information transfer .
Outcome:	The proposed method increases functional specialization and mitigates negative information transfer in multi-task learning without adding any parameters.

Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment (2024.naacl-long)

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Chong Li, Shaonan Wang, Jiajun Zhang, Chengqing Zong

Subgoal Discovery for Hierarchical Dialogue Policy Learning (D18-1)

Challenge:	Existing studies show that multilingual generative models exhibit a strong language bias toward high-resource languages.
Approach:	They propose a cross-lingual alignment framework exploiting pairs of translation sentences to improve cross-linguistic abilities.
Outcome:	The proposed framework improves cross-lingual abilities and mitigates performance gap.

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Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara

Dialogue Systems & Conversational Agents

Challenge:	Existing methods to develop dialogue agents for complex tasks require sparse reward signals.
Approach:	They propose a divide-and-conquer approach that exploits the hidden structure of a task . they use subgoals to divide a goal-oriented task into simpler subgoal sets .
Outcome:	The proposed approach performs competitively against state-of-the-art methods that require human-defined subgoals.

Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information (2024.lrec-main)

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Qiang Gao, Bobo Li, Zixiang Meng, Yunlong Li, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

Information Extraction & Text Mining

Challenge:	Existing cross-document event coreference resolution models lack the ability to capture long-distance dependencies.
Approach:	They propose to construct document-level Rhetorical Structure Theory trees and cross-document Lexical Chains to model structural and semantic information of documents.
Outcome:	The proposed model outperforms baseline models on English and Chinese datasets by large margins.

V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me (2025.acl-long)

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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Jiapeng Wang, YiFan Zhang, Zhuoma GongQue, Chong Sun, Yida Xu, Yadong Xue, Ye Tian, Zhimin Bao, Lan Yang, Chen Li, Honggang Zhang

Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing (2026.findings-acl)

Challenge:	Deciphering oracle bone scripts using AI technology is not an overnight task due to the evolution of written language over millennia.
Approach:	They propose a framework that utilizes Large Multi-modal Models (LMMs) for interpreting Oracle Bone Script (OBS).
Outcome:	The proposed framework provides quantitative analyses and superior deciphering capability.

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Chunyu Ye, Yunhao Zhang, Jingyuan Sun, Chong Li, Yang Zhao, Shaonan Wang

Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training (2021.emnlp-main)

Challenge:	Current approaches to decoding language from the human brain rely on unimodal representations, neglecting the brain’s inherently multimodal processing.
Approach:	They propose a framework that leverages Multimodal Large Language Models to align brain signals with a shared semantic space encompassing text, images, and audio.
Outcome:	The proposed framework achieves an 8.48% improvement on the most commonly used benchmark on fMRI datasets with textual, visual, and auditory stimuli.

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Zhengyan Li, Yicheng Zou, Chong Zhang, Qi Zhang, Zhongyu Wei

Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities (2026.findings-acl)

Challenge:	Recent studies have focused on identifying the sentiment polarity of aspects in product reviews.
Approach:	They propose to use supervised Contrastive Pre-Training to learn implicit sentiment . they propose to train large-scale sentiment-annotated corpora from in-domain language resources .
Outcome:	The proposed model achieves state-of-the-art performance on SemEval2014 benchmarks and comprehensively validates its effectiveness on learning implicit sentiment.

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Kang He, Yuzhe Ding, Rao Fu, Yukang Feng, Kaipeng Zhang, Yiming Liu, Fei Li, Chong Teng, Donghong Ji

LCQMC:A Large-scale Chinese Question Matching Corpus (C18-1)

Challenge:	Existing methods for multimodal sentiment analysis are often dynamically incomplete.
Approach:	They propose a new uncertainty-calibrated elastic alignment framework to address these issues by employing probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment.
Outcome:	The proposed framework outperforms state-of-the-art models in multiple benchmarks and consistently outperformed existing models.

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Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li, Buzhou Tang

Paraphrasing

Challenge:	Existing methods for question answering system lack large-scale question matching corpora . lack of large-sized question matching results in problem solving .
Approach:	They propose a large-scale Chinese question matching corpus which is released to the public . they use a search engine to collect large-sized question pairs related to high-frequency words .
Outcome:	The proposed corpus is more general than paraphrase corpus as it focuses on intent matching rather than paraphrasing.

Dynamic Emotion and Personality Profiling for Multimodal Deception Detection (2026.acl-long)

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Li Zheng, Yanyi Luo, Hao Fei, Yuzhe Ding, Yujie Huang, Fei Li, Chong Teng, Donghong Ji

Refining and Synthesis: A Simple yet Effective Data Augmentation Framework for Cross-Domain Aspect-based Sentiment Analysis (2024.findings-acl)

Challenge:	Existing methods for deception detection lack sample-level dynamic annotations for emotions and personality.
Approach:	They propose a multi-model multi-prompt annotation scheme and a strict label quality evaluation standard for deception, emotion, and personality annotations.
Outcome:	The proposed framework outperforms state-of-the-art models on the MDPE and DDEP datasets.

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Haining Wang, Kang He, Bobo Li, Lei Chen, Fei Li, Xu Han, Chong Teng, Donghong Ji

LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)

Challenge:	Aspect-based Sentiment Analysis (ABSA) data augmentation has attracted increasing attention in recent years due to data sparsity.
Approach:	They propose a framework to augment ABSA data using pseudo labels for target domain . they refine generated labeled data using a natural language inference filter .
Outcome:	The proposed framework outperforms 7 strong baselines on 4 kinds of ABSA tasks.

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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun

Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation (2023.emnlp-main)

Challenge:	Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size.
Approach:	They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding.
Outcome:	The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models.

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Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian, Liang Pang, Jiangnan Li, Qirong Ho, Yanan Cao, Weiping Wang

What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)

Challenge:	Existing knowledge-grounded dialogue generation models struggle with dull and repetitive outputs, a problem commonly termed as text degeneration.
Approach:	They propose a framework that allows the model to "cheat" the objective by duplicating knowledge segments in a superficial pattern matching based on overlap.
Outcome:	The proposed framework can be applied to a WoW dataset and shows that it works across models and decoding strategies.

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Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji

Natural Language Interfaces

Challenge:	Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields.
Approach:	They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies.
Outcome:	The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges.

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

Robustness in NLP

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

Mitigating the Discrepancy Between Video and Text Temporal Sequences: A Time-Perception Enhanced Video Grounding method for LLM (2025.coling-main)

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Xuefen Li, Bo Wang, Ge Shi, Chong Feng, Jiahao Teng