Papers by Si Li

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

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

Low-Resource NLP

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.

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

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

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

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

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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.

A Prism Module for Semantic Disentanglement in Name Entity Recognition (P19-1)

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Kun Liu, Shen Li, Daqi Zheng, Zhengdong Lu, Sheng Gao, Si Li

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

Challenge:	Xu et al., 2015) proposed a noise reduction mechanism to disentangle semantics of words . hard and soft attention mechanisms are used to reduce noise in NLP tasks .
Approach:	They propose a prism module to disentangle semantic aspects of words and reduce noise . they propose combining prism modules with downstream models to improve model performance .
Outcome:	The proposed method significantly improves the performance of baselines on named entity recognition (NER) tasks.

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

Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification (2021.acl-long)

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.

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Jiasheng Si, Deyu Zhou, Tongzhe Li, Xingyu Shi, Yulan He

Reasoning

Challenge:	Existing methods for fact verification focus on analyzing semantic interaction between claim and evidence but fail to capture their topical consistency . Existing models focus on the aggregation of multiple pieces of evidence without considering their implicit stances to the claim, thereby introducing spurious information.
Approach:	They propose a topic-aware evidence reasoning and stance-again aggregation model that checks topical consistency between claims and evidence.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets.

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

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

Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network (N18-2)

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.

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Chenliang Li, Weiran Xu, Si Li, Sheng Gao

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)

Challenge:	Abstractive text summarization models are hard to be controlled in the process of generation, which leads to a lack of key information.
Approach:	They propose a guiding generation model that combines extractive and abstractive methods to generate text summarization.
Outcome:	The proposed model improves on the CNN/Daily Mail dataset.

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Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

Natural Language Interfaces

Challenge:	Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models.
Approach:	They propose a self-supervised pre-training scheme that pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus for generating new text conditioned on context.
Outcome:	The proposed scheme achieves state-of-the-art results on a variety of language generation benchmarks covering generative question answering, abstractive summarization and conversational response generation.

Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking (2021.acl-short)

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Yinpei Dai, Hangyu Li, Yongbin Li, Jian Sun, Fei Huang, Luo Si, Xiaodan Zhu

Document Segmentation Matters for Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Existing dialog state tracking models neglect rich structural information in a dataset.
Approach:	They propose to use curriculum learning to leverage dialog state tracking data . they propose a model-agnostic framework that pre-trains a DST model with schema information .
Outcome:	The proposed framework improves performance over a transformer-based and RNN-based model on WOZ2.0 and MultiWOZ2.1.

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Zhitong Wang, Cheng Gao, Chaojun Xiao, Yufei Huang, Shuzheng Si, Kangyang Luo, Yuzhuo Bai, Wenhao Li, Tangjian Duan, Chuancheng Lv, Guoshan Lu, Gang Chen, Fanchao Qi, Maosong Sun

Leveraging Generative Large Language Models with Visual Instruction and Demonstration Retrieval for Multimodal Sarcasm Detection (2024.naacl-long)

Challenge:	Existing rule-based chunking methods lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence.
Approach:	They propose a method that leverages document summaries as pseudo-instructions to guide chunking by computing semantic similarity between sentences and the summary.
Outcome:	Experiments on multiple open-domain question-answering benchmarks show that PIC significantly improves retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training.

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Binghao Tang, Boda Lin, Haolong Yan, Si Li

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (2022.findings-emnlp)

Challenge:	Existing methods for multimodal sarcasm detection do not fully utilize cross-modal features, limiting their performance on in-domain datasets.
Approach:	They propose a multimodal sarcasm detection model with a designed instruction template and a demonstration retrieval module.
Outcome:	The proposed model outperforms existing methods on in-domain datasets and achieves state-of-the-art performance.

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Zefeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li

Programming Languages in NLP

Challenge:	Extensive experiments show that STAR outperforms previous pre-training methods and ranks first on the leaderboard . text-to-SQL parsing aims to translate natural language (NL) questions into executable SQL queries .
Approach:	They propose a SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing . they propose two objectives that explore context-dependence of NL utterances and SQL queries .
Outcome:	The proposed framework outperforms existing methods on two downstream benchmarks and ranks first on the leaderboard.

ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)

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Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Multilinguality

Challenge:	Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency.
Approach:	They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets.
Outcome:	The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets.

MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)

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Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing (2021.findings-emnlp)

Challenge:	Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance.
Approach:	They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities.
Outcome:	The proposed framework outperforms baseline methods on low-resource tasks.

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Boda Lin, Mingzheng Li, Si Li, Yong Luo

Low-Resource NLP

Challenge:	Existing methods for dependency parsing are often of the pseudo-annotation type, but they fail to consider the change of model structure for domain adaptation.
Approach:	They propose a method that accomplishes unsupervised cross-domain dependency parsing without using labeled data.
Outcome:	The proposed method achieves consistent performance improvement on CODT1 and CTB9 domains.

MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints (2026.findings-acl)

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Kangyang Luo, Shuzheng Si, Yuzhuo Bai, Cheng Gao, Zhitong Wang, Cheng Huang, Yingli Shen, Yufeng Han, Wenhao Li, Cunliang Kong, Maosong Sun

MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning (2026.acl-long)

Challenge:	Existing supervised neural methods are underexplored for coreference resolution, especially in incremental clustering.
Approach:	They propose a dual-threshold incremental clustering approach based on a lightweight Transformer.
Outcome:	Experiments on common benchmarks show that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints.

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Weikang Shi, Aldrich Yu, Rongyao Fang, Houxing Ren, Ke Wang, Aojun Zhou, Changyao Tian, Xinyu Fu, Yuxuan Hu, Zimu Lu, Linjiang Huang, Si Liu, Rui Liu, Hongsheng Li

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

Challenge:	Existing approaches to visual chain-of-thought are limited by external tools or fail to generate high-fidelity diagrams.
Approach:	They propose a framework to enable large multimodal models with VCoT capabilities . they pre-train a model on a 15.2M-pair corpus and teach it how to leverage visual aids .
Outcome:	The proposed framework unlocks complex, human-like visual reasoning in large language models . it pre-trains the model on a 15.2M-pair corpus and fine-tunes it on MathCanvas-Instruct .

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

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

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

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

Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations (2023.acl-long)

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

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Linlin Liu, Xingxuan Li, Megh Thakkar, Xin Li, Shafiq Joty, Luo Si, Lidong Bing

SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2022.coling-1)

Challenge:	Using hidden representations, pretrained language models are prone to overfitting due to the huge amount of parameters.
Approach:	They propose a method that inserts random autoencoders between hidden layers of a PLM to transform activations from the previous layers into multi-view compressed representations before feeding them into the upper layers.
Outcome:	The proposed method improves performance across sequence- and token-level lowresource tasks.

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Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li

Programming Languages in NLP

Challenge:	Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping .
Approach:	They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations.
Outcome:	The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets.

APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts (2025.acl-long)

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Honghua Dong, Qidong Su, Yubo Gao, Zhaoyu Li, Yangjun Ruan, Gennady Pekhimenko, Chris J. Maddison, Xujie Si

AdaReTaKe: Adaptive Redundancy Reduction to Perceive Longer for Video-language Understanding (2025.findings-acl)

Challenge:	Large Language Models (LLMs) are capable of handling diverse tasks with well-crafted prompts and integration of external tools.
Approach:	They propose a prompt programming language that acts as a bridge between computer programs and LLMs by providing convenient conventions between them.
Outcome:	The proposed language is intuitive, concise, and efficient through representative scenarios including Chain-of-Thought with self-consistency (CoT-SC) and ReAct tool-use agent.

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Xiao Wang, Qingyi Si, Shiyu Zhu, Jianlong Wu, Li Cao, Liqiang Nie

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

Challenge:	Multimodal Large Language Models (MLLMs) are limited by context length when processing long videos.
Approach:	They propose a training-free method that flexibly reduces redundancy by allocating compression ratios among time and model layers with theoretical guarantees.
Outcome:	Experiments on videoMME, MLVU, LongVideoBench, and LVBench show that AdaRETAKE outperforms existing methods by 2.3% and 2.8% for 7B and 72B models.

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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

Visual Enhanced Entity-Level Interaction Network for Multimodal Summarization (2024.findings-naacl)

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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Haolong Yan, Binghao Tang, Boda Lin, Gang Zhao, Si Li

GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)

Challenge:	Existing methods to generate concise summarizations rely on coarse-grained textual and visual information, but they are underutilized.
Approach:	They propose a Visual Enhanced Entity-Level Interaction Network to address underutilization of multimodal inputs at a fine-grained level.
Outcome:	The proposed model outperforms existing models on two MMS datasets and proposes new metrics to measure factual consistency of entities in the output.

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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun

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

Challenge:	Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically.
Approach:	They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance.
Outcome:	The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs.

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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

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

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

Information Retrieval

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.

Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition (2022.findings-emnlp)

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Gang Zhao, Guanting Dong, Yidong Shi, Haolong Yan, Weiran Xu, Si Li

ImCoref-CeS: An Improved Lightweight Pipeline for Coreference Resolution with LLM-based Checker-Splitter Refinement (2026.acl-long)

Challenge:	Existing methods for name-based entity recognition neglect the integrity of entity semantics and conduct cross-modal interaction at token-level.
Approach:	They propose a multimodal named entity recognition model that captures visual information and fuses it into tokens to rid non-entity tokens of visual noise.
Outcome:	The proposed model captures entity-related visual information and fuses it into tokens . it eliminates visual noise and makes non-entity tokens easily misidentified as entities .

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Kangyang Luo, Yuzhuo Bai, Shuzheng Si, Cheng Gao, Zhitong Wang, Yingli Shen, Wenhao Li, Zhu Liu, Yufeng Han, Jiayi Wu, Cunliang Kong, Maosong Sun

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

Challenge:	Existing supervised neural methods for coreference resolution are underexplored . current methods rely on small language models, but their potential is underexploited .
Approach:	They propose a framework that integrates an enhanced supervised model with LLM-based reasoning.
Outcome:	The proposed method surpasses existing state-of-the-art methods in coreference resolution.

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

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

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

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

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

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

UltraIF: Advancing Instruction Following from the Wild (2025.emnlp-main)

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.

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Kaikai An, Li Sheng, Ganqu Cui, Shuzheng Si, Ning Ding, Yu Cheng, Baobao Chang

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

Challenge:	a lack of transparency has resulted in a gap between research community and leading companies . large language models have demonstrated remarkable capabilities in following complex instructions .
Approach:	They propose a method to build large language models that can follow complex instructions with open-source data.
Outcome:	The proposed approach can synergize complex instructions and filter responses with evaluation questions.

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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.

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

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

DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction (2023.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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Gang Zhao, Xiaocheng Gong, Xinjie Yang, Guanting Dong, Shudong Lu, Si Li

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

Challenge:	Current Event Extraction methods focus on high-resource scenarios, which requires large amount of annotated data.
Approach:	They propose a demonstration-based learning paradigm for EE to fully use annotated data . they propose EE as a natural language generation task guided by schema-based prompts .
Outcome:	The proposed model outperforms current methods in low-resource scenarios.

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

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

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

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

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%)

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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.

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

Information Retrieval

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.

Estimating Soft Labels for Out-of-Domain Intent Detection (2022.emnlp-main)

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Hao Lang, Yinhe Zheng, Jian Sun, Fei Huang, Luo Si, Yongbin Li

Sentiment Analysis

Challenge:	Existing methods to detect out-of-dominance (OOD) intents are limited by the lack of OOD samples.
Approach:	They propose an adaptive soft pseudo labeling method that can estimate soft labels for pseudo OOD samples when training OOD detectors.
Outcome:	The proposed method outperforms competing methods on three benchmark datasets and consistently outperformed previous methods.

SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding (2022.coling-1)

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Wanwei He, Yinpei Dai, Binyuan Hui, Min Yang, Zheng Cao, Jianbo Dong, Fei Huang, Luo Si, Yongbin Li

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

Challenge:	Existing methods for dialog understanding only consider self-augmented dialogs as positive samples and treat all other dialogs like negative ones.
Approach:	They propose a tree-structured pre-trained conversation model which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training.
Outcome:	The proposed model can achieve state-of-the-art results on the DialoGLUE benchmark.

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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.

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

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Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu

Dependency Parsing via Sequence Generation (2022.findings-emnlp)

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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Boda Lin, Zijun Yao, Jiaxin Shi, Shulin Cao, Binghao Tang, Si Li, Yong Luo, Juanzi Li, Lei Hou

StructuralLM: Structural Pre-training for Form Understanding (2021.acl-long)

Challenge:	Existing methods for dependency parsing are transition-based, graph-based and sequence-to-sequence method.
Approach:	They propose to achieve dependency parsing (DP) via Sequence Generation (SG) by utilizing only the pre-trained language model without any auxiliary structures.
Outcome:	The proposed method performs well on DP benchmarks including PTB, UD2.2, SDP15 and SemEval16.

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Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

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

Challenge:	Existing pre-trained language models focus on text-only representation, neglecting cell-level layout information.
Approach:	They propose a pre-training approach to leverage cell and layout information from scanned documents.
Outcome:	The proposed model achieves state-of-the-art in various downstream tasks . it uses 2Dposition embeddings to model word-level layout information .

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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.

ViPE: Visual Perception in Parameter Space for Efficient Video-Language Understanding (2025.emnlp-main)

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Shichen Lu, Tongtian Yue, Longteng Guo, Handong Li, Xingjian He, Si Liu, Jing Liu

Visual Data in NLP

Challenge:	Existing video-language models rely on concatenating visual tokens with textual inputs for joint modeling, but this method suffers from significant inefficiency when scaling to long videos with dense visual inputs.
Approach:	They propose a video-to-parameter efficiency paradigm called ViPE that transforms video content into visual perceptual weights, which are directly injected into the LLM’s parameters.
Outcome:	The proposed model reduces FLOPs by 85% and inference time by up to 65% while reducing FLOP and FLOP inference times by up-to-65%.

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.

GATEAU: Selecting Influential Samples for Long Context Alignment (2025.emnlp-main)

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Shuzheng Si, Haozhe Zhao, Gang Chen, Yunshui Li, Kangyang Luo, Chuancheng Lv, Kaikai An, Fanchao Qi, Baobao Chang, Maosong Sun

Challenge:	Existing studies have attempted to scale up the available data volume by synthesizing long instruction-following samples, but a lack of a well-defined strategy for ensuring data quality may introduce low-quality samples and restrict the model’s performance.
Approach:	They propose a framework to identify influential samples enriched with long-range dependency relations that can be used to align large language models to handle instructions with extremely long contexts.
Outcome:	The proposed framework identifies samples with long-range dependency relations and shows that the model trained on these samples exhibits better instruction-following and long-context understanding capabilities.

Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning (2026.acl-long)

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Ziheng Li, Liu Kang, Feng Xiao, Luxi Xing, Qingyi Si, Zhuoran Li, Weikang Gong, Deqing Yang, Yanghua Xiao, Hongcheng Guo

Reasoning

Challenge:	Group Relative Policy Optimization (GRPO) uses a coarse-grained credit assignment mechanism that propagates group-level rewards uniformly to to every token in a sequence, neglecting the varying contribution of individual reasoning steps.
Approach:	They introduce Outcome-grounded Advantage Reshaping (OAR) which redistributes advantages based on how much each token influences the model’s final answer.
Outcome:	Empirical results show that OAR-G outperforms GRPO on a high-fidelity attribution signal and suppresses low-impact tokens while preserving the advantage mass.

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2026.acl-long)

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Keyu Li, Junhao Shi, Yang Xiao, Mohan Jiang, Jie Sun, Yunze Wu, Dayuan Fu, Shijie Xia, Xiaojie Cai, Tianze Xu, Weiye Si, Wenjie Li, Dequan Wang, Pengfei Liu

Challenge:	Existing benchmarks focus on single agentic capability, failing to capture long-horizon real-world scenarios.
Approach:	They propose a benchmark that evaluates 6 agentic capabilities across 32 real-world scenarios.
Outcome:	Experiments show that closed-source models outperform open-source model (48.4% vs 32.1%) integrating models with advanced scaffolds to form autonomous agents is a paradigm shift.

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

UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt (2024.lrec-main)

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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Yucheng Cai, Wentao Ma, Yuchuan Wu, Shuzheng Si, Yuan Shao, Zhijian Ou, Yongbin Li

Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples (2022.emnlp-main)

Challenge:	Recent studies have shown that multi-task instruction tuning after pre-training greatly improves the model’s robustness and transfer ability, which is crucial for building a high-quality dialog system.
Approach:	They propose to use Task-aware Automatic Prompt generation (TAP) to automatically generate high-quality prompts from 15 dialog-related tasks.
Outcome:	The proposed model is robust to input prompts and capable of various dialog-related tasks.

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Linlin Liu, Xin Li, Ruidan He, Lidong Bing, Shafiq Joty, Luo Si

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

Challenge:	Existing methods for language model pretraining use limited knowledge graph data for knowledge-intensive tasks.
Approach:	They propose to make better use of multilingual annotations and language agnostic properties of KG triples for pretraining LMs.
Outcome:	The proposed models show significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks.

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

Leveraging BERT and TFIDF Features for Short Text Clustering via Alignment-Promoting Co-Training (2024.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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Zetong Li, Qinliang Su, Shijing Si, Jianxing Yu