Papers by Zhongyu Wei

RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (2023.acl-long)

Challenge:	Recent studies have focused on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards.
Approach:	They propose a hybrid alignment framework HAF-RM that incorporates additional constraint on token-level policy probabilities in addition to the reward score.
Outcome:	The proposed framework can supervise the internal preference model at the token level and optimize the mapping layer of the reward model at sequence level.

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Jun Zhao, WenYu Zhan, Xin Zhao, Qi Zhang, Tao Gui, Zhongyu Wei, Junzhe Wang, Minlong Peng, Mingming Sun

Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation (2023.emnlp-main)

Challenge:	Existing methods for zero-shot relation extraction lack explicit modeling of matching pattern . et al. (2018) show that our method achieves higher matching accuracy and faster inference speed .
Approach:	They propose a fine-grained semantic matching method tailored for zero-shot relation extraction . they decompose sentence-level similarity score into entity matching score and context matching score .
Outcome:	The proposed method achieves higher matching accuracy and faster inference speed than state-of-the-art methods.

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Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei

Text Generation

Challenge:	Existing work describes paragraph-level counter-argument generation task as paragraph-based . however, sentence-level generation can be quite different due to its unique constraints and brevity-focused challenges.
Approach:	They propose a benchmark framework for sentence-level counter-argument generation . they use an annotated debate forum dataset to generate high-quality counter-argments .
Outcome:	The proposed framework and evaluator are competitive in counter-argument generation tasks.

ALaRM: Align Language Models via Hierarchical Rewards Modeling (2024.findings-acl)

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Yuhang Lai, Siyuan Wang, Shujun Liu, Xuanjing Huang, Zhongyu Wei

A Lexicon-Based Graph Neural Network for Chinese NER (D19-1)

Challenge:	Current alignment approaches struggle with inconsistency and sparsity of human supervision signals.
Approach:	They propose a framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF) it integrates holistic rewards with aspect-specific rewards to enhance alignment of large language models with human preferences.
Outcome:	The proposed framework improves the alignment of large language models with human preferences by integrating holistic rewards with aspect-specific rewards.

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Tao Gui, Yicheng Zou, Qi Zhang, Minlong Peng, Jinlan Fu, Zhongyu Wei, Xuanjing Huang

MERMAID: Multi-perspective Self-reflective Agents with Generative Augmentation for Emotion Recognition (2025.emnlp-main)

Challenge:	Chinese named entity recognition models are vulnerable to word ambiguities due to the lack of global semantics and chain structure.
Approach:	They propose a lexicon-based graph neural network with global semantics to solve word ambiguities in Chinese named entity recognition (NER) Lexicons are used to construct the graph and provide word-level features.
Outcome:	The proposed model improves on four NER datasets on Chinese characters, potential words, and the whole-sentence semantics.

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Zhongyu Yang, Junhao Song, Siyang Song, Wei Pang, Yingfang Yuan

Sentiment Analysis

Challenge:	Existing multimodal large language models struggle to handle ambiguous emotional expressions and implicit affective cues, which are crucial for affective understanding but largely overlooked.
Approach:	They propose a multi-agent framework that integrates a self-reflection module, an emotion-guided visual augmentation module, and a cross-modal verification module to enhance emotion recognition.
Outcome:	Extensive experiments show that MERMAID outperforms existing methods and achieves absolute accuracy gains of 8.70%–27.90% across diverse benchmarks.

Unleashing the Power of Language Models in Text-Attributed Graph (2023.findings-emnlp)

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Haoyu Kuang, Jiarong Xu, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, Zhongyu Wei

Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation (2024.findings-acl)

Challenge:	Existing studies on graph learning on text-attributed graphs have been limited by memory cost and underutilization of relationships between nodes and words.
Approach:	They propose a Node Representation Update Pre-training Architecture based on Co-modeling text and graph to learn representations of papers and words simultaneously.
Outcome:	The proposed model outperforms baselines on the ogbn-arxiv benchmark dataset.

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Xinyi Mou, Zhongyu Wei, Xuanjing Huang

Challenge:	Existing methods for simulating social movements encounter challenges in capturing behavior of participants.
Approach:	They propose a hybrid framework for social media user simulation wherein users are categorized into two types: core and ordinary users.
Outcome:	The proposed framework is able to simulate the behavior of social media users across real-world datasets and demonstrate its effectiveness and flexibility.

Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval (2022.findings-naacl)

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Zhihao Fan, Zhongyu Wei, Zejun Li, Siyuan Wang, Xuanjing Huang, Jianqing Fan

VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models (2025.naacl-long)

Challenge:	Existing approaches to retrieve hard negative sentences are limited in the scale of the dataset thus fail to identify negative samples of high difficulty for every image.
Approach:	They propose to use a model to generate synthetic negative sentences with higher difficulty by masking and refilling the images and performing word discrimination and word correction tasks to improve retrieval and generation.
Outcome:	The proposed model generates synthetic negative sentences with higher difficulty on MS-COCO and Flickr30K and is robust and faithful to state-of-the-art training.

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Zejun Li, Ruipu Luo, Jiwen Zhang, Minghui Qiu, Xuanjing Huang, Zhongyu Wei

Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs (2023.acl-long)

Challenge:	Despite the impressive capabilities of large multi-modal models, their effectiveness in handling complex tasks has been limited by the prevailing singlestep reasoning paradigm.
Approach:	They propose a visuallygrounded object-centric Chain-of-Thought reasoning framework for LMMs that is based on a multi-modal interleaved and aligned representation of object concepts.
Outcome:	The proposed model outperforms SOTA models in CLEVR and EmbSpatial benchmarks.

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Siyuan Wang, Zhongyu Wei, Meng Han, Zhihao Fan, Haijun Shan, Qi Zhang, Xuanjing Huang

AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)

Challenge:	Existing methods for inductive reasoning over knowledge graphs lack the ability to model the logical structures of complex queries.
Approach:	They propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs that encodes linearized query structures and entities using pre-trained language models to find answers.
Outcome:	The proposed framework encodes query structures and entities using pre-trained language models to find answers.

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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei

Linguistics & Cognitive NLP

Challenge:	Large language models are increasingly employed to empower autonomous agents to simulate human behavior.
Approach:	They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts.
Outcome:	The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning.

Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text (2022.findings-acl)

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Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, Nan Duan

An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving (2021.findings-emnlp)

Challenge:	Existing methods for logical reasoning of text focus on contextual semantics while struggling to explicitly model the logical inference process.
Approach:	They propose a logic-driven context extension framework and a data-driven augmentation algorithm that uses contrastive learning to better capture logical information.
Outcome:	The proposed framework outperforms existing methods on two benchmark datasets, ReClor and LogiQA.

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Qinzhuo Wu, Qi Zhang, Zhongyu Wei

Numerical Reasoning

Challenge:	Existing work on graph neural networks to capture word relationships neglects the rest of the problem.
Approach:	They propose an edge-enhanced hierarchical graph encoder to incorporate edge label information.
Outcome:	The proposed model can improve performance on the MAWPS and Math23K datasets compared with state-of-the-art methods.

Modeling Evolution of Message Interaction for Rumor Resolution (2020.coling-main)

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Lei Chen, Zhongyu Wei, Jing Li, Baohua Zhou, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for rumor resolution ignore local interactions during the message diffusion which is important for the identification of rumors.
Approach:	They propose to model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity.
Outcome:	Experiments on a PHEME dataset show that the proposed model achieves higher accuracy than existing methods.

Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph (D19-1)

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Xinzhu Lin, Xiahui He, Qin Chen, Huaixiao Tou, Zhongyu Wei, Ting Chen

Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)

Challenge:	Existing studies on symptom diagnosis based on EHRs focus on the standard electronic medical records, but the dialogues between doctors and patients that contain more rich information are not well studied.
Approach:	They propose to build a global attention mechanism to capture more symptom related information and build symptom graphs to model the associations between symptoms rather than treating each symptom independently.
Outcome:	The proposed model achieves the state-of-the-art on the constructed dataset.

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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan

Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template (D19-55)

Challenge:	Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems.
Approach:	They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations.
Outcome:	The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods.

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Yunfan Gu, Yang Yuqiao, Zhongyu Wei

Paraphrasing

Challenge:	Recent approaches for paraphrasing generate unpredictable results .
Approach:	They propose a question paraphrasing pipeline model based on templates that identifies template and retrieves candidate templates and fills them with original topic words.
Outcome:	The proposed model outperforms the seq2seq model on two datasets and is more promising when the training sample is small.

From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking (2024.emnlp-main)

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Siyuan Wang, Zhuohan Long, Zhihao Fan, Zhongyu Wei

Align Voting Behavior with Public Statements for Legislator Representation Learning (2021.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable performance across various tasks, effectively following instructions to meet diverse user needs.
Approach:	They propose a framework for evaluation benchmarks and attack techniques for LLMs and MLLMs to enhance their security.
Outcome:	The proposed frameworks have been exploited to exploit the weaknesses of LLMs and MLLMs.

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Xinyi Mou, Zhongyu Wei, Lei Chen, Shangyi Ning, Yancheng He, Changjian Jiang, Xuanjing Huang

Representation Learning

Challenge:	Existing studies rely on roll call data to estimate political preference of legislators.
Approach:	They propose to integrate voting behavior and public statements on Twitter to jointly model legislators.
Outcome:	The proposed model improves on the task of roll call vote prediction . it also shows that the model captures nuances in statements .

PathQG: Neural Question Generation from Facts (2020.emnlp-main)

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Siyuan Wang, Zhongyu Wei, Zhihao Fan, Zengfeng Huang, Weijian Sun, Qi Zhang, Xuanjing Huang

Representation Learning

Challenge:	Existing research for question generation encodes text as a sequence of tokens without explicitly modeling fact information.
Approach:	They propose to incorporate facts in the input text for question generation in a comprehensive way.
Outcome:	The proposed model outperforms state-of-the-art models and human evaluation shows it generates relevant and informative questions.

Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization (2024.lrec-main)

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Libo Sun, Siyuan Wang, Meng Han, Ruofei Lai, Xinyu Zhang, Xuanjing Huang, Zhongyu Wei

Text Generation

Challenge:	Product review summarization aims to generate a concise summary based on product reviews . factual accuracy, aspect comprehensiveness, and content relevance are challenges .
Approach:	They propose an FB-Thinker framework to improve product review summarization ability . they propose two Chinese product review summary datasets for instruction-tuning and evaluation .
Outcome:	The proposed framework improves product review summarization with forward reasoning and backward refinement.

CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation (2026.acl-long)

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Ruifeng Yuan, Wanxing Chang, Weiwei Cao, Bowen Shi, Zhongyu Wei, Ling Zhang, Jianpeng Zhang

SFMSS: Service Flow aware Medical Scenario Simulation for Conversational Data Generation (2025.findings-naacl)

Challenge:	Existing evaluation metrics for radiology report generation focus on lexical overlap and entity matching.
Approach:	They propose a benchmark to evaluate the fine-grained factual consistency of CT reports . they use a question-answering process to query a machine-generated report .
Outcome:	The proposed benchmark evaluates the fine-grained factual consistency of CT reports . it correlates better with expert clinical assessment and is more sensitive to errors .

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Zhijie Bao, Qingyun Liu, Xuanjing Huang, Zhongyu Wei

Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation (2025.coling-main)

Challenge:	Medical-specific Large Language Models (LLMs) have demonstrated impressive performance on medical-related exams and tasks.
Approach:	They propose a framework for medical conversational data generation that uses Authentic Seed Data to ensure quality of the data.
Outcome:	The proposed model outperforms all baselines and human evaluations, and aligns with human preferences and clinical demands.

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Siyuan Wang, Zhuohan Long, Zhihao Fan, Xuanjing Huang, Zhongyu Wei

Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better (2026.findings-acl)

Challenge:	Recent advances in Large Language Models have demonstrated remarkable performance across tasks.
Approach:	They propose a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models.
Outcome:	The proposed framework extends existing benchmarks to extend models across tasks and tasks.

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Dianyi Wang, Wei Song, Yikun Wang, Siyuan Wang, Kaicheng Yu, Zhongyu Wei, Jiaqi Wang

Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification (2021.findings-acl)

Challenge:	Typical large vision-language models emphasize vision-to-language alignment while overlooking fine-grained visual information.
Approach:	They introduce autoregressive semantic visual reconstruction (ASVR) that enables joint learning of visual and textual modalities within a unified autoregression framework.
Outcome:	The proposed model improves baselines and multimodal understanding benchmarks by 2-3%.

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Jian Yuan, Zhongyu Wei, Donghua Zhao, Qi Zhang, Changjian Jiang

Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction (2025.findings-naacl)

Challenge:	Existing researches focus on sentence matching but the interaction of opinions requires reasoning of knowledge, which is beyond textual information.
Approach:	They propose to leverage external knowledge to enhance the identification of interactive argument pairs by analyzing the discussion thread of the target topic in an online forum.
Outcome:	The proposed model achieves state-of-the-art in the benchmark dataset.

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ShengbinYue ShengbinYue, Ting Huang, Zheng Jia, Siyuan Wang, Shujun Liu, Yun Song, Xuanjing Huang, Zhongyu Wei

SpeechMedAssist: Efficiently and Effectively Adapting Speech Language Models for Medical Consultation (2026.acl-long)

Challenge:	Large Language Models (LLMs) have advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios.
Approach:	They propose a Multi-agent Legal Simulation Driver to generate synthetic data by simulating interactive legal scenarios.
Outcome:	The proposed framework ensures consistency of legal attributes between participants and introduces a supervisory mechanism to align participants’ characters and behaviors as well as addressing distractions.

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Sirry Chen, Jieyi Wang, Wei Chen, Zhongyu Wei

Mask Attention Networks: Rethinking and Strengthen Transformer (2021.naacl-main)

Challenge:	Recent advances in speech language models have enabled more natural speech-based interactions, but the scarcity of medical speech data and the inefficiency of fine-tuning on speech data hinder adoption of SpeechLMs in medical consultation.
Approach:	They propose a SpeechLM natively capable of conducting speech-based multi-turn interactions with patients.
Outcome:	The proposed model outperforms baselines in both effectiveness and robustness in most evaluation settings.

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Zhihao Fan, Yeyun Gong, Dayiheng Liu, Zhongyu Wei, Siyuan Wang, Jian Jiao, Nan Duan, Ruofei Zhang, Xuanjing Huang

EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)

Challenge:	Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation.
Approach:	They propose to combine SAN and Feed-Forward Networks to create a dynamic mask attention network with a learnable mask matrix which can model localness adaptively.
Outcome:	The proposed model outperforms the original Transformer on translation and text summarization tasks.

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Xinyi Mou, Chen Qian, Wei Liu, Ling Yan, Yao Hu, Xuanjing Huang, Zhongyu Wei

Green & Sustainable NLP

Challenge:	Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics.
Approach:	They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%.
Outcome:	The proposed model reduces token consumption by over 20% while preserving human language.

Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments (2026.acl-long)

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Zheng Jia, Shengbin Yue, Wei Chen, Siyuan Wang, Yidong Liu, Zejun Li, Yun Song, Zhongyu Wei

Challenge:	Existing benchmarks for legal intelligence are limited to static evaluation paradigms or simplified scenarios.
Approach:	They introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents.
Outcome:	The proposed framework assesses task performance and procedural compliance across legal proficiency levels.

A Partition Filter Network for Joint Entity and Relation Extraction (2021.emnlp-main)

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Zhiheng Yan, Chong Zhang, Jinlan Fu, Qi Zhang, Zhongyu Wei

AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models (2025.coling-demos)

Challenge:	Existing approaches to extract entity and relation feature are flawed because they do not consider the intimate connection between NER and RE.
Approach:	They propose a partition filter network to model two-way interaction between tasks . they leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition.
Outcome:	The proposed model performs significantly better than previous approaches on six public datasets.

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Xiawei Liu, Shiyue Yang, Xinnong Zhang, Haoyu Kuang, Libo Sun, Yihang Yang, Siming Chen, Xuanjing Huang, Zhongyu Wei

A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)

Challenge:	We introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation.
Approach:	They introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation.
Outcome:	The proposed system generates public responses considering demographic distributions.

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Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu

Detecting Adversarial Samples through Sharpness of Loss Landscape (2023.findings-acl)

Challenge:	Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context.
Approach:	They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model.
Outcome:	The proposed model captures structural information of arguments and is able to identify arguments automatically.

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Rui Zheng, Shihan Dou, Yuhao Zhou, Qin Liu, Tao Gui, Qi Zhang, Zhongyu Wei, Xuanjing Huang, Menghan Zhang

Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining (2023.emnlp-main)

Challenge:	Existing studies have shown that adversarial samples are more vulnerable than normal ones to textual adversarials.
Approach:	They propose a simple and effective sharpness-based detector that can distinguish adversarial samples by maximizing the loss increment within the region where the inference sample is located.
Outcome:	The proposed method outperforms previous detection methods by large margins on three text classification tasks.

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Jingcong Liang, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei

Task-oriented Dialogue System for Automatic Diagnosis (P18-2)

Challenge:	Recent studies have discussed its capability to assist language models for various applications.
Approach:	They propose a structure to organize arguments using the Hierarchical Argumentation Graph (Hi-ArG) and propose two approaches to exploit Hi-AarG, including a text-graph multi-modal model GreaseArR and a framework augmented with graph information.
Outcome:	The proposed structure supersedes existing language models on two argumentation tasks while incorporating graph information during further training improves vanilla language models.

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Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, Xiangying Dai

A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators (C18-1)

Challenge:	Existing methods to identify phenotypes using electronic health records (EHRs) are expensive and difficult to transfer models from one disease to another.
Approach:	They propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports.
Outcome:	The proposed system can collect additional symptoms from conversation and improve disease identification accuracy.

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Zhihao Fan, Zhongyu Wei, Siyuan Wang, Yang Liu, Xuanjing Huang

Text Generation

Challenge:	Existing research on visual question generation is focused on training models to fit the annotated data set that makes them indifferent from other language generation tasks.
Approach:	They propose to use two discriminators to enhance the training of a visual question generator to ask natural questions about an image.
Outcome:	The proposed model outperforms state-of-the-art models in terms of automatic and human evaluation metrics.

Simple-VGC: Enhancing Visual Grounding in Multimodal Reasoning via Adaptive Tool Composition (2026.acl-long)

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Ye Wang, Qianglong Chen, Siyuan Wang, Zejun Li, Shijie Guo, Zhirui Zhang, Zhongyu Wei

Connectivity Patterns are Task Embeddings (2023.findings-acl)

Challenge:	Existing multimodal large language models suffer from systematic failures in basic visual understanding.
Approach:	They propose a tool-augmented reasoning framework with three targeted compensation strategies to address these problems.
Outcome:	The proposed framework improves visual grounding by re-injecting the original image to mitigate visual forgetting, the authors show . the proposed framework also improves the accuracy of the visual inputs, the researchers show - and the results are promising .

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Zhiheng Xi, Rui Zheng, Yuansen Zhang, Xuanjing Huang, Zhongyu Wei, Minlong Peng, Mingming Sun, Qi Zhang, Tao Gui

One-Model-Connects-All: A Unified Graph Pre-Training Model for Online Community Modeling (2023.findings-emnlp)

Challenge:	Existing methods for predicting inter-task transferability are sparse and task-specific.
Approach:	They propose a method that uses connectivity patterns of neurons as a unique identifier associated with a task.
Outcome:	The proposed method outperforms baselines in predicting inter-task transferability across data regimes and transfer settings while keeping high efficiency in computation and storage.

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Ruoxue Ma, Jiarong Xu, Xinnong Zhang, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, Zhongyu Wei

AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs (2026.acl-long)

Challenge:	Existing studies on integrating online community to solve social problems have not fully utilized these three components and the relationship among them.
Approach:	They propose a framework that simultaneously considers communities, users, and texts and can easily connect with a variety of downstream tasks related to social media.
Outcome:	The proposed model can be used to perform violation detection, sentiment analysis, and community recommendation across multiple tasks.

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Xuanwen Ding, Chengjun Pan, Zejun Li, Jiwen Zhang, Siyuan Wang, Zhongyu Wei

Green & Sustainable NLP

Challenge:	Evaluating multimodal large language models (MLLMs) is becoming increasingly expensive as benchmarks grow in scale and cross-modality complexity.
Approach:	They propose an adaptive evaluation framework for efficient benchmarking that treats evaluation as an interview-like process by keeping a hypothesized ability structure of the evaluated model and actively selecting the informative questions.
Outcome:	Experiments on four representative multimodal benchmarks show that **A2-Judger significantly improves sample efficiency while maintaining reliable evaluation results.

Open Set Relation Extraction via Unknown-Aware Training (2023.acl-long)

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Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang

Discrete Argument Representation Learning for Interactive Argument Pair Identification (2021.naacl-main)

Challenge:	Existing supervised relation extraction methods can still misclassify unknown relations into known relations due to the lack of supervision signals.
Approach:	They propose a method that regularizes the model by dynamically synthesizing negative instances that can provide the missing supervision signals.
Outcome:	The proposed method achieves SOTA unknown relation detection without compromising the classification of known relations.

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Lu Ji, Zhongyu Wei, Jing Li, Qi Zhang, Xuanjing Huang

Representation Learning

Challenge:	Existing research on monological argumentation covers claims generation, argument structure prediction, and essay scoring.
Approach:	They propose to identify argument pairs from two posts with opposite stances to a certain topic.
Outcome:	The proposed framework outperforms competing models on a large-scale dataset . it also proves that it is useful for analyzing argument pairs from two posts .

SoMeLVLM: A Large Vision Language Model for Social Media Processing (2024.findings-acl)

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Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei

Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues (2022.acl-long)

Challenge:	Genereal domain large models lack nuanced multimodal understanding of social media . general domain models focus more on text than other modalities, which is not consistent with real-world user habits.
Approach:	They propose a Large Vision Language Model for Social Media Processing that combines five key capabilities to understand and generate real social media behavior.
Outcome:	The proposed model achieves state-of-the-art performance in multiple social media tasks.

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Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Zhongyu Wei, Weidong Zhan, Baobao Chang, Sujian Li, Tianyu Liu, Zhifang Sui

Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning (P19-1)

Challenge:	Existing work in vision language cross-modal reasoning uses binary or multi-choice classification based on source image and textual query.
Approach:	They propose a task where a textual premise is the background presumption on each source image.
Outcome:	The proposed task is based on a dataset of 15,360 movie screenshots and human-curated premise templates from 6 pre-defined categories.

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Zhihao Fan, Zhongyu Wei, Siyuan Wang, Xuanjing Huang

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)

Challenge:	Existing research on image captioning generates frequent n-grams with irrelevant words.
Approach:	They propose to construct an image-grounded vocabulary incorporating visual information and relations among words into the decoding process directly.
Outcome:	The proposed framework is compared with state-of-the-art models on MS COCO and Flickr30k and shows that it is more efficient than existing models.

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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou

Symbolic Working Memory Enhances Language Models for Complex Rule Application (2024.emnlp-main)

Challenge:	Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected.
Approach:	They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models.
Outcome:	The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models.

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Siyuan Wang, Zhongyu Wei, Yejin Choi, Xiang Ren

Interleaved Latent Visual Reasoning with Selective Perceptual Modeling (2026.acl-long)

Challenge:	Large Language Models (LLMs) excel in single-step rule application but struggle with multi-step deductive reasoning when rules are presented non-sequentially.
Approach:	They propose to augment LLMs with external working memory and introduce a neurosymbolic framework for rule application that stores facts and rules in both natural language and symbolic forms, enabling precise tracking.
Outcome:	The proposed framework iteratively performs symbolic rule grounding and LLM-based rule implementation.

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Shuai Dong, Siyuan Wang, Xingyu Liu, Chenglin Li, Haowen Hou, Zhongyu Wei

Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022.coling-1)

Challenge:	Existing approaches to interleaved reasoning are limited by the cost of re-encoding pixel-dense images.
Approach:	They propose a framework that unifies dynamic state evolution with precise perceptual modeling.
Outcome:	The proposed framework outperforms existing approaches on multimodal reasoning benchmarks.

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Siyuan Wang, Zhongyu Wei, Zhihao Fan, Qi Zhang, Xuanjing Huang

AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator (2025.coling-main)

Challenge:	Existing methods for multi-hop reasoning ignore grounding on supporting facts of each step, which tends to generate inaccurate decompositions.
Approach:	They propose an interpretable stepwise reasoning framework that incorporates supporting sentences and questions at each intermediate step and utilizes the inference of the current hop for the next until reasoning out the final result.
Outcome:	The proposed model can boost performance and yield a better interpretable reasoning process without decomposition supervision.

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Zhihao Fan, Lai Wei, Jialong Tang, Wei Chen, Wang Siyuan, Zhongyu Wei, Fei Huang

MAGNET: Towards Adaptive GUI Agents with Memory-Driven Knowledge Evolution (2026.acl-long)

Challenge:	Recent large language models (LLMs) have demonstrated superior performance in static medical question answering benchmarks, rivaling even human experts.
Approach:	They propose a multi-agent framework emulating dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner to assess the performance of LLM-driven Doctor agents in simulated clinical scenarios.
Outcome:	The proposed framework emulates dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner.

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Libo Sun, Jiwen Zhang, Siyuan Wang, Zhongyu Wei

Challenge:	Mobile GUI agents powered by large foundation models can perform tasks autonomously, but frequent updates that alter UI appearance and reorganize workflows cause agents trained on historical data to fail.
Approach:	They propose a memory-driven adaptive agent framework with stationary memory that links visual features to stable functional semantics and procedural memory that captures stable task intents across varying workflows.
Outcome:	The proposed framework improves performance over memory-augmented baselines and offline benchmarks on AndroidWorld.

Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding (2026.findings-acl)

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Jieyi Wang, Yazhe Niu, Dexuan Xu, Zhongyu Wei

UI-Hawk: Unleashing the Screen Stream Understanding for Mobile GUI Agents (2025.emnlp-main)

Challenge:	Recent Large Audio Language Models (LALMs) have shown strong capabilities in audio understanding, yet their reasoning remains vulnerable to perceptual errors.
Approach:	They propose a large-scale dataset for Perception-Aware Question Answering that uses a hierarchical decoupling strategy to separate speech from environmental sounds and distinguishes among multiple speakers.
Outcome:	The proposed model improves on MMAU-mini, MMAR, and PAQA while maintaining comparable performance on multiple benchmarks.

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Jiwen Zhang, Ya-Qi Yu, Minghui Liao, WenTao Li, Jihao Wu, Zhongyu Wei

An Enhanced Knowledge Injection Model for Commonsense Generation (2020.coling-main)

Challenge:	Existing GUI agents depend on current visual observations and plain-text action history, ignoring the significance of history screens.
Approach:	They propose a multi-modal GUI agent specifically designed to process screen streams . they propose UI-Hawk incorporates a history-aware visual encoder to handle the sequences .
Outcome:	The proposed GUI agent can process screen streams encountered during GUI navigation.

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Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang

Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference (2025.acl-long)

Challenge:	a recent study shows that digging the relationship of concepts from scratch is non-trivial for commonsense generation tasks.
Approach:	They use a retrieve-and-edit framework to retrieve a prototype with these concepts . they use qt and qq to generate commonsense questions at scale .
Outcome:	The proposed method significantly improves the performance on commonsense generation tasks.

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Siyuan Wang, Dianyi Wang, Chengxing Zhou, Zejun Li, Zhihao Fan, Xuanjing Huang, Zhongyu Wei

Visual Data in NLP

Challenge:	Existing Large Vision-Language Models (LVLMs) learn visual capacity through visual instruction tuning.
Approach:	They propose a method for LVLMs to be trained by selective layers tuning . they propose removing non-critical layers outside the visual region .
Outcome:	The proposed approach preserves nearly 99% of visual performance and improves textual task results while reducing training time.

Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication (2020.coling-main)

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Ruize Wang, Zhongyu Wei, Ying Cheng, Piji Li, Haijun Shan, Ji Zhang, Qi Zhang, Xuanjing Huang

Android in the Zoo: Chain-of-Action-Thought for GUI Agents (2024.findings-emnlp)

Challenge:	Existing methods for visual storytelling construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content.
Approach:	They propose a topic description task to detect the global semantic context of an image stream and a story is then constructed with the guidance of the topic description.
Outcome:	The proposed framework can generate stories with higher quality compared to state-of-the-art methods on a VIST dataset.

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Jiwen Zhang, Jihao Wu, Teng Yihua, Minghui Liao, Nuo Xu, Xiao Xiao, Zhongyu Wei, Duyu Tang

Challenge:	Existing studies on large language models (LLMs) focus on the semantics of smartphone operations.
Approach:	They propose a large language model (LLM) which predicts a sequence of actions of API by analyzing past actions and visual observations.
Outcome:	The proposed model improves the prediction of actions on a zero-shot Android-In-The-Zoo dataset compared to previous models .

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

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

Sentiment Analysis

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

EMGLLM: Data-to-Text Alignment for Electromyogram Diagnosis Generation with Medical Numerical Data Encoding (2025.findings-acl)

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Zefei Long, Zhenbiao Cao, Wei Chen, Zhongyu Wei

Structured Data in NLP

Challenge:	Existing Large Language Models struggle to interpret EMG tables . EMGLLM is a data-to-text model for medical examination tables based on electrical signals .
Approach:	They propose a data-to-text model that aligns EMG data into word embeddings that reflect health degree.
Outcome:	The proposed model outperforms baseline models in understanding EMG tables and generating high-quality diagnoses.

LifeSim: Long-Horizon User Life Simulator for Personalized Assistant Evaluation (2026.findings-acl)

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Feiyu Duan, Xuanjing Huang, Zhongyu Wei

Challenge:	Existing benchmarks for personalized assistants fail to capture the complexity of external contexts and users’ cognitive states.
Approach:	They propose a user simulator that models user cognition through the Belief-Desire-Intention model within physical environments for coherent life trajectories generation and simulates intention-driven user interactive behaviors.
Outcome:	The proposed model can model user cognition through the Belief-Desire-Intention model within physical environments for coherent life trajectories generation and simulates intention-driven user interactive behaviors.

Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model (C18-1)

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Lu Ji, Zhongyu Wei, Xiangkun Hu, Yang Liu, Qi Zhang, Xuanjing Huang

Actively Supervised Clustering for Open Relation Extraction (2023.acl-long)

Challenge:	Existing research explores different text features of reply comments on word level and ignores interactions between participants.
Approach:	They propose a co-attention mechanism based neural network to capture interactions between participants on argument level to better model dialogical argumentation.
Outcome:	The proposed model outperforms state-of-the-art methods on a publicly available dataset showing that it extracts interactive argument pairs from the original post and the reply.

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Jun Zhao, Yongxin Zhang, Qi Zhang, Tao Gui, Zhongyu Wei, Minlong Peng, Mingming Sun

Doc-V*: Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA (2026.acl-long)

Challenge:	Existing methods for Open Relation Extraction (OpenRE) use a two-stage pipeline, which learns relation representations and assignments in the first stage, then manually labels relation for each cluster.
Approach:	They propose a method that performs relation learning and relation labeling simultaneously without a significant increase in human effort.
Outcome:	The proposed method improves existing SOTA methods by 13.8% and 10.6% on two datasets.

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Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang, Yuyi Zhang, Wenyu Ruan, Xiaojin Zhang, Zhongyu Wei, Zhenbo Luo, Jian Luan, Wei Chen, Xiang Bai

DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning (2024.lrec-main)

Challenge:	Existing OCR-free approaches to document visual question answering are brittle and passive.
Approach:	They propose an OCR-free agentic framework that casts multi-page DocVQA as sequential evidence aggregation.
Outcome:	The proposed framework outperforms open-source and proprietary models in five benchmarks and improves out-of-domain performance by 47.9% over baseline.

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Mengfei Du, Binhao Wu, Jiwen Zhang, Zhihao Fan, Zejun Li, Ruipu Luo, Xuanjing Huang, Zhongyu Wei

PASUM: A Pre-training Architecture for Social Media User Modeling Based on Text Graph (2024.lrec-main)

Challenge:	Existing studies focus on cross-modal attention at the fusion stage, but modality features generated by disparate uni-encoders reside in their own spaces, leading to a decline in the quality of cross-modulation and decision-making.
Approach:	They propose a framework to align navigation-related modalities before fusion by cross-modal contrastive learning.
Outcome:	The proposed framework integrates with the majority of existing models, resulting in improved navigation performance on various VLN benchmarks, including R2R, R4R, and CVDN.

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Kun Wu, Xinyi Mou, Lanqing Xue, Zhenzhe Ying, Weiqiang Wang, Qi Zhang, Xuanjing Huang, Zhongyu Wei

Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM (2024.findings-acl)

Challenge:	Existing studies have incorporated different digital traces to better learn the representations of social media users, limited by overloaded text information and hard-to-collect social network information.
Approach:	They propose a Pre-training Architecture for Social Media User Modeling based on Text Graph and combine microblogs to represent social media users based upon the text graph model.
Outcome:	The proposed framework can represent users based on text even without social network information on microblogs.

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Jingcong Liang, Rong Ye, Meng Han, Ruofei Lai, Xinyu Zhang, Xuanjing Huang, Zhongyu Wei

Challenge:	Recent studies have focused on short dialogues, but mainly on short debates.
Approach:	They propose to use Large Language Models to construct an automated debate judge to evaluate multi-turn debates.
Outcome:	The proposed system improves on the PanelBench benchmark, which compares its performance to actual debate outcomes.

Word Form Matters: LLMs’ Semantic Reconstruction under Typoglycemia (2025.findings-acl)

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Chenxi Wang, Tianle Gu, Zhongyu Wei, Lang Gao, Zirui Song, Xiuying Chen

Challenge:	Typoglycemia is a phenomenon where people can read words even when the middle letters of the words are scrambled.
Approach:	They propose a reliable metric to quantify the degree of semantic reconstruction and validate its effectiveness.
Outcome:	The proposed metric quantifies the degree of semantic reconstruction and validates its effectiveness.

Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training (2023.acl-long)

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Zejun Li, Zhihao Fan, Jingjing Chen, Qi Zhang, Xuanjing Huang, Zhongyu Wei

Multilinguality

Challenge:	Existing studies address the problem of translating English data into other languages, but they are limited in form and scale.
Approach:	They propose a framework to unify cross-lingual and cross-modal pre-training by using English data.
Outcome:	The proposed framework unifies cross-lingual and cross-modal pre-training on different data.

Simplify the Usage of Lexicon in Chinese NER (2020.acl-main)

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Ruotian Ma, Minlong Peng, Qi Zhang, Zhongyu Wei, Xuanjing Huang

EmbSpatial-Bench: Benchmarking Spatial Understanding for Embodied Tasks with Large Vision-Language Models (2024.acl-short)

Challenge:	Named entity recognition (NER) is concerned with the identification of named entities in unstructured text.
Approach:	They propose a method for incorporating word lexicon into character representations . experimental results show method can be easily incorporated with pre-trained models .
Outcome:	The proposed method achieves 6.15 times faster inference speed and better performance on four benchmark Chinese NER datasets.

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Mengfei Du, Binhao Wu, Zejun Li, Xuanjing Huang, Zhongyu Wei

UPPAM: A Unified Pre-training Architecture for Political Actor Modeling based on Language (2023.acl-long)

Challenge:	Recent studies have revealed significant deficiencies of LVLMs in understanding visual contents, leaving the gap between current embodied intelligence and large vision-language models (LVLM) .
Approach:	They propose to use a benchmark to evaluate LVLMs' spatial understanding of embodied environments to evaluate their ability to understand visual contents.
Outcome:	The proposed benchmark is derived from embodied scenes and covers 6 spatial relationships from an egocentric perspective.

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Xinyi Mou, Zhongyu Wei, Qi Zhang, Xuanjing Huang

A Two Stage Adaptation Framework for Frame Detection via Prompt Learning (2022.coling-1)

Challenge:	Existing studies have incorporated contextual information to better learn the representation of political actors for specific tasks.
Approach:	They propose to use statements to represent political actors and learn mapping from languages to representations using social networks and behaviors as self-constructed supervision.
Outcome:	The proposed model can be generalized to political actors and solve downstream tasks.

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Xinyi Mou, Zhongyu Wei, Changjian Jiang, Jiajie Peng

SocioBench: Modeling Human Behavior in Sociological Surveys with Large Language Models (2025.emnlp-main)

Challenge:	Existing frameworks focus on a single scenario or issue, ignoring the special characteristics of frame detection that new events emerge continuously and policy agenda changes dynamically.
Approach:	They propose a framework to adapt to different contexts and frame typologies . they propose coding tasks that learn transferable encoders and verbalizers based on pivots and prompts - and generalization tasks that apply them to new issues and label sets.
Outcome:	The proposed framework shows superiority in both full-resource and low-resourced conditions.

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Jia Wang, Ziyu Zhao, Tingjuntao Ni, Zhongyu Wei

Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)

Challenge:	Large language models (LLMs) lack large-scale, systematically constructed benchmarks for evaluating their alignment with real-world social attitudes.
Approach:	They propose a benchmark to assess LLMs' alignment with real-world social attitudes . they find LLM models achieve only 30–40% accuracy when simulating individuals .
Outcome:	The proposed benchmark shows that LLMs achieve only 30% accuracy when simulating individuals in complex survey scenarios.

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Ruize Wang, Duyu Tang, Nan Duan, Wanjun Zhong, Zhongyu Wei, Xuanjing Huang, Daxin Jiang, Ming Zhou

Challenge:	Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments.
Approach:	They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts.
Outcome:	The proposed method achieves superior performance on a large dataset for propaganda detection.

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang

Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs (2024.acl-long)

Challenge:	Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach:	They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome:	The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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Siyuan Wang, Zhongyu Wei, Yejin Choi, Xiang Ren

KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (2023.findings-acl)

Challenge:	Large language models (LLMs) have impressive human-like performance across various reasoning tasks, but their mastery of underlying inferential rules falls short of human capabilities.
Approach:	They propose a logic scaffolding inferential rule generation framework to construct an infer- ential rule base, ULogic, comprising both primitive and compositional rules across five domains.
Outcome:	The proposed model improves the ability to generate accurate, complex and abstract conclusions and premises and improves various commonsense reasoning tasks.

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Wei Chen, Shiqi Wei, Zhongyu Wei, Xuanjing Huang

Automatic Term Name Generation for Gene Ontology: Task and Dataset (2020.findings-emnlp)

Challenge:	Existing frameworks for symptom status recognition in doctor-patient dialogues are inadequate.
Approach:	They propose a framework for symptom status recognition that formalizes a natural language inference task . they generate knowledge about the symptom and a hypothesis about its status for each symptom .
Outcome:	The proposed framework outperforms baselines and has advantages in cross-disease and cross-symptom scenarios.

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Yanjian Zhang, Qin Chen, Yiteng Zhang, Zhongyu Wei, Yixu Gao, Jiajie Peng, Zengfeng Huang, Weijian Sun, Xuanjing Huang

Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning (D18-1)

Challenge:	Gene Ontology (GO) terms are used to describe gene function in biology and bio-medicine.
Approach:	They propose a task to generate term names for GO and build a large-scale benchmark dataset.
Outcome:	The proposed model outperforms baselines by incorporating the relations between genes, words and terms for term name generation.

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Yucheng Wang, Zhongyu Wei, Yaqian Zhou, Xuanjing Huang

Challenge:	Existing systems for automatic essay scoring are trained to predict the score of each essay at a time without considering rating schema.
Approach:	They propose a reinforcement learning framework that incorporates quadratic weighted kappa as guidance to optimize the scoring system.
Outcome:	Experiments on benchmark datasets show the proposed framework is effective.

A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)

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Lei Chen, Guanying Li, Zhongyu Wei, Yang Yang, Baohua Zhou, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges.
Approach:	They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification.
Outcome:	The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification.

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

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

DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization (2023.acl-long)

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

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SongYang Gao, Shihan Dou, Yan Liu, Xiao Wang, Qi Zhang, Zhongyu Wei, Jin Ma, Ying Shan

DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)

Challenge:	Existing adversarial training methods require multi-step gradient ascents or word substitutions to obtain adversarials, which impairs the effectiveness of adversariarial training.
Approach:	They propose a procedure for instead adversarial training with only clean data that estimates the adversarials loss by perturbing the input data’s probability distribution rather than their embeddings.
Outcome:	The proposed procedure reduces time consumption by up to 70% compared to current best-performing adversarial training methods.

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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan

Dialogue Systems & Conversational Agents

Challenge:	Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP).
Approach:	They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses.
Outcome:	The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets.

Strong Reasoning Isn’t Enough: Evaluating Evidence Elicitation in Interactive Diagnosis (2026.findings-acl)

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Zhuohan Long, Zhongyu Wei