Papers by Xiaodong Lin

Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)

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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He

Dialogue Systems & Conversational Agents

Challenge:	Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state.
Approach:	They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states .
Outcome:	The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets.

DARL: Encouraging Diverse Answers for General Reasoning without Verifiers (2026.findings-acl)

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Chongxuan Huang, Lei Lin, Xiaodong Shi, Wenping Hu, Ruiming Tang

Reasoning

Challenge:	Recent efforts such as RLPR have extended RLVR to general domains, enabling training on broader datasets and achieving improvements over RL PR.
Approach:	They propose a framework that encourages the generation of diverse answers within a controlled deviation range from the reference while preserving alignment with it.
Outcome:	Extensive experiments on 13 benchmarks show that DARL surpasses RLPR in both reasoning accuracy and output diversity.

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)

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Fobo Shi, Peijun Qing, Dong Yang, Nan Wang, Youbo Lei, Haonan Lu, Xiaodong Lin, Duantengchuan Li

Language Models

Challenge:	Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions.
Approach:	They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts.
Outcome:	The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks.

Lightweight Spatial Modeling for Combinatorial Information Extraction From Documents (2023.findings-eacl)

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Yanfei Dong, Lambert Deng, Jiazheng Zhang, Xiaodong Yu, Ting Lin, Francesco Gelli, Soujanya Poria, Wee Sun Lee

Information Extraction & Text Mining

Challenge:	Existing datasets do not cover documents with complex spatial structures and a lack of spatial information for document entity classification.
Approach:	They propose a new spatial bias in attention calculation based on the K-nearest-neighbor graph of document entities that limits entities’ attention to their local radius.
Outcome:	The proposed model outperforms baselines in most entity types and is highly parameter-efficient compared to existing methods.

No, you’re not alone: A better way to find people with similar experiences on Reddit (D19-55)

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Zhilin Wang, Elena Rastorgueva, Weizhe Lin, Xiaodong Wu

Text Clustering

Challenge:	a probabilistic clustering algorithm can help users find posts that discuss experiences similar to their own . a recent study shows that probabilistic Clustering can yield a better performance than baseline clustering methods .
Approach:	They propose a probabilistic clustering algorithm that can help Reddit users find posts that discuss experiences similar to their own.
Outcome:	The proposed algorithm can find posts that discuss experiences similar to their own . it performs better than baseline clustering methods due to high runtime overhead .

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

Visual Data in NLP

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)

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Guanqun Bi, Lei Shen, Yanan Cao, Meng Chen, Yuqiang Xie, Zheng Lin, Xiaodong He

Text Generation

Challenge:	Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context.
Approach:	They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals.
Outcome:	The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness.

ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction (2026.findings-acl)

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Zichun Guo, Yuling Shi, Wenhao Zeng, Chao Hu, Haotian Lin, Terry Yue Zhuo, Jiawei Chen, Xiaodong Gu, Wenping Ma

Multimodality

Challenge:	Empirical evaluations on state-of-the-art MLLMs reveal a significant performance gap . ML models lack the fine-grained cross-modal reasoning required to bridge visual discontinuities.
Approach:	They propose a benchmark that renders fragmented documents directly from Markdown to facilitate evaluation of VRDU tasks.
Outcome:	The proposed benchmark renders fragmented documents directly from Markdown.

ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Parity LLM Data Valuation (2025.naacl-long)

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Yanzhou Pan, Huawei Lin, Yide Ran, Jiamin Chen, Xiaodong Yu, Weijie Zhao, Denghui Zhang, Zhaozhuo Xu

Language Models

Challenge:	Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance.
Approach:	They propose a third-party data valuation approach that assesses the value of individual data samples and proposes a learning strategy to approximate LinFiK.
Outcome:	The proposed approach surpasses baselines in effectiveness and efficiency, showing significant scalability advantages as LLM parameters increase.

CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation (2023.findings-acl)

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Yikun Lei, Zhengshan Xue, Xiaohu Zhao, Haoran Sun, Shaolin Zhu, Xiaodong Lin, Deyi Xiong

Language Models

Challenge:	End-to-end speech-totext translation (ST) data are limited due to the limited resources.
Approach:	They propose a knowledge distillation framework for speech translation that integrates knowledge from machine translation and decouples knowledge from non-target class knowledge.
Outcome:	The proposed framework outperforms state-of-the-art models on a benchmark dataset.

GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs (2022.emnlp-main)

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Dong Yang, Peijun Qing, Yang Li, Haonan Lu, Xiaodong Lin

Semantic Text Processing

Challenge:	Existing methods for embedding knowledge graphs are difficult due to complicated query structures and incomplete graph data.
Approach:	They propose a probabilistic embedding model for encoding entities and queries to answer different types of FOL queries on KGs.
Outcome:	The proposed model outperforms state-of-the-art models on public benchmarks on three large logical query datasets.

Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization (2023.findings-emnlp)

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Lei Lin, Shuangtao Li, Yafang Zheng, Biao Fu, Shan Liu, Yidong Chen, Xiaodong Shi

Semantic Text Processing

Challenge:	Recent studies show that sequence-to-sequence (seq2sequ) models struggle with compositional generalization (CG) a crucial property of human language learning is its compositional globalization (GC), the algebraic ability to understand and produce a potentially infinite number of novel combinations from known components.
Approach:	They propose a sequence-to-sequence (seq2sequ) extension which learns to compose representations of different encoder layers dynamically for different tasks.
Outcome:	The proposed model achieves competitive results on two comprehensive and realistic benchmarks, which empirically demonstrates the effectiveness of the proposed model.