Papers by Yue Min

Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)

Challenge:	Existing methods for fine-grained opinion mining (OM) are based on span-based annotations, but they are not effective.
Approach:	They propose a unified span-based approach for the end-to-end OM setting using syntactic constituents and multi-task learning to integrate them into the proposed model.
Outcome:	The proposed approach achieves significant improvements over previous work on the MPQA 2.0 dataset and reduces the number of wrongly-predicted opinion expressions and roles.

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Bin Liang, Ang Li, Jingqian Zhao, Lin Gui, Min Yang, Yue Yu, Kam-Fai Wong, Ruifeng Xu

Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)

Challenge:	Existing methods for stance detection for pure texts have limited results to multi-modal content.
Approach:	They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities.
Outcome:	The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter .

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Qingkai Min, Qipeng Guo, Xiangkun Hu, Songfang Huang, Zheng Zhang, Yue Zhang

Information Extraction & Text Mining

Challenge:	Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts.
Approach:	They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM.
Outcome:	The proposed approach surpasses the performance of both large and small language models individually, underscoring its effectiveness in diverse scenarios.

StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation (2026.findings-acl)

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Qingyu Meng, Min Chen, Dingming Liu, Yifan Mo, Yue Su, Xin Sun, Koen Hindriks, Jiahuan Pei

Dialogue Systems & Conversational Agents

Challenge:	Motivational interviewing (MI) is a directive, client-centered counseling approach for eliciting clients' motivation for behavioral change.
Approach:	They propose a multi-LLM agent framework for controllable MI dialogue generation . therapist and client agents generate MI-coded utterances guided by MI codes .
Outcome:	The proposed framework can generate fluent dialogues with minimal intervention time and a high level of evaluation.

Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences (2020.acl-main)

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Xiangyu Duan, Baijun Ji, Hao Jia, Min Tan, Min Zhang, Boxing Chen, Weihua Luo, Yue Zhang

Multilinguality

Challenge:	a monolingual speaker can learn to translate by looking up a bilingual dictionary . a novel task of machine translation (MT) is based on no parallel sentences but can refer to a ground-truth bilingual dictionary and large-scale monolingual corpora.
Approach:	They propose a task of machine translation that uses a bilingual dictionary and large-scale monolingual corpora to translate a monolingual speaker.
Outcome:	The proposed task is based on a bilingual dictionary and large scale monolingual corpora, while being independent on parallel sentences.

NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts (2023.findings-acl)

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Yue Zhang, Bo Zhang, Haochen Jiang, Zhenghua Li, Chen Li, Fei Huang, Min Zhang

A Pilot Study for Chinese SQL Semantic Parsing (D19-1)

Challenge:	Recent studies on Chinese grammatical error correction focus on learning essays.
Approach:	They propose a Chinese grammatical error correction dataset that annotates multiple references for 12,500 sentences from three native domains.
Outcome:	The proposed dataset can be used to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.

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Qingkai Min, Yuefeng Shi, Yue Zhang

TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice (2026.acl-long)

Challenge:	Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping.
Approach:	They propose to use a Chinese-based semantic parser to map natural language text into SQL.
Outcome:	The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping.

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Gang Hu, Yating Chen, Haiyan Ding, Wang Gao, Huang Jiajia, Min Peng, Qianqian Xie, Kun Yue

Challenge:	Large Language Models excel in general domains but lack real-world practical capabilities.
Approach:	They propose a benchmark for Chinese taxation practice that combines 10 traditional application tasks with 3 pioneering real-world scenarios.
Outcome:	The proposed benchmark combines 10 traditional tasks with 3 pioneering real-world scenarios.

Answering Narrative-Driven Recommendation Queries via a Retrieve–Rank Paradigm and the OCG-Agent (2025.emnlp-main)

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Yunxiao Shi, Haoning Shang, Xing Zi, Wujiang Xu, Yue Feng, Min Xu

Question Answering

Challenge:	Existing approaches to generate narrative-driven recommendation are based on large language models (LLMs) but the RAG paradigm is inherently ill-suited for such special queries.
Approach:	They propose a novel retrieve-rank paradigm that generatively retrieves structurally adaptive and semantically aligned candidates, ensuring both extensive candidate coverage and high-quality information.
Outcome:	The proposed paradigm outperforms the existing paradigm and the existing one under real-world scenarios.

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

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

Semantic Text Processing

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

Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)

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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang

Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model (2024.lrec-main)

Challenge:	Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios.
Approach:	They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice.
Outcome:	The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models .

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Yue Wang, Zilong Zheng, Juntao Li, Zhihui Liu, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang

MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction (2022.naacl-main)

Challenge:	Large Language Models (LLMs) have been gaining attention for their ability to perform a wide range of open-domain tasks . however, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios .
Approach:	They propose a task to evaluate the performance of Large Language Models (LLMs) they propose RCSC task to convert Chinese text into correct text .
Outcome:	The proposed task evaluates the performance of existing methods in Chinese text . the realistic Chinese spell checker can achieve state-of-the-art performance on the task .

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Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)

Challenge:	Using a multi-reference multi-source evaluation dataset, Chinese grammatical error correction (CGEC) is relatively scarce.
Approach:	They propose a multi-reference multi-source evaluation dataset for Chinese grammar error correction . the dataset contains 7,063 sentences written by Chinese-as-a-Second-Language learners .
Outcome:	The proposed dataset can be used to evaluate Chinese grammar errors in Chinese.

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Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li

Dialogue Systems & Conversational Agents

Challenge:	End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity.
Approach:	They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends.
Outcome:	The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.

𝒜3: Automatic Alignment Framework for Attributed Text Generation (2025.acl-long)

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Yue Wang, Haoke Zhang, Juntao Li, Jinxiong Chang, Min Zhang

DataSciBench: An LLM Agent Benchmark for Data Science (2026.findings-acl)

Challenge:	Existing approaches of aligning large language models to follow user instructions can lead to undue emphasis on irrelevant documents, which in turn reduces the quality of responses.
Approach:	They propose to use a framework to automatically generate high-quality attributed query-response pairs for both supervised fine-tuning and preference optimization stages without human annotation.
Outcome:	The proposed framework can generate high-quality attributed query-response pairs without human annotation without human intervention.

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Dan Zhang, Sining Zhoubian, Min Cai, Fengzu Li, Lekang Yang, Wei Wang, Tianjiao Dong, Ziniu Hu, Jie Tang, Yisong Yue

Challenge:	Existing benchmarks focus on single task, simple evaluation metrics, and readily available ground truth (GT) DataSciBench is built on curated, natural, and challenging prompts with complex evaluation criteria and uncertain GT.
Approach:	They propose a benchmark for evaluating Large Language Models in data science that integrates LLM-based self-consistency and human verification to ensure accuracy.
Outcome:	The proposed framework outperforms open-source models in all metrics and offers rigorous insights into LLM strengths and weaknesses.

SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser (2022.emnlp-main)

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Yue Zhang, Bo Zhang, Zhenghua Li, Zuyi Bao, Chen Li, Min Zhang

A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution (2024.naacl-long)

Challenge:	Existing approaches to grammatical error correction are unreliable when processing ungrammatically . a new approach is proposed that incorporates dependency syntactic information into the encoder part of GEC models.
Approach:	They propose a syntax-enhanced grammatical error correction approach called SynGEC that incorporates dependency syntactic information into the encoder part of GEC models.
Outcome:	The proposed approach outperforms strong baselines and achieves competitive performance on mainstream English and Chinese GEC datasets.

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Bowen Ding, Qingkai Min, Shengkun Ma, Yingjie Li, Linyi Yang, Yue Zhang

Responsible & Trustworthy NLP

Challenge:	Existing state-of-the-art event coreference resolution systems rely on spurious and spurious associations in the input mention pair text.
Approach:	They propose a rationale-centric counterfactual data augmentation method that leverages the debiasing capability of counterfact data haussed by LLM-in-the-loop to mitigate spurious association while emphasizing causation.
Outcome:	The proposed method achieves state-of-the-art on three popular cross-document benchmarks and demonstrates robustness in out-of domain scenarios.

Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks (2020.acl-main)

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Bo Zhang, Yue Zhang, Rui Wang, Zhenghua Li, Min Zhang

Contrastive Attention Mechanism for Abstractive Sentence Summarization (D19-1)

Challenge:	Opinion role labeling (ORL) is a fine-grained opinion analysis task . due to the scarcity of labeled data, ORL remains challenging for data-driven methods due to its complexity and complexity.
Approach:	They propose to integrate syntactic knowledge into ORL models by comparing and integrating different representations and using dependency graph convolutional networks to encode parser information at different processing levels.
Outcome:	The proposed model achieves 4.34 higher F1 score than the current state-of-the-art.

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Xiangyu Duan, Hongfei Yu, Mingming Yin, Min Zhang, Weihua Luo, Yue Zhang

Summarization

Challenge:	Existing attention mechanisms for abstractive sentence summarization are based on rule-based methods and large-scale training corpora.
Approach:	They propose a contrastive attention mechanism that extends the sequence-to-sequence framework for abstractive sentence summarization task.
Outcome:	The proposed mechanism improves the state-of-the-art on the abstractive sentence summarization task.

MelTrim: Coarse-to-Fine Data Pruning for Speech Classification (2026.findings-acl)

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Shaobo Wang, Tianle Niu, Xuan Ouyang, Xintong Li, Zhengkun Ge, Yue Min, Xiaoqian Liu, Hankun Wang, Linfeng Zhang

Information Extraction & Text Mining

Challenge:	Unlike image or text classification, speech classification tasks are particularly challenging due to the difficulty in capturing the acoustic, semantic, and contextual representations.
Approach:	They propose a dataset pruning method that coarsely filters redundant samples using DBSCAN clustering on Mel-Frequency Cepstral Coefficients (MFCC) features.
Outcome:	The proposed method achieves 49.5% improvement in WA on the MEAD dataset and 41.9% reduction in EER on speaker identification tasks.

Multi-Document Event Extraction Using Large and Small Language Models (2025.emnlp-main)

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Qingkai Min, Zitian Qu, Qipeng Guo, Xiangkun Hu, Zheng Zhang, Yue Zhang

Information Extraction & Text Mining

Challenge:	Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges .
Approach:	They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks.
Outcome:	The proposed framework outperforms existing methods and provides new insights into collaborative reasoning to tackle the complexities of multi-document event extraction.

Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing (2025.acl-long)

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Peiming Guo, Meishan Zhang, Jianling Li, Min Zhang, Yue Zhang

Semantic Text Processing

Challenge:	Existing constituency treebanks are limited in out-of-domain settings, therefore constituency parsing is still a challenge.
Approach:	They propose a novel method for constituency parsing using large language models . they use a cross-domain constituency treebank to fill missing words with the incomplete one .
Outcome:	The proposed method achieves state-of-the-art performance on average compared with baselines on five target domains of MCTB.

Code-Switching for Enhancing NMT with Pre-Specified Translation (N19-1)

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Kai Song, Yue Zhang, Heng Yu, Weihua Luo, Kun Wang, Min Zhang

Multilinguality

Challenge:	Existing methods to constrain NMT use placeholder tags for lexicon words and hard constraints during decoding.
Approach:	They propose to use placeholder tags to replace lexicon words with target translations . they use a data augmentation method to make code-switched training data .
Outcome:	The proposed method improves translation quality without hurting unconstrained words.

G-SPEED: General SParse Efficient Editing MoDel (2023.findings-emnlp)

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Haoke Zhang, Yue Wang, Juntao Li, Xiabing Zhou, Min Zhang

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages.
Approach:	They propose a general SParse Efficient Editing MoDel which can fulfill diverse editing requirements through a single model while maintaining low computational costs.
Outcome:	The proposed model can fulfill diverse editing requirements through a single model while maintaining low computational costs.

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

Speech & Audio in NLP

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

LLM-enhanced Self-training for Cross-domain Constituency Parsing (2023.emnlp-main)

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Jianling Li, Meishan Zhang, Peiming Guo, Min Zhang, Yue Zhang