Papers by Enhong Chen

Scalable Data Synthesis through Human-like Cognitive Imitation and Data Recombination (2025.emnlp-main)

Challenge:	masked language models have been used for natural language processing tasks but few studies have adopted it in the sequence-to-sequence models.
Approach:	They propose to combine encoder and decoder to train a masked sequence-to-sequence model . they propose to train the encoder more rigorously by masking the encoded input .
Outcome:	The proposed model achieves 27.69/32.24 BLEU scores on English-German/German-English tasks with 5+ times speed up compared with an autoregressive model.

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Zhongyi Ye, Weitai Zhang, Xinyuan Zhou, Yongxin Zhu, Ninghui Rao, Enhong Chen

Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization (2024.findings-acl)

Challenge:	Large language models (LLMs) rely on massive amounts of training data, however, the quantity of empirically observed data is limited.
Approach:	They propose a data synthesis framework that mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources.
Outcome:	The proposed framework mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources thereby enhancing advanced reasoning capabilities in large language models.

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Yanghai Zhang, Ye Liu, Shiwei Wu, Kai Zhang, Xukai Liu, Qi Liu, Enhong Chen

SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)

Challenge:	Multimodal Summarization with Multimodal Output (MSMO) is a new approach to produce a multimodal summary that integrates both text and relevant images.
Approach:	They propose an Entity-Guided Multimodal Summarization model that integrates both text and relevant images to produce a multimodal summary.
Outcome:	The proposed model integrates text-image and entity-image information and refines image selection through knowledge distillation from a pre-trained vision-language model.

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Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, null Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, Enhong Chen

Challenge:	Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios.
Approach:	They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance.
Outcome:	The proposed framework improves model capabilities across all domains and scales.

Granular Entity Mapper: Advancing Fine-grained Multimodal Named Entity Recognition and Grounding (2024.findings-emnlp)

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Ziqi Wang, Chen Zhu, Zhi Zheng, Xinhang Li, Tong Xu, Yongyi He, Qi Liu, Ying Yu, Enhong Chen

LongTutor: Benchmarking Large Language Models for Long-term Personalized Tutoring (2026.acl-long)

Challenge:	Existing methods for fine-grained content extraction are limited by long-tailed distribution of textual entity categories and performance of object detectors.
Approach:	They propose a multi-granularity entity recognition module and a reranking module to integrate hierarchical information of entity categories, visual cues, and external textual resources collectively.
Outcome:	The proposed framework achieves state-of-the-art on the fine-grained content extraction task.

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Ning Li, Zheng Zhang, Zhenya Huang, Rui Li, Yi Zhan, Yinbo Luo, Qi Liu, Enhong Chen

Cross Attention Augmented Transducer Networks for Simultaneous Translation (2021.emnlp-main)

Challenge:	Existing evaluations focus on isolated, short-term interactions, overlooking the inherently long-term nature of learning.
Approach:	They propose a benchmark for long-term personalized tutoring based on an annotated learning log . they propose an automated generator–verifier pipeline to enable benchmark expansion .
Outcome:	The proposed benchmarks evaluate LLMs across three progressive tasks: evidence acquisition, knowledge state diagnosis, and adaptive teaching action.

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Dan Liu, Mengge Du, Xiaoxi Li, Ya Li, Enhong Chen

Text Generation

Challenge:	Existing approaches to simultaneous translation are limited by monotonic constraint . a novel architecture for simultaneous translation is proposed .
Approach:	They propose a cross attention-augmented transducer for simultaneous translation that optimizes both policies and translation models by expanding target sequences with blank symbols.
Outcome:	The proposed architecture achieves better latency-quality trade-offs than state-of-the-art approaches.

Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)

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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Information Extraction & Text Mining

Challenge:	Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP.
Approach:	They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens.
Outcome:	The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs.

In-Context Former: Lightning-fast Compressing Context for Large Language Model (2024.findings-emnlp)

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Xiangfeng Wang, Zaiyi Chen, Tong Xu, Zheyong Xie, Yongyi He, Enhong Chen

VIGIL: Defending LLM Agents Against Tool-Stream Injection via Verify-Before-Commit (2026.acl-long)

Challenge:	Existing methods to reduce inference costs of transformer-based large language models entail quadratic complexity . et al., 2017): transformer-derived large language model performance is a major challenge.
Approach:	They propose a method that compresses long contexts into short soft prompts . they use the self-attention mechanism of the large model to extract and condense information .
Outcome:	The proposed method reduces compression costs by 68 to 112 times while achieving 90% of baseline performance.

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Junda Lin, Zhaomeng Zhou, Zhi Zheng, Shuochen Liu, Tong Xu, Yong Chen, Enhong Chen

Robustness in NLP

Challenge:	Existing defenses for indirect prompt injection are limited by static protection mechanisms . existing models prioritize injected rules due to strict alignment, whereas static protections sever the feedback loop required for adaptive reasoning.
Approach:	They propose a framework that shifts the paradigm from restrictive isolation to a verify-before-commit protocol.
Outcome:	The proposed framework outperforms state-of-the-art dynamic defenses by reducing the attack success rate by over 22% while more thandoubling utility under attack compared to static baselines.

RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs (2023.findings-acl)

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Xukai Liu, Kai Zhang, Ye Liu, Enhong Chen, Zhenya Huang, Linan Yue, Jiaxian Yan

Structured Data in NLP

Challenge:	Existing methods for entity alignment fail to account for heterogeneity among KGs and distinction between KG entities and relations.
Approach:	They propose a Relation-gated Heterogeneous Graph Network (RHGN) that uses a relation-gate based convolutional layer to distinguish relations and entities in the KG.
Outcome:	Extensive experiments on four datasets show that the proposed method is superior to state-of-the-art methods.

ARM: An Alignment-and-Replacement Module for Chinese Spelling Check Based on LLMs (2024.emnlp-main)

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Changchun Liu, Kai Zhang, Junzhe Jiang, Zirui Liu, Hanqing Tao, Min Gao, Enhong Chen

TestAgent: An Adaptive and Intelligent Expert for Human Assessment (2025.findings-acl)

Challenge:	Chinese Spelling Check (CSC) aims to identify and correct spelling errors in Chinese texts, where enhanced semantic understanding of a sentence can significantly improve correction accuracy.
Approach:	They propose a plug-and-play Alignment-and -Replacement module that enhances existing Chinese CSC models without retraining or fine-tuning.
Outcome:	The proposed module improves existing models while reducing retraining and fine-tuning.

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Junhao Yu, Yan Zhuang, Yuxuan Sun, Weibo Gao, Qi Liu, Mingyue Cheng, Zhenya Huang, Enhong Chen

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory (2026.acl-long)

Challenge:	Existing adaptive testing methods face several challenges due to mechanized nature of most algorithms and noisy response data.
Approach:	They propose to use large language models to enhance adaptive testing through interactive engagement to capture test-takers’ responses and anomalies.
Outcome:	The proposed agent achieves more accurate results with 20% fewer questions than state-of-the-art baselines and testers preferred it in speed, smoothness, and other dimensions.

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Derong Xu, Shuochen Liu, Pengfei Luo, Pengyue Jia, Yingyi Zhang, Yi Wen, Yimin Deng, Wenlin Zhang, Enhong Chen, Xiangyu Zhao, Tong Xu

Challenge:	Existing memory systems rely on static, hand-crafted update rules for personalization, but sparse outcome rewards provide weak supervision, resulting in unstable long-horizon optimization.
Approach:	They propose a memory guideline optimization framework that learns how memory should be organized and what information to update.
Outcome:	The proposed framework learns how memory should be organized and what information to update.

MindBridge: Scalable and Cross-Model Knowledge Editing via Memory-Augmented Modality (2025.findings-acl)

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Shuaike Li, Kai Zhang, Qi Liu, Enhong Chen

Challenge:	Existing knowledge editing methods overfit to specific models, causing edited knowledge to be discarded during each LLM update and requiring frequent re-editing.
Approach:	They propose a solution that allows editors to edit knowledge in multiple LLMs at the same time.
Outcome:	The proposed solution performs better even in editing tens of thousands of knowledge entries and can adapt to different LLMs.

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment (2025.findings-emnlp)

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Yuqing Huang, Rongyang Zhang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Xuyang Zhi, Guiquan Liu, Xin Li, Hao Wang, Enhong Chen

OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting (2024.emnlp-main)

Challenge:	Existing solutions for supervised fine-tuning often lead to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities.
Approach:	They propose a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance.
Outcome:	The proposed method achieves a superior balance between downstream learning and general capability retention.

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Xukai Liu, Ye Liu, Kai Zhang, Kehang Wang, Qi Liu, Enhong Chen

Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.findings-acl)

Challenge:	Entity Linking (EL) is the process of associating ambiguous textual mentions to specific entities in a knowledge base.
Approach:	They propose a framework that utilizes the few-shot learning capabilities of Large Language Models without the need for fine-tuning to improve the accuracy of EL.
Outcome:	The framework outperforms current state-of-the-art methods in a few-shot entity linking task.

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Liyang He, Chenglong Liu, Rui Li, Zhenya Huang, Shulan Ruan, Jun Zhou, Enhong Chen

Visualization Recommendation with Prompt-based Reprogramming of Large Language Models (2024.acl-long)

Challenge:	Sentence embedding is essential for many NLP tasks, but reliance on manual labels limits scalability.
Approach:	They propose a method for controlling the generation direction of large language models in the latent space by integrating ranking information and semantic information.
Outcome:	The proposed method achieves new SOTA performance with a modest cost in ranking sentence synthesis.

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Xinhang Li, Jingbo Zhou, Wei Chen, Derong Xu, Tong Xu, Enhong Chen

Double-Checker: Large Language Model as a Checker for Few-shot Named Entity Recognition (2024.findings-emnlp)

Challenge:	Traditional visualization recommendations require extensive manual maintenance and yet fail to fully comprehend tabular data.
Approach:	They propose a hierarchical table prompt-based reprogramming framework that integrates tabular data into LLMs through a strategically crafted prompt learning method.
Outcome:	The proposed framework achieves state-of-the-art performance and will be made publicly available upon acceptance.

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Wei Chen, Lili Zhao, Zhi Zheng, Tong Xu, Yang Wang, Enhong Chen

VIRT: Improving Representation-based Text Matching via Virtual Interaction (2022.emnlp-main)

Challenge:	Recent studies have demonstrated remarkable performance on few-shot Named Entity Recognition tasks due to the high cost of obtaining high-quality labeled data.
Approach:	They propose to decompose the task into entity span detection and entity type classification using a type-independent entity span detector and then classify the detected spans based on their types.
Outcome:	The proposed method consistently yields improvements over two baseline approaches.

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Dan Li, Yang Yang, Hongyin Tang, Jiahao Liu, Qifan Wang, Jingang Wang, Tong Xu, Wei Wu, Enhong Chen

Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration (2026.acl-short)

Challenge:	Experimental results show that representation-based text matching methods suffer from performance degradation due to the lack of interactions between the pair of texts.
Approach:	They propose a virtual interaction mechanism that enables deep interaction between texts . they propose 'inteRacTion mechanism' that can be integrated into existing methods as plugins .
Outcome:	The proposed method outperforms state-of-the-art models on six text matching benchmarks.

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Hang Lv, Hongchao Gu, Ruiqing Yang, Liangyue Li, Zulong Chen, Defu Lian, Hao Wang, Enhong Chen

Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis (2022.findings-acl)

Challenge:	Existing methods for listwise reranking exhibit intrinsic position bias . existing methods are constrained by an inherent trade-off between efficiency and flexibility .
Approach:	They propose a training-free framework that mechanically decouples positional bias from ranking decisions.
Outcome:	a training-free framework decouples position bias from ranking decisions . evaluations show it outperforms training-based methods and outperformed expensive methods .

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Kai Zhang, Kun Zhang, Mengdi Zhang, Hongke Zhao, Qi Liu, Wei Wu, Enhong Chen

Sentiment Analysis

Challenge:	Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in a sentence.
Approach:	They propose to use a dynamic aspect-oriented semantics-based method to learn ABSA.
Outcome:	The proposed method can learn dynamic aspect-oriented semantics for ABSA on three benchmark datasets.

Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation (2024.emnlp-main)

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Yuanjie Lyu, Zihan Niu, Zheyong Xie, Chao Zhang, Tong Xu, Yang Wang, Enhong Chen

Information Retrieval

Challenge:	Large language models (LLMs) often produce factual errors due to limited internal knowledge.
Approach:	They propose a retrieval-augmented generation framework that generates plan tokens to guide subsequent generation.
Outcome:	The proposed framework improves the accuracy of large language models with external knowledge sources.

RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery (2025.findings-acl)

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Hongchao Gu, Dexun Li, Kuicai Dong, Hao Zhang, Hang Lv, Hao Wang, Defu Lian, Yong Liu, Enhong Chen

To Paraphrase or Not: Efficient Comment Detoxification with Unsupervised Detoxifiability Discrimination (2026.eacl-short)

Challenge:	Existing methods for knowledge-intensive long texts struggle with issues like hallucinations, topic incoherence, and significant latency.
Approach:	They propose a retrieval-augmented long text generation framework with writing Planning and Information to address these challenges.
Outcome:	The proposed framework outperforms state-of-the-art methods on a freshWiki-2024 dataset.

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Jing Ke, Zheyong Xie, Shaosheng Cao, Tong Xu, Enhong Chen

Non-Parametric Domain Adaptation for End-to-End Speech Translation (2022.emnlp-main)

Challenge:	Existing methods for detoxification of toxic comments are limited by overcorrection and data scarcity . experimental results show that DID outperforms existing methods on academic data and an industrial platform .
Approach:	They propose a paradigm that adaptively conducts filtering or paraphrasing for each toxic comment based on its detoxifiability . they propose 'detoxifiabilities-aware detoxification' that can be trained to filter or paraphrase toxic comments based upon their detoxifikatability based only on detoxificable comments .
Outcome:	Experimental results show that DID outperforms existing methods on academic and industrial data.

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Yichao Du, Weizhi Wang, Zhirui Zhang, Boxing Chen, Tong Xu, Jun Xie, Enhong Chen

Multilinguality

Challenge:	End-to-end speech translation (E2E-ST) systems have received increasing attention due to its less error propagation, lower latency and fewer parameters.
Approach:	They propose a non-parametric method that leverages in-domain text translation corpus to achieve domain adaptation for E2E-ST systems.
Outcome:	The proposed method outperforms the existing in-domain fine-tuning strategies on the Europarl-ST benchmark.

Budgeted Policy Learning for Task-Oriented Dialogue Systems (P19-1)

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Zhirui Zhang, Xiujun Li, Jianfeng Gao, Enhong Chen

Natural Language Interfaces

Challenge:	Existing approaches to integrating reinforcement learning into task-oriented dialogue systems require a fixed, small amount of user interactions to learn.
Approach:	They propose a budget-conscious scheduling approach that optimizes a fixed, small amount of user interactions for dialogue agent learning.
Outcome:	The proposed approach improves on a movie-ticket booking task with simulated and real users.

MRT: Multi-modal Short- and Long-range Temporal Convolutional Network for Time-sync Comment Video Behavior Prediction (2024.lrec-main)

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Weihao Zhao, Weidong He, Hao Wang, Haoyang Bi, Han Wu, Chen Zhu, Tong Xu, Enhong Chen

Generative Input: Towards Next-Generation Input Methods Paradigm (2024.findings-acl)

Challenge:	Using time-sync comments, it is difficult to understand user behavior due to complexity of interactions between users, videos, and comments.
Approach:	They propose a novel time-sync comment behavior prediction model that takes historical behavior into account and optimizes it on the basis of user preferences.
Outcome:	The proposed model improves the performance of time-sync comments on visual frames and textual comments on two cats playing simultaneously.

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Keyu Ding, Yongcan Wang, Zihang Xu, Zhenzhen Jia, Enhong Chen

iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)

Challenge:	generative models have been used for various NLP tasks but their application in the field of input methods remains under-explored.
Approach:	They propose a novel Generative Input paradigm that uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback.
Outcome:	The proposed paradigm achieves state-of-the-art in the Full-mode Key-sequence to Characters task and surpasses GPT-4 in the other input methods.

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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings.
Approach:	They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences.
Outcome:	The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications.

Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data (2026.acl-long)

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FengXian Dong, Zhi Zheng, Xiao Han, Wei Chen, Jingqing Ruan, Tong Xu, Yong Chen, Enhong Chen

Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)

Challenge:	Existing methods for automated feature generation rely on predefined operator libraries and do not incorporate feature semantics, limiting their ability to produce high-quality features.
Approach:	They propose a Memory-Augmented LLM-based Multi-Agent System (MALMAS) that decomposes the generation process into agents with distinct responsibilities.
Outcome:	The proposed method extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning.

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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen