Papers by Bin Liang

Reducing Spurious Correlations in Aspect-based Sentiment Analysis with Explanation from Large Language Models (2023.findings-emnlp)

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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Qianlong Wang, Keyang Ding, Bin Liang, Min Yang, Ruifeng Xu

Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering (2024.emnlp-industry)

Challenge:	Aspect-based sentiment analysis models are susceptible to learning spurious correlations between words . a recent study shows that feature engineering is time-consuming and costly .
Approach:	They propose to use a template to prompt LLMs to generate an appropriate explanation for the sentiment polarity of each aspect to reduce spurious correlations.
Outcome:	The proposed methods improve ABSA models and their generalization ability.

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Lu Shi, Bin Qi, Jiarui Luo, Yang Zhang, Zhanzhao Liang, Zhaowei Gao, Wenke Deng, Lin Sun

Information Retrieval

Challenge:	Aegis is an advanced LLM-based multi-agent for intelligent functional safety engineering that can perform all phases of a vehicle's lifecycle, including design, development, production, operation, and decommissioning.
Approach:	They introduce Aegis: An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering.
Outcome:	The proposed solution can perform Hazard Analysis and Risk Assessment (HARA), document Functional Safety Requirements (FSR), and plan test cases for Automatic Emergency Braking (AEB) systems.

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

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

Learning distributed sentence vectors with bi-directional 3D convolutions (2020.coling-main)

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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Bin Liu, Liang Wang, Guosheng Yin

Representation Learning

Challenge:	Existing methods that render words or characters into images separately, but instead use text's visual features as input, we use 3-dimensional convolutions to learn distributed sentence representation.
Approach:	They propose to use text's visual features as input to learn distributed sentence representation using 3-dimensional sentence tensors and multiple 3-dimensional convolutions with different lengths are applied to the sentence .
Outcome:	The proposed model performs well on several downstream natural language processing tasks.

Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration (2026.findings-acl)

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Linzhuang Sun, Tianyu Guo, Hao Liang, Ruitong Liu, Yuying Li, Qifeng Cai, Jingxuan Wei, Yuchen Wu, Bihui Yu, Xiangxiang Zhang, Wentao Zhang, Bin Cui

Programming Languages in NLP

Challenge:	Structured Query Language (SQL) is the cornerstone for data-driven decision-making.
Approach:	They propose a benchmark to rigorously evaluate Large Language Models within a dynamic interaction framework.
Outcome:	The proposed benchmark aims to rigorously evaluate LLMs within a dynamic interaction framework.

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)

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Bin Liang, Hang Su, Rongdi Yin, Lin Gui, Min Yang, Qin Zhao, Xiaoqi Yu, Ruifeng Xu

An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis (2023.findings-acl)

Challenge:	Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence.
Approach:	They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights .
Outcome:	The proposed approach outperforms the state-of-the-art methods on 6 benchmark datasets.

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Yice Zhang, Yifan Yang, Bin Liang, Shiwei Chen, Bing Qin, Ruifeng Xu

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Existing approaches to Aspect-Based Sentiment Analysis (ABSA) are lacking in a comprehensive evaluation and fair comparison.
Approach:	They propose to use a knowledge-mining method to build a large-scale knowledge-annotated SPT corpus and integrate sentiment knowledge into pre-training.
Outcome:	The proposed method is able to build a large-scale knowledge-annotated SPT corpus and compares with other methods.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

A Multi-persona Framework for Argument Quality Assessment (2025.acl-long)

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Bojun Jin, Jianzhu Bao, Yufang Hou, Yang Sun, Yice Zhang, Huajie Wang, Bin Liang, Ruifeng Xu

Challenge:	Existing methods for argument quality assessment do not consider multi-perspective evaluation due to subjective nature of arguments.
Approach:	They propose a multi-persona framework for argument quality assessment that simulates diverse evaluator perspectives through large language models.
Outcome:	The proposed framework outperforms baselines while providing comprehensive multi-perspective rationales on IBM-Rank-30k and IBM-ArgQ-5.3kArgs datasets.

MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models (2025.findings-acl)

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Boyang Xue, Hongru Wang, Rui Wang, Sheng Wang, Zezhong Wang, Yiming Du, Bin Liang, Wenxuan Zhang, Kam-Fai Wong

Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors (2026.acl-long)

Challenge:	Existing studies on LLM confidence estimations in languages other than English have been limited to English.
Approach:	They propose to use question-related language to prompt LLMs to assess their confidence in large language models.
Outcome:	The proposed model improves on question-related language prompts for LS tasks, while English exhibits notable linguistic dominance in confidence estimations.

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Zhiwei Zhang, Fei Zhao, Rui Wang, Zezhong Wang, Bin Liang, Jiakang Wang, Yao Hu, Shaosheng Cao, Kam-Fai Wong

Robustness in NLP

Challenge:	Large language models (LLMs) can call tools effectively, but they remain brittle in multi-turn execution.
Approach:	They propose a framework that converts execution errors into on-policy corrective supervision within the RL training loop.
Outcome:	The proposed framework improves the error recovery rate of Qwen3-8B by 5.7% absolute and overall accuracy by 4.0% on BFCL v4 Multi-Turn.

PITA: Prompting Task Interaction for Argumentation Mining (2024.acl-long)

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Yang Sun, Muyi Wang, Jianzhu Bao, Bin Liang, Xiaoyan Zhao, Caihua Yang, Min Yang, Ruifeng Xu

Text Classification

Challenge:	Argumentation mining (AM) aims to detect arguments and their inherent relations from textual compositions.
Approach:	They propose a method to model the inter-relationships among three subtasks within a generative framework.
Outcome:	The proposed method achieves state-of-the-art performance on two AM benchmarks.

MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models (2025.emnlp-main)

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Zhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao, Yifan Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis (2020.coling-main)

Challenge:	Current approaches focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context.
Approach:	They propose a benchmark to evaluate how large vision language models understand memes in their original context.
Outcome:	The proposed benchmark evaluates how large vision language models understand meme intent in their original context.

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Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, Ruifeng Xu

SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis (2022.emnlp-main)

Challenge:	Existing methods for aspect sentiment analysis do not include explicit sentiment expressions.
Approach:	They propose to construct a heterogeneous graph by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect.
Outcome:	The proposed model outperforms state-of-the-art methods on four benchmark datasets and significantly boosts performance in comparison with BERT.

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Bingbing Wang, Bin Liang, Jiachen Du, Min Yang, Ruifeng Xu

Context-aware Embedding for Targeted Aspect-based Sentiment Analysis (P19-1)

Challenge:	Existing research on sentiment analysis based on eye movement signals has been attributed importance.
Approach:	They propose a linguistic probing eye movement paradigm to extract eye movement features based on the relationship between linguistic features and human reading behavior.
Outcome:	The proposed graph architecture achieves state-of-the-art performance on two sentiment analysis datasets with eye movement signals and three sentiment analysis data without eye movement signal.

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Bin Liang, Jiachen Du, Ruifeng Xu, Binyang Li, Hejiao Huang

Representation Learning

Challenge:	Existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA.
Approach:	They propose to refine the embeddings of targets and aspects using a sparse coefficient vector . this allows the embeds to be refined from highly correlative words instead of context-independent vectors .
Outcome:	Experiments show that the proposed method improves on two benchmark datasets.

A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)

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Jianzhu Bao, Yuhang He, Yang Sun, Bin Liang, Jiachen Du, Bing Qin, Min Yang, Ruifeng Xu

Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph (2021.emnlp-main)

Challenge:	Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks.
Approach:	They propose a generative framework where expected outputs of AM are framed as a simple target sequence.
Outcome:	The proposed framework achieves state-of-the-art on two AM benchmarks.

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Jianzhu Bao, Bin Liang, Jingyi Sun, Yice Zhang, Min Yang, Ruifeng Xu

Structured Data in NLP

Challenge:	Existing studies on argumentation mining focus on monological argumentation and dialogical argumentation.
Approach:	They propose a mutual guidance framework that could guide arguments in one passage . they propose an inter-sentence relation graph to effectively model the inter-relations between two sentences .
Outcome:	The proposed method outperforms the current state-of-the-art model.

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs (2023.findings-emnlp)

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Hongru Wang, Rui Wang, Fei Mi, Yang Deng, Zezhong Wang, Bin Liang, Ruifeng Xu, Kam-Fai Wong

Natural Language Interfaces

Challenge:	Existing LLMs generate responses based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context.
Approach:	They propose a linguistic cue-based chain-of-thoughts method which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue.
Outcome:	The proposed method outperforms standard prompting methods on in-depth dialogue questions and linguistic cues exhibited in the context.

Inducing Argument Facets for Faithful Opinion Summarization (2025.findings-emnlp)

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Jian Wang, Yanjie Liang, Yuqing Sun, Bin Gong

Text Generation

Challenge:	Faithful opinion summarization task involves generating a summary that covers the majority and minority opinions in documents.
Approach:	They propose a facets-guided opinion summarization method that induces facets and partitions documents into multiple facet-specific sets.
Outcome:	The proposed method outperforms state-of-the-art methods and multiple LLMs on two representative datasets and shows it can be used in specialty domains.

VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs (2025.emnlp-main)

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Keer Lu, Keshi Zhao, Zhuoran Zhang, Zheng Liang, Bin Cui, Tengjiao Wang, Wentao Zhang

COPR: Continual Human Preference Learning via Optimal Policy Regularization (2025.findings-acl)

Challenge:	Existing work focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains.
Approach:	They propose a data composition framework that allows LLMs to enhance their multi-domain capabilities during supervised fine-tuning.
Outcome:	The proposed framework improves multi-domain fostering performance by 29.77% compared to uniform weights.

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Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu

Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022.emnlp-main)

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is effective for aligning Large Language Models with human preferences, but its complex process limits its ability to continually learn human feedback.
Approach:	They propose a non-RL offline method to convert historical optimal policies into optimization constraints when continually learning new preferences.
Outcome:	The proposed method outperforms strong CL baselines in terms of reward-based evaluations and human assessment.

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Yice Zhang, Yifan Yang, Yihui Li, Bin Liang, Shiwei Chen, Yixue Dang, Min Yang, Ruifeng Xu

A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection (2023.emnlp-main)

Challenge:	Existing work focuses on extracting aspect terms and opinion terms without considering the relations between aspect terms .
Approach:	They propose a task to extract aspect terms, opinion terms, and expressed sentiments from a two-dimensional (2D) table.
Outcome:	The proposed method achieves state-of-the-art on several public benchmarks and is well-suited to the ASTE task.

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Geng Tu, Ran Jing, Bin Liang, Min Yang, Kam-Fai Wong, Ruifeng Xu

EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)

Challenge:	Existing studies in Emotion Recognition in Conversations (ERC) focus on capturing context-sensitive and speaker-sensitive dependencies, ignoring the unintended dataset biases of data.
Approach:	They propose a training-free debiasing framework that extracts biases from the model by generating counterfactual utterances and contexts and mitigates them using simple yet empirically robust element-wise subtraction operations.
Outcome:	Experiments on three public datasets show that the proposed framework improves generalization ability and fairness across different ERC models.

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Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

Challenge:	Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios.
Approach:	They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts.
Outcome:	The proposed model performs comparable to state-of-the-art large models on the test set.

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

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Bingbing Wang, Zhengda Jin, Bin Liang, Wenjie Li, Jing Li, Ruifeng Xu, Min Zhang

Data-Centric Perspectives on Agentic Retrieval-Augmented Generation: A Survey (2026.findings-acl)

Challenge:	Existing methods operate by learning to fuse modalities, leading to frequent misjudgments.
Approach:	They propose a paradigm shift from learning to fuse to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop.
Outcome:	The proposed model significantly outperforms baseline models and exhibits strong generalization.

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Jingwen Deng, Jihao Huang, Zhen Hao Wong, Hao Liang, Quanqing Xu, Bin Cui, Wentao Zhang

Information Retrieval

Challenge:	Large Language Models (LLMs) excel at natural language understanding and generation, yet rely on static pre-training data.
Approach:	They propose to augment Large Language Models with external retrieval to ground model outputs . traditional RAG is constrained by a fixed retrieve-then-generate routine . authors aim to guide creation of high-quality datasets for next generation of adaptive LLM agents .
Outcome:	The proposed model can decompose tasks, issue exploratory queries, and refine evidence through iterative retrieval.

Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)

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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu

SEEK: Segmented Embedding of Knowledge Graphs (2020.acl-main)

Challenge:	Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs).
Approach:	They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts.
Outcome:	The proposed framework outperforms strong baselines while significantly improving explainability.

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Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu

Decomposing Argumentative Essay Generation via Dialectical Planning of Complex Reasoning (2024.findings-acl)

Challenge:	Existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them far from satisfactory.
Approach:	They propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity.
Outcome:	The proposed framework can achieve highly competitive relational expressiveness without increasing model complexity.

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Yuhang He, Jianzhu Bao, Yang Sun, Bin Liang, Min Yang, Bing Qin, Ruifeng Xu

EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems (2026.acl-long)

Challenge:	Argumentative Essay Generation (AEG) is a challenging task in computational argumentation, where detailed logical reasoning and effective rhetorical skills are essential.
Approach:	They propose an argumentative planning strategy for prompting large language models to generate high-quality essays by sketch planning and dialectical planning.
Outcome:	The proposed method generates more dialectical and persuasive essays with higher diversity compared to baselines.

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Zhengyi Zhao, Shubo Zhang, Yiming Du, Bin Liang, Baojun Wang, Zhongyang Li, Binyang Li, Kam-Fai Wong

Natural Language Interfaces

Challenge:	Existing dialogue systems process conversational turns in isolation, overlooking event structures that guide natural interactions.
Approach:	They propose a framework that explicitly models relationships between conversational events to generate more contextually appropriate dialogue responses.
Outcome:	Experiments on three dialogue datasets show that the proposed approach produces more natural responses while requiring less computational overhead.

Context or Knowledge is Not Always Necessary: A Contrastive Learning Framework for Emotion Recognition in Conversations (2023.findings-acl)

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Geng Tu, Bin Liang, Ruibin Mao, Min Yang, Ruifeng Xu

MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System (2023.findings-acl)

Challenge:	Existing studies focus on modeling context-sensitive dependencies and knowledge-sensitive dependences.
Approach:	They propose a framework based on contrastive learning called CKCL to distinguish utterances for better vector representations.
Outcome:	The proposed framework outperforms state-of-the-art models on four datasets.

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Libo Qin, Shijue Huang, Qiguang Chen, Chenran Cai, Yudi Zhang, Bin Liang, Wanxiang Che, Ruifeng Xu

T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering (2025.emnlp-main)

Challenge:	Existing benchmarks for multi-modal sarcasm detection have some shortcomings . a new framework can leverage multi-grained cues from multiple perspectives for multimodal detection .
Approach:	They propose a correction dataset that removes spurious cues and re-annotates the unreasonable samples.
Outcome:	The proposed framework outperforms the existing benchmarks in multi-modal sarcasm detection.

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Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Question Answering

Challenge:	Existing efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions but add human bias to the reasoning process.
Approach:	They propose a framework that dynamically adapts reasoning depth based on question complexity.
Outcome:	Experimental results show that the proposed framework achieves higher accuracy than baseline methods and reduces computational overhead by up to 25.2%.

Pre-training with Meta Learning for Chinese Word Segmentation (2021.naacl-main)

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Zhen Ke, Liang Shi, Songtao Sun, Erli Meng, Bin Wang, Xipeng Qiu

Set Learning for Generative Information Extraction (2023.emnlp-main)

Challenge:	Recent studies show that pre-trained models are beneficial to Chinese Word Segmentation (CWS). However, these models lack task-specific prior segmentation knowledge.
Approach:	They propose a pre-trained Chinese word segmentation model MetaSeg which incorporates meta learning into a multi-criteria pre-training task.
Outcome:	Empirical results show that MetaSeg can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings.

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Jiangnan Li, Yice Zhang, Bin Liang, Kam-Fai Wong, Ruifeng Xu

Information Extraction & Text Mining

Challenge:	Recent efforts to employ sequence-to-sequence models to solve IE tasks have been focused on a single problem: structured objects are an unordered set, resulting in a potential order bias.
Approach:	They propose a sequence-to-sequence (Seq2Sequen) model that considers multiple permutations of structured objects to optimize set probability approximately.
Outcome:	The proposed model improves existing frameworks on vast tasks and datasets.

CFBench: A Comprehensive Constraints-Following Benchmark for LLMs (2025.acl-long)

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Tao Zhang, ChengLIn Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Tao Zhang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou

RISER: Orchestrating Latent Reasoning Skills for Adaptive Activation Steering (2026.findings-acl)

Challenge:	Existing evaluations of Large Language Models (LLMs) focus on fragmented constraints or narrow scenarios, but they overlook the comprehensiveness and authenticity of constraints from the user’s perspective.
Approach:	They propose a Chinese Comprehensive Constraints Following Benchmark for LLMs that compiles constraints from real-world instructions and constructs a systematic framework for constraint types.
Outcome:	The proposed framework integrates multi-dimensional assessment criteria with requirement prioritization, covering various perspectives of constraints, instructions, and requirement fulfillment.

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Wencheng Ye, Xiaoyang Yuan, Yi Bin, Hengyu Jin, Liang Peng, Pengpeng Zeng, Heng Tao Shen

Discourse Structure-Aware Prefix for Generation-Based End-to-End Argumentation Mining (2024.findings-acl)

Challenge:	Existing methods for domain-specific reasoning with large language models require updating parameter updates.
Approach:	They propose a plug-and-play intervention framework that adaptively steers LLM reasoning in activation space.
Outcome:	The proposed framework achieves zero-shot accuracy improvements of 3.4–6.5% over the base model while outperforming chain-of-thought-style reasoning with 2–3 higher token efficiency and robust accuracy gains.

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Yang Sun, Guanrong Chen, Caihua Yang, Jianzhu Bao, Bin Liang, Xi Zeng, Min Yang, Ruifeng Xu

METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis (2020.coling-main)

Challenge:	Recent advances in AM models overlook the integration of supplementary discourse structure information, resulting in suboptimal outcomes.
Approach:	They propose a framework which generates discourse structure-aware prefixes for each layer of the generation model.
Outcome:	The proposed framework achieves state-of-the-art performance on two AM benchmarks.

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Bin Jiang, Jing Hou, Wanyue Zhou, Chao Yang, Shihan Wang, Liang Pang

Probing Structural Knowledge from Pre-trained Language Model for Argumentation Relation Classification (2022.findings-emnlp)

Challenge:	Existing methods for learning complex sentences with multiple aspects are ill-equipped to learn complex sentences .
Approach:	They propose a mutual enhanced transformation network for the ABSA task . it improves representation learning of the aspect with contextual semantic features .
Outcome:	The proposed model improves representation learning of the aspect with contextual semantic features, giving the aspect more abundant information.

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Yang Sun, Bin Liang, Jianzhu Bao, Min Yang, Ruifeng Xu

Probing Graph Decomposition for Argument Pair Extraction (2023.findings-acl)

Challenge:	Argumentation relation classification (ARC) is the most challenging subtask of argumentation mining.
Approach:	They propose a dual prior graph neural network to explore probing knowledge and syntactical information for comprehensively modeling the relationship between AC pairs.
Outcome:	The proposed model outperforms the state-of-the-art models on three public datasets.

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Yang Sun, Bin Liang, Jianzhu Bao, Yice Zhang, Geng Tu, Min Yang, Ruifeng Xu

Structured Data in NLP

Challenge:	Argument pair extraction (APE) aims to extract interactive argument pairs from two passages within a discussion.
Approach:	They propose a method to extract interactive argument pairs from two passages . they propose to decompose the probing graph into four sub-graphs based on inter- and intra-passage perspectives .
Outcome:	The proposed method improves on strong baselines on two benchmark datasets.

PEDNet: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation (2020.coling-main)

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Bin Jiang, Wanyue Zhou, Jingxu Yang, Chao Yang, Shihan Wang, Liang Pang

Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)

Challenge:	Existing persona-based dialogue models generate personalized responses using predefined persona information, but they lack personality.
Approach:	They propose a persona-based dual Alternating Learning Network that generates personalized responses using predefined persona information.
Outcome:	The proposed method produces more personalized responses than baseline methods.

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Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu

JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialogue Policy Learning (2024.lrec-main)

Challenge:	Large language models extract useful information from conversation history to enhance the response in long-term conversations.
Approach:	They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation.
Outcome:	The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation .

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Wai-Chung Kwan, Huimin Wang, Hongru Wang, Zezhong Wang, Bin Liang, Xian Wu, Yefeng Zheng, Kam-Fai Wong

In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis (2024.lrec-main)

Challenge:	Dialogue policy learning (DPL) aims to determine an abstract representation (also known as action) to guide what the response should be.
Approach:	They propose a joint Transformer-based model that generates a token-grained policy that allows more dynamic dialogue action generation without the need for predefined action candidates.
Outcome:	The proposed model outperforms existing models showing improvements of 9% and 13% in success rate and 34% and 37% in diversity of dialogue actions across two benchmark dialogue modeling tasks.

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Qianlong Wang, Hongling Xu, Keyang Ding, Bin Liang, Ruifeng Xu

In-context Learning for Few-shot Multimodal Named Entity Recognition (2023.findings-emnlp)

Challenge:	Existing approaches to solve few-shot aspect-based sentiment analysis (ABSA) are suboptimal for this task because of in-context examples .
Approach:	They propose to retrieve in-context examples for few-shot aspect-based sentiment analysis . they construct positive and negative pairs from three perspectives and train the retriever .
Outcome:	The proposed retrieval framework outperforms baselines on four ABSA datasets.

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Chenran Cai, Qianlong Wang, Bin Liang, Bing Qin, Min Yang, Kam-Fai Wong, Ruifeng Xu

An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations (2023.findings-emnlp)

Challenge:	Existing methods for named entity recognition are time-consuming and laborintensive.
Approach:	They propose a few-shot multimodal named entity recognition task that uses few examples to locate and identify named entities for a text-image pair.
Outcome:	The proposed framework outperforms baselines under several few-shot settings.

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Geng Tu, Bin Liang, Bing Qin, Kam-Fai Wong, Ruifeng Xu

Natural Language Interfaces

Challenge:	Existing efforts in ERC focus on context- and speaker-sensitive dependencies, but lack of annotated data and high cost of obtaining such knowledge is a blank slate.
Approach:	They propose a Multiple Knowledge Fusion Model to integrate multiple knowledge generated by Large Language Models (LLMs) they analyze the contribution and complementarity of this knowledge into the model.
Outcome:	The proposed model integrates multiple knowledge generated by LLMs and analyzes its contribution and complementarity on three public datasets.

CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation (2025.acl-long)

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Jingqian Zhao, Bingbing Wang, Geng Tu, Yice Zhang, Qianlong Wang, Bin Liang, Jing Li, Ruifeng Xu

Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis (2022.coling-1)

Challenge:	Publicly available datasets can be used to evaluate performance of large language models . however, contamination of test data can artificially inflate model performance .
Approach:	They propose a Contamination-resilient Evaluation strategy that updates data with real-world knowledge.
Outcome:	The proposed evaluation strategy can be used to update datasets with real-world knowledge.

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Zijie Lin, Bin Liang, Yunfei Long, Yixue Dang, Min Yang, Min Zhang, Ruifeng Xu

SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding (2026.acl-demo)

Challenge:	Existing studies in Multimodal Sentiment Analysis lack a mechanism to understand complex relations between different modalities.
Approach:	They propose a hierarchical graph contrastive learning framework for multimodal sentiment analysis that explores the relationships between modality representations.
Outcome:	The proposed framework outperforms the state-of-the-art in multimodal sentiment analysis on two benchmark datasets.

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Runcong Zhao, Qinglin Zhu, Hainiu Xu, Bin Liang, Lin Gui, Yulan He

CLLE: A Benchmark for Continual Language Learning Evaluation in Multilingual Machine Translation (2022.findings-emnlp)

Challenge:	Existing methods for constructing character relationships from plain text are time-consuming and low in coverage.
Approach:	They propose a human-in-the-loop framework that combines LLM-based extraction with symbolic reasoning.
Outcome:	The proposed framework improves annotation accuracy and consistency while significantly reducing time cost.

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Han Zhang, Sheng Zhang, Yang Xiang, Bin Liang, Jinsong Su, Zhongjian Miao, Hui Wang, Ruifeng Xu

Multilinguality

Challenge:	Existing benchmarks for Continual Language Learning (CLL) are limited due to the complexity of the task and the lack of unified benchmarks.
Approach:	They propose a Continual Language Learning Evaluation benchmark CLLE in multilingual translation.
Outcome:	The proposed method is effective when compared with other strong benchmarks.

Stance Detection on Social Media with Background Knowledge (2023.emnlp-main)

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Ang Li, Bin Liang, Jingqian Zhao, Bowen Zhang, Min Yang, Ruifeng Xu

Supervised neural machine translation based on data augmentation and improved training & inference process (D19-52)

Challenge:	Existing studies of stance detection focus on learning stance information about specific targets from context, but in real-world scenarios, we usually have a certain understanding of a target when we express our stance on it.
Approach:	They propose to take the background knowledge of the target into account for better stance detection by categorizing it into episodic and discourse knowledge categories and a heuristic retrieval algorithm based on the topic to retrieve the Wikipedia documents relevant to the sample.
Outcome:	The proposed framework achieves state-of-the-art on four benchmark datasets showing that the proposed framework is able to detect stances in-target and zero-shot scenarios.

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Yixuan Tong, Liang Liang, Boyan Liu, Shanshan Jiang, Bin Dong

Machine Translation

Challenge:	This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019 .
Approach:	They propose a model for translation tasks of WAT 2019 that employs a Transformer model as the baseline and a deep layer model to improve translation quality.
Outcome:	The proposed methods can improve translation quality over traditional statistical machine translation (SMT) The proposed models can improve the translation quality of Japanese-English and Japanese-Chinese corpus.

Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)

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Andrea Sottana, Bin Liang, Kai Zou, Zheng Yuan

Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement.
Approach:	They propose to use both automatic and human evaluation to evaluate generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction.
Outcome:	The proposed model outperforms many popular models according to human reviewers on the majority of metrics, while scoring much worse when using classic automatic evaluation metrics.

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Yice Zhang, Yifan Yang, Meng Li, Bin Liang, Shiwei Chen, Ruifeng Xu

Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network (2022.acl-long)

Challenge:	Aspect Sentiment Triplet Extraction (ASTE) is an important task in sentiment analysis, but data scarcity limits performance of existing methods.
Approach:	They propose a target-to-source augmentation approach to alleviate the issue of data scarcity in Aspect Sentiment Triplet Extraction (ASTE) they use fluency and alignment discriminators to provide feedback and use this feedback to optimize the generator.
Outcome:	The proposed approach significantly improves the performance of existing methods.

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Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, Ruifeng Xu

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning (2025.emnlp-main)

Challenge:	Existing studies on multimodal sarcasm detection using textual and visual information have been limited to text-only approaches.
Approach:	They propose to construct a cross-modal graph for each multi-modal instance to explicitly draw the ironic relations between textual and visual modalities.
Outcome:	The proposed model achieves state-of-the-art in multi-modal sarcasm detection.

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Yiming Du, Yifan Xiang, Bin Liang, Dahua Lin, Kam-Fai Wong, Fei Tan