Papers by Si Zhang

LLM-Rec: Personalized Recommendation via Prompting Large Language Models (2024.findings-naacl)

Challenge:	Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge.
Approach:	They propose a dual-decision retrieval-augmented generation that integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality.
Outcome:	The proposed model outperforms baselines on four medical question-answering datasets while suppressing interference from noisy contexts.

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Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, Qifan Wang, Si Zhang, Ren Chen, Chris Leung, Jiajie Tang, Jiebo Luo

A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents (2021.naacl-main)

Challenge:	Recent advances in large language models (LLMs) have showcased their remarkable ability to harness commonsense knowledge and reasoning.
Approach:	They propose a novel approach which incorporates four distinct prompting strategies of text enrichment for improving personalized text-based recommendations.
Outcome:	The proposed approach improves recommendation quality and even basic MLP models achieve comparable or even better results than complex content-based methods.

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Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang

Syntactic Text Processing

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.

A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks (2026.acl-long)

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Shuzheng Si, Haozhe Zhao, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, Maosong Sun

Green & Sustainable NLP

Challenge:	Recent advances in large language models (LLMs) have leapt from static chatbots to versatile agents that tackle complex tasks such as science experiments.
Approach:	They propose a plan-and-execute framework and propose 'EAGLET' to enhance the executor agent's planning abilities without human effort.
Outcome:	The proposed method outperforms existing methods on three long-horizon tasks and reduces training costs by 8 compared to baselines.

LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)

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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang

Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)

Challenge:	Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios.
Approach:	They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers .
Outcome:	The proposed model outperforms baselines and class transfer models in low-resource scenarios.

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Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

UFO: A UI-Focused Agent for Windows OS Interaction (2025.naacl-long)

Challenge:	Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts.
Approach:	They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features.
Outcome:	The proposed method achieves state-of-the-art on three benchmark datasets.

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Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

Natural Language Interfaces

Challenge:	UFO is a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications . it decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.
Approach:	They propose a UI-Fcused Windows OS agent that decomposes user requests using a divide-and-conquer approach and incorporates a control interaction module tailored for Windows OS.
Outcome:	The proposed agent decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.

FaithLens: Detecting and Explaining Faithfulness Hallucination (2026.findings-acl)

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Shuzheng Si, Qingyi Wang, Haozhe Zhao, Yuzhuo Bai, Guanqiao Chen, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, Maosong Sun

COCO-DR: Combating Distribution Shift in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning (2022.emnlp-main)

Challenge:	Recent progress in large language models (LLMs) has revolutionized text generation.
Approach:	They propose a faithfulness hallucination detection model that can provide binary predictions and corresponding explanations to improve trustworthiness.
Outcome:	The proposed model outperforms advanced models on 12 diverse tasks.

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Yue Yu, Chenyan Xiong, Si Sun, Chao Zhang, Arnold Overwijk

Enhancing Dialogue Generation with Conversational Concept Flows (2023.findings-eacl)

Challenge:	Using COCO-DR, we combat distribution shifts between source training tasks and target scenarios.
Approach:	They propose a method to combat distribution shifts between source training tasks and target scenarios by COtinuous COtrastive learning.
Outcome:	The proposed method outperforms existing models on BEIR and the giant GPT-3 embedding model with 500x more parameters.

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Siheng Li, Wangjie Jiang, Pengda Si, Cheng Yang, Qiu Yao, Jinchao Zhang, Jie Zhou, Yujiu Yang

Dialogue Systems & Conversational Agents

Challenge:	Existing studies show that explicitly modeling concept flows with a large commonsense knowledge graph improves response quality, but there is a gap between the knowledge graph and the conversation.
Approach:	They propose to model human conversational concept flows with a commonsense knowledge graph . they extract abundant concepts and relations from natural conversations and build a conversation-aware knowledge graph.
Outcome:	The proposed method performs better than baselines on a large-scale reddit conversation dataset.

Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation (D19-1)

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Zhuoren Jiang, Zhe Gao, Guoxiu He, Yangyang Kang, Changlong Sun, Qiong Zhang, Luo Si, Xiaozhong Liu

De-Biased Court’s View Generation with Causality (2020.emnlp-main)

Challenge:	Currently, Chinese characters share glyph and phonetic variations to escape detection algorithms due to their complexity and complexity.
Approach:	They propose a Chinese variation-enhanced Graph Embedding algorithm that can learn Chinese character embeddings and latent variation families.
Outcome:	The proposed model outperforms state-of-the-art models on Chinese spam detection datasets and review datasets.

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Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu

Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots (2022.findings-emnlp)

Challenge:	Existing approaches to court’s view generation can be used to address this problem, but neglecting the confounding bias in data can limit the model performance and pollute learning outcomes.
Approach:	They propose a novel Attentional and Counterfactual based Natural Language Generation method consisting of an attentional encoder and a pair of innovative counterfactual decoders to generate judgment-discriminative court's views.
Outcome:	The proposed method is able to generate judgment-discriminative court's views (both supportive and non-supportive views) under both quantitative and qualitative evaluation metrics.

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Haomin Fu, Yeqin Zhang, Haiyang Yu, Jian Sun, Fei Huang, Luo Si, Yongbin Li, Cam Tu Nguyen

Dialogue Systems & Conversational Agents

Challenge:	Documents contain various structures that hinder the ability of machines to comprehend . user information needs are often underspecified, and the nature of heterogeneous documents poses challenges.
Approach:	They propose a dataset for building machines that help users seek information via conversations . their dataset contains over 100,000 turns based on Chinese documents from five domains .
Outcome:	The proposed tasks are challenging and worthy of further research.

OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)

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Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan

Text Generation

Challenge:	Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets.
Approach:	They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models.
Outcome:	The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work.

Looking Beyond Text: Reducing Language Bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance (2025.emnlp-main)

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Haozhe Zhao, Shuzheng Si, Liang Chen, Yichi Zhang, Maosong Sun, Baobao Chang, Minjia Zhang

Visual Data in NLP

Challenge:	Large vision-language models (LVLMs) have been criticized for their language bias.
Approach:	They propose to use a dual-attention mechanism to construct separate attention for visual and text inputs to enhance integration of visual inputs across models.
Outcome:	Experiments show that the proposed model debiases LVLMs from their language bias, enhancing visual comprehension and reducing hallucinations without additional resources.

AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)

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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun

An Empirical Study of Instruction-tuning Large Language Models in Chinese (2023.findings-emnlp)

Challenge:	Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs.
Approach:	They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction.
Outcome:	The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI.

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Qingyi Si, Tong Wang, Zheng Lin, Xu Zhang, Yanan Cao, Weiping Wang

Sub-Character Tokenization for Chinese Pretrained Language Models (2023.tacl-1)

Challenge:	emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage.
Approach:	They propose to use Chinese instruction-tuning LLMs as a cookbook for customizing LLM models that can better respond to Chinese instructions.
Outcome:	The proposed LLM can be used to customize Chinese LLMs that can better respond to Chinese instructions.

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Chenglei Si, Zhengyan Zhang, Yingfa Chen, Fanchao Qi, Xiaozhi Wang, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun

MENTOR: Efficient Autoregressive Image Generation with Balanced Multimodal Control (2026.findings-acl)

Challenge:	Existing tokenization methods for Chinese PLMs treat each character as an indivisible token, but ignore the unique feature of the writing system where additional linguistic information exists below the character level.
Approach:	They propose to encode Chinese characters into short sequences and construct Chinese vocabulary based on the encoded text.
Outcome:	The proposed tokenizers can tokenize inputs into much shorter sequences, improving computational efficiency.

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Haozhe Zhao, Zefan Cai, Shuzheng Si, Liang Chen, Jiuxiang Gu, Wen Xiao, Minjia Zhang, Junjie Hu

READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises (2023.acl-long)

Challenge:	Recent text-to-image models achieve impressive visual quality but still face challenges in precise controllability, balancing multimodal inputs, and high training cost for multimodal image generation.
Approach:	They propose an autoregressive framework with a two-stage training paradigm for controllable multimodal image generation.
Outcome:	Extensive experiments on DreamBench++ and DreamBech show that the proposed framework achieves a strong balance between textual and visual guidance for controllable image generation.

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Chenglei Si, Zhengyan Zhang, Yingfa Chen, Xiaozhi Wang, Zhiyuan Liu, Maosong Sun

Review-based Question Generation with Adaptive Instance Transfer and Augmentation (2020.acl-main)

Challenge:	Existing benchmarks for Chinese inputs often lack a realistic representation of real-world noises.
Approach:	They construct a Chinese multi-task benchmark with REalistic and Diverse input noises . they use pinyin input and speech input to recruit speakers from diverse dialects based on their inputs - a feature that is important for Chinese NLP benchmarks if it is implemented in real-world applications.
Outcome:	The proposed benchmarks are based on four different tasks and are designed to maximize diversity.

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Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si

Text Generation

Challenge:	Existing methods to generate questions for verbose reviews are inefficient for potential consumers . lack of training data hinders efficient review digestion, authors say .
Approach:	They propose to generate questions that can be answered by corresponding review sentences . they propose an iterative learning framework with adaptive instance transfer and augmentation .
Outcome:	The proposed model can generate questions that can be answered by review sentences . it is easier to find critical review parts that are important for potential consumers .

Controllable Text Generation with Residual Memory Transformer (2024.findings-acl)

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Hanqing Zhang, Si Sun, Haiming Wu, Dawei Song

Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information (2024.findings-emnlp)

Challenge:	Large-scale Causal Language Models (CLMs) have been successful in text generation, but there is still a challenge to control the generation process.
Approach:	They propose a non-intrusive, lightweight control plugin to control the generation process of a CLM at arbitrary time steps.
Outcome:	The proposed plugin can handle any type of control conditions and cooperate with the base CLM through a residual learning paradigm.

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Yongheng Zhang, Qiguang Chen, Jingxuan Zhou, Peng Wang, Jiasheng Si, Jin Wang, Wenpeng Lu, Libo Qin

Reasoning

Challenge:	Chain-of-Thought (CoT) is a key technique for enhancing the performance of Large Language Models.
Approach:	They propose a framework that optimizes outputs by utilizing wrong information and multi-perspective verification.
Outcome:	The proposed framework surpasses all baselines on 8 datasets and 5 LLMs.

Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding (N18-2)

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Huasha Zhao, Yi Yang, Qiong Zhang, Luo Si

Plan Dynamically, Express Rhetorically: A Debate-Driven Rhetorical Framework for Argumentative Writing (2025.emnlp-main)

Challenge:	Entity recognition is a widely benchmarked task in natural language processing . a neural architecture called BiLSTM-CRF is used to model the language sequences .
Approach:	They propose a neural architecture called BiLSTM-CRF to model the language sequences.
Outcome:	The proposed system achieves state-of-the-art on English entity recognition task and also in other languages.

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Xueguan Zhao, Wenpeng Lu, Chaoqun Zheng, Weiyu Zhang, Jiasheng Si, Deyu Zhou

Linguistic Theories

Challenge:	Argumentative essay generation (AEG) is a complex task that requires advanced semantic understanding, logical reasoning, and organized integration of perspectives.
Approach:	They propose a debate-driven rhetorical framework for argumentative writing that integrates Bitzer’s rhetorical situation theory to improve logical depth, argumentative diversity, and rhetorical persuasiveness.
Outcome:	The proposed framework improves logical depth, argumentative diversity, and rhetorical persuasiveness over existing state-of-the-art models.

Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering (2023.findings-acl)

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Qianglong Chen, Guohai Xu, Ming Yan, Ji Zhang, Fei Huang, Luo Si, Yin Zhang

Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization (D19-1)

Challenge:	Existing knowledge-enhanced methods have trouble obtaining knowledge from different knowledge bases . a concept-centric model can be used to generate a contrastive explanation for QA tasks .
Approach:	They propose a Concept-centric Prompt-bAsed Contrastive Explanation Generation model which converts obtained symbolic knowledge into the contrastive explanation for better distinguishing the differences among given candidates.
Outcome:	The proposed model achieves new SOTA on CSQA, QASC, and OBQA.

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Yingchi Liu, Quanzhi Li, Marika Cifor, Xiaozhong Liu, Qiong Zhang, Luo Si

A Unified Syntax-aware Framework for Semantic Role Labeling (D18-1)

Challenge:	Sexual harassment is a pervasive, worldwide problem with a long history . statistics show that girls and women are put at high risk of experiencing harassment.
Approach:	They manually annotated sexual harassment stories with labels in dimensions of location, time, and harassers’ characteristics and applied natural language processing techniques to extract key elements at the same time.
Outcome:	The proposed algorithms will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, and enabling faster action to be taken.

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Zuchao Li, Shexia He, Jiaxun Cai, Zhuosheng Zhang, Hai Zhao, Gongshen Liu, Linlin Li, Luo Si

Beyond Static Artifacts: An Evolutionary Framework for Synthetic Claim Generation (2026.acl-long)

Challenge:	Syntactic information has been paid a great attention over the role of enhancing SRL . but the gap between syntax-aware and syntax-gnostic SRL is smaller . a new framework proposes syntax-based SRL for a wide range of NLP tasks .
Approach:	They propose to extend existing models to investigate more effective ways of incorporating syntax into sequential neural networks.
Outcome:	The proposed framework outperforms existing models on CoNLL-2009 benchmarks in English and Chinese.

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Yeqing Teng, Jiasheng Si, Shuxia Lin, Linhai Zhang, Weiyu Zhang, Wenpeng Lu, Deyu Zhou, Xiaoming Wu

Challenge:	Existing claim detection benchmarks treat claims as static textual artifacts . current research ignores sociological etiology of how information naturally emerges and mutates .
Approach:	They propose a socially generative framework for synthetic claim generation . they propose utterance, proposition and context-based simulations to capture truth decay .
Outcome:	The proposed paradigm models claims as socially evolving entities . it allows precise simulation of truth decay and intervened propagation with multi-auditor oversight .

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)

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Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si

TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)

Challenge:	Argument mining (AM) is a computational process that is used to analyze information in a debating system.
Approach:	They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks .
Outcome:	The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks .

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Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun

Robustness in NLP

Challenge:	Existing approaches to safety alignment of large language models rely on costly manual annotations or human review.
Approach:	They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation.
Outcome:	The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability.

One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)

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Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li

How to Make LMs Strong Node Classifiers? (2026.findings-eacl)

Challenge:	Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality.
Approach:	They propose a method that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets.
Outcome:	Nuggets outperforms existing methods on MT-Bench and Alpaca-Eval benchmarks.

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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong

Aspect Sentiment Classification with Document-level Sentiment Preference Modeling (2020.acl-main)

Challenge:	Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs).
Approach:	They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications.
Outcome:	The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication.

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Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang, Guodong Zhou

Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision (2021.acl-long)

Challenge:	Existing studies consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect.
Approach:	They propose a Cooperative Graph Attention Networks approach for cooperatively learning aspect-related sentence representation.
Outcome:	The proposed approach outperforms the state-of-the-art methods in document-level sentiment classification.

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Si Sun, Yingzhuo Qian, Zhenghao Liu, Chenyan Xiong, Kaitao Zhang, Jie Bao, Zhiyuan Liu, Paul Bennett

MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning (2025.findings-acl)

Challenge:	Neural information retrieval models have shown advanced results in many ranking scenarios where massive relevance labels or clickthrough data are available.
Approach:	They propose a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains.
Outcome:	The proposed method improves the few-shot ranking accuracy of Neu-IR models on three TREC benchmarks in the web, news, and biomedical domains.

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Dacao Zhang, Kun Zhang, Shimao Chu, Le Wu, Xin Li, Si Wei

Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2024.findings-acl)

Challenge:	Recent advances in Large Language Models (LLMs) have revolutionized various domains, offering unprecedented performance across numerous tasks.
Approach:	They propose a new Mixture of Low-Rank Experts (MoRE) for multi-task PEFT to improve performance of LLMs with fewer parameters.
Outcome:	The proposed method improves performance over multiple tasks and no additional inference cost.

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Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang

Challenge:	Recent advances in Large Language Models (LLMs) have greatly advanced problem solving in diverse domains such as mathematical reasoning and knowledge reasoning.
Approach:	They propose a thought prompting approach called 'Everything of Thoughts' it leverages pretrained reinforcement learning and Monte Carlo Tree Search to incorporate external domain knowledge and planning capability into thoughts.
Outcome:	The proposed approach outperforms existing approaches on game of 24, 8-Puzzle, and Pocket Cube.

Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios (2023.emnlp-industry)

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Yilun Zhao, Haowei Zhang, Shengyun Si, Linyong Nan, Xiangru Tang, Arman Cohan

Text Generation

Challenge:	Existing table-to-text generation techniques that transform complex tabular data into comprehensible narratives are lacking in real-world applications.
Approach:	They investigate the table-to-text capabilities of different LLMs using four datasets within two real-world information seeking scenarios.
Outcome:	The proposed models can generate table-to-text data in two real-world information seeking scenarios and perform better than existing models.

Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue (2020.findings-emnlp)

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WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, Luo Si

Dialogue Systems & Conversational Agents

Challenge:	Existing research on customer service dialogue generation generates generic responses from sellers . however, such cost prohibits small businesses, and multiturn dialogue generation is becoming more popular.
Approach:	They propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information to generate generic seller responses.
Outcome:	The proposed model can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.

One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (C18-1)

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Lu Wang, Shoushan Li, Changlong Sun, Luo Si, Xiaozhong Liu, Min Zhang, Guodong Zhou

Question Answering

Challenge:	Existing studies on question answer matching focus on formal text . however, there exists many scenarios where the QA text is informal .
Approach:	They propose a novel QA matching approach using informal text from a product review site.
Outcome:	The proposed approach improves word-level and sentence-level attentions for solving the noisy problem in the informal text.

Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)

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Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

Sentiment Analysis

Challenge:	Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus .
Approach:	They propose a task to predict aspects from interactive QA style reviews using an annotated corpus.
Outcome:	The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data.

A Simple Concatenation can Effectively Improve Speech Translation (2023.acl-short)

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Linlin Zhang, Kai Fan, Boxing Chen, Luo Si

Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs (2026.acl-long)

Challenge:	Experimental results show that in our unified cross-modal ST model, models can effectively utilize the auxiliary information from speech and text.
Approach:	They propose a unified cross-modal ST method which concatenates speech and text as the input and builds a teacher that can utilize both cross-modities simultaneously.
Outcome:	The proposed method can effectively utilize the auxiliary information from speech and text, and achieve compelling results on MuST-C datasets.

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Wai Man Si, Mingjie Li, Michael Backes, Yang Zhang

FCGCL: Fine- and Coarse-Granularity Contrastive Learning for Speech Translation (2022.findings-emnlp)

Challenge:	Empirical evaluations on ML models show substantial reductions in unsafe generations and improved robustness against jailbreak attacks.
Approach:	They propose a resource-efficient pruning framework that directly identifies unsafe behaviors while preserving model utility.
Outcome:	The proposed framework reduces unsafe generations and improves robustness against jailbreak attacks with minimal utility loss.

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Hao Zhang, Nianwen Si, Yaqi Chen, Zhen Li, Tong Niu, Xukui Yang, Dan Qu

Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective (2022.emnlp-main)

Challenge:	Existing methods to perform implicit knowledge transfer from machine translation to ST model are difficult because of the task complexity and data scarcity.
Approach:	They recommend a method which conducts explicit knowledge transfer from MT to ST model by fine and coarse granularity contrastive learning.
Outcome:	The proposed method improves the performance of the end-to-end speech translation model on all 8 languages.

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Baijun Ji, Tong Zhang, Yicheng Zou, Bojie Hu, Si Shen

Supervised Treebank Conversion: Data and Approaches (P18-1)

Challenge:	Existing studies focus on extracting multi-granularity visual features for integration or designing model architectures for better message passing across various modalities.
Approach:	They propose to decompose the informative visual signals into two parts: source-specific information and target-specific info.
Outcome:	The proposed method can enhance the visual awareness of MMT models against strong baselines.

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Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si

Syntactic Text Processing

Challenge:	Existing work on treebank conversion focuses on unsupervised treebanks . lack of manually labeled data means that sentences have two syntactic trees at the same time.
Approach:	They propose supervised treebank conversion using bi-tree aligned sentences . they propose two conversion approaches based on state-of-the-art deep biaffine parser .
Outcome:	The proposed method outperforms the state-of-the-art deep biaffine parser on the English WSJ dataset by 0.97 (93.76% -92.79%)

Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention (2026.findings-acl)

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Mengqi Liao, Lu Wang, Chaoyun Zhang, Bo Qiao, Si Qin, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Huaiyu Wan

Reasoning

Challenge:	Existing methods to evict KV cache during inference phase are impractical for industrial-grade applications.
Approach:	They propose a method that combines token-wise KV cache eviction with PagedAttention and propose 'zipage' it achieves 95% of the performance of Full KV inference engines while delivering over 2.1 speedup .
Outcome:	The proposed method achieves 95% of the performance of Full KV inference engines while delivering over 2.1 speedup on large-scale mathematical reasoning tasks.

Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning (P19-1)

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Quanzhi Li, Qiong Zhang, Luo Si

Towards Generalizable and Robust Text-to-SQL Parsing (2022.findings-emnlp)

Challenge:	Social media platforms do not always pose authentic information, and rumors spread fear or hate.
Approach:	They propose a new multi-task learning approach for rumor detection and stance classification tasks.
Outcome:	The proposed model outperforms the state-of-the-art rumor detection approaches on two datasets.

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Chang Gao, Bowen Li, Wenxuan Zhang, Wai Lam, Binhua Li, Fei Huang, Luo Si, Yongbin Li

Programming Languages in NLP

Challenge:	Text-to-SQL parsers must be generalizable and robust against input perturbations.
Approach:	They propose a novel framework to learn text-to-SQL parsing in stages to improve parser's ability to acquire general SQL knowledge instead of capturing spurious patterns.
Outcome:	The proposed framework achieves state-of-the-art performance on the Spider, SParC, and CoSQL datasets.

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

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

Machine Translation

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

Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information (2024.findings-emnlp)

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Guobiao Zhang, Xueping Peng, Tao Shen, Guodong Long, Jiasheng Si, Libo Qin, Wenpeng Lu

Challenge:	Existing methods focus on local optimal while ignoring sole-mention disambiguation boosted by richer context from other mentions’ disambiguating processes.
Approach:	They propose an approach to extracting medical entity disambiguation using memory mechanism and memorized entity information (M3E) they use a memory mechanism module that performs memory caching, retrieval, fusion and cross-network residual to aid the disambiguations of remaining mentions.
Outcome:	The proposed method outperforms state-of-the-art methods on two benchmark datasets.

Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)

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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

WSDPO: A Generative Word Sense Disambiguation Framework with Chain-of-Thought and Preference Optimization (2026.acl-long)

Challenge:	Existing methods to classify QA text contain rich sentiment information.
Approach:	They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines.
Outcome:	The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit.

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Kunpeng Kang, Shuaimin Li, Kaiyuan Zhang, Luyang Zhang, Jiasheng Si, Bing Xu, Kehai Chen, Muyun Yang, Wenpeng Lu

DPGA-TextSyn: Differentially Private Genetic Algorithm for Synthetic Text Generation (2025.findings-acl)

Challenge:	Word sense disambiguation (WSD) is a fundamental task in natural language processing.
Approach:	They propose a training framework for generative WSD with chain-of-thought (CoT) and preference optimization.
Outcome:	The proposed framework achieves significant performance gains on rare and unseen settings and exhibits strong generalization in standard evaluation settings.

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Zhonghao Sun, Zhiliang Tian, Yiping Song, Yuyi Si, Juhua Zhang, Minlie Huang, Kai Lu, Zeyu Xiong, Xinwang Liu, Dongsheng Li

Ethical NLP

Challenge:	Existing methods to fine-tune large language models pose privacy risks . researchers have synthesized data with strong generation capabilities closed-source LLMs to alleviate this problem .
Approach:	They propose to combine general LLMs with genetic algorithm to produce relevant and diverse synthetic text under differential privacy constraints.
Outcome:	The proposed method significantly improves the performance of the model in downstream tasks while ensuring privacy.

Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning (2020.findings-emnlp)

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Rui Wang, Shijing Si, Guoyin Wang, Lei Zhang, Lawrence Carin, Ricardo Henao

Rumor Detection on Social Media: Datasets, Methods and Opportunities (D19-50)

Challenge:	Existing pretrained language models are agnostic to downstream information and can overfit when fine-tuned with low resource datasets.
Approach:	They integrate label information as a task-specific prior into the self-attention component of pretrained BERT models.
Outcome:	Experiments on benchmarks and real-word datasets show that the proposed approach can improve the performance of pretrained models when fine-tuned with small datasets.

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Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu

A Neural Multi-digraph Model for Chinese NER with Gazetteers (P19-1)

Challenge:	Social media platforms are used for information gathering, but they also lead to the spreading of rumors and fake news.
Approach:	This paper presents a comprehensive list of datasets used for rumor detection . it also reviews the important studies based on what types of information they exploit .
Outcome:	This paper presents an overview of the recent studies in the rumor detection field . it provides a comprehensive list of datasets used for rumour detection .

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Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)

Challenge:	Existing approaches to incorporating gazetteers into NER systems rely on manually defined selection strategies or handcrafted templates, which may not lead to optimal effectiveness.
Approach:	They propose to use graph neural networks to automatically learn how to incorporate multiple gazetteers into an NER system by capturing the information that the gazetteer offers.
Outcome:	The proposed model outperforms existing methods on Chinese NER datasets while incorporating rich gazetteer information while resolving ambiguities.

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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

Visual Data in NLP

Challenge:	Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment.
Approach:	They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives.
Outcome:	The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering.

ReContraster: Making Your Posters Stand Out with Regional Contrast (2026.acl-long)

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Peixuan Zhang, Zijian Jia, Ziqi Cai, Shuchen Weng, Si Li, Boxin Shi

Cognitive Modeling

Challenge:	Effective poster design requires rapidly capturing attention and clearly conveying messages.
Approach:	They propose a poster-based model that leverages regional contrast to make posters stand out.
Outcome:	The proposed model outperforms state-of-the-art methods in producing striking posters.

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning (D19-1)

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Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou

Reward Yourself: Efficient Self Rewards for Trustworthy Sampling (2026.findings-acl)

Challenge:	Recent neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC) however, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability.
Approach:	They propose a Hierarchical Reinforcement Learning approach to DASC that incorporates clause selection and word selection strategies to tackle the data noise problem.
Outcome:	The proposed approach over the state-of-the-art approaches shows impressive performance over the current baselines.

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Mingjie Li, Wai Man Si, Michael Backes, Yang Zhang

Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning (2026.acl-long)

Challenge:	Retraining reward models to address privacy leaks and stereotypes is expensive . recent advances in large language models have led to improvements in understanding .
Approach:	They propose a lightweight intrinsic reward that can be used to prune existing LLMs to approximate an "untrust" and an ""untrust "" token distribution.
Outcome:	Experiments with two reward models and four LLMs show that selfRW improves trustworthiness with minimal impact on general utility benchmarks.

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Ziqing Zhuang, Linhai Zhang, Jiasheng Si, Deyu Zhou, Yulan He

Reasoning

Challenge:	Existing approaches for improving LLM reasoning remain episodic and lack reusable meta-reasoning skills.
Approach:	They propose a framework that consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.
Outcome:	The proposed framework consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.

Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning (2021.findings-acl)

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Chenglei Si, Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun

Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering (2025.naacl-long)

Challenge:	Pretrained language models perform poorly under adversarial attacks due to the large search space.
Approach:	They propose a method to cover a much larger proportion of the attack search space by adding textual adversarial examples during training.
Outcome:	The proposed method covers a much larger proportion of the attack search space.

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Chenglei Si, Yanzhe Zhang, Ryan Li, Zhengyuan Yang, Ruibo Liu, Diyi Yang

ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)

Challenge:	Generative AI has made rapid advances in multimodal understanding and code generation.
Approach:	They construct a first real-world benchmark for multimodal large language models that directly convert visual designs into code implementations by manually curating 484 diverse real-life webpages as test cases.
Outcome:	The proposed model can generate code implementations that directly render into the given reference webpages, given the screenshots as input.

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Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, Luo Si

Structured Data in NLP

Challenge:	Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description.
Approach:	They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information.
Outcome:	The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information.

Syntax-Enhanced Self-Attention-Based Semantic Role Labeling (D19-1)

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Yue Zhang, Rui Wang, Luo Si

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models (2022.emnlp-main)

Challenge:	Abstract: Syntax is the bridge to semantics, but recent studies have discussed the necessity of syntax in the context of SRL.
Approach:	They propose a syntax-enhanced self-attention model that incorporates syntactic knowledge into the SRL task effectively.
Outcome:	The proposed model achieves state-of-the-art for the Chinese SRL task on the CoNLL-2009 dataset.

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Weiyan Shi, Ryan Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu

Semi-supervised Domain Adaptation for Dependency Parsing (P19-1)

Challenge:	Existing approaches to protect language models from privacy leakage suffer from limited user control and low utility . et al., 2018: a novel framework that achieves SDP for state-of-the-art large transformer-based models.
Approach:	They propose a framework that applies differential privacy to large language models . they use redacted in-domain data to fine-tune the model with original in- domain data .
Outcome:	The proposed framework achieves strong utility compared to baselines.

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Zhenghua Li, Xue Peng, Min Zhang, Rui Wang, Luo Si