Papers by Fei Xia
Improving Chinese Word Segmentation with Wordhood Memory Networks (2020.acl-main)
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| Challenge: | Contextual features are important in Chinese word segmentation (CWS) but it is difficult to integrate wordhood information into existing neural models. |
| Approach: | They propose a neural framework that integrates contextual wordhood information with several popular encoder-decoder combinations for Chinese word segmentation. |
| Outcome: | The proposed framework achieves state-of-the-art performance on five benchmark datasets. |
Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction (2020.findings-emnlp)
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| Challenge: | Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims to extract aspect terms and opinion terms from review text in the form of opinion pairs or opinion triplets. |
| Approach: | They propose a grid-based AFOE tagging scheme to address the task in an end-to-end fashion only with one unified grid tracking task. |
| Outcome: | The proposed tagging scheme outperforms baselines and achieves state-of-the-art performance. |
Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing (2022.coling-1)
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| Challenge: | Dependency parsing is an important natural language processing task which analyzes the syntactic structure of an input sentence. |
| Approach: | They propose a structure-aware encoder pre-trained on auto-parsed data to improve dependency parsing . they propose combining gold dependency trees with existing parsers to improve parser performance . |
| Outcome: | The proposed approach outperforms baselines under different parsers and dependency standards under different parameters and model architectures. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents (2021.naacl-main)
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| Challenge: | Existing methods for fine-grained opinion mining (OM) are based on span-based annotations, but they are not effective. |
| Approach: | They propose a unified span-based approach for the end-to-end OM setting using syntactic constituents and multi-task learning to integrate them into the proposed model. |
| Outcome: | The proposed approach achieves significant improvements over previous work on the MPQA 2.0 dataset and reduces the number of wrongly-predicted opinion expressions and roles. |
Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)
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| Challenge: | Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL . |
| Approach: | They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on two English benchmark datasets. |
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct (2025.findings-acl)
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Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, Fei Huang
| Challenge: | a new framework for image-text instruction data evolution improves MLLM performance . lack of high-quality instruction data remains a major bottleneck in ML modeling . |
| Approach: | They propose a multimodal instruction data evolution framework that iteratively enhances data quality through fine-grained perception, cognitive reasoning, and interaction evolution. |
| Outcome: | The proposed approach improves MLLM performance in nine vision-language tasks while using significantly less data. |
End-to-end Aspect-based Sentiment Analysis with Combinatory Categorial Grammar (2023.findings-acl)
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| Challenge: | End-to-end aspect-based sentiment analysis (EASA) is a natural language processing task that requires a deep understanding of the running text. |
| Approach: | They propose a method to improve EASA with CCG supertags that carry syntactic and semantic information of the associated words. |
| Outcome: | The proposed approach outperforms baselines and achieves state-of-the-art results on all datasets. |
Depth Growing for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition. |
| Approach: | They propose a two-stage approach with three specially designed components to construct deeper NMT models. |
| Outcome: | The proposed approach improves on WMT14 EnglishGerman and EnglishFrench translation tasks. |
HierGR: Hierarchical Semantic Representation Enhancement for Generative Retrieval in Food Delivery Search (2025.acl-industry)
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Fuwei Zhang, Xiaoyu Liu, Xinyu Jia, Yingfei Zhang, Zenghua Xia, Fei Jiang, Fuzhen Zhuang, Wei Lin, Zhao Zhang
| Challenge: | Generative retrieval (GR) is an emerging search paradigm for food delivery search. |
| Approach: | They propose a method that harnesses the advanced query understanding capabilities of large language models to enhance the retrieval of results for complex and long-tail queries in food delivery search scenarios. |
| Outcome: | The proposed method increases the number of online orders by 0.68% for complex search intents. |
PlanGenLLMs: A Modern Survey of LLM Planning Capabilities (2025.acl-long)
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| Challenge: | Existing studies have focused on developing LLMs to automate complex planning tasks. |
| Approach: | They propose to provide a comprehensive overview of current LLM planners to fill this gap . they examine performance criteria including completeness, executability, optimality, representation, generalization, and efficiency . |
| Outcome: | The proposed survey examines performance criteria for LLM planners and highlights their strengths and weaknesses. |
Improving Question Generation with Multi-level Content Planning (2023.findings-emnlp)
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| Challenge: | Existing studies suggest key phrase selection is essential for question generation, yet it is difficult to connect disjointed phrases into meaningful questions, especially for long context. |
| Approach: | They propose a QG framework that uses multi-level content planning to generate questions from a given context and an answer. |
| Outcome: | The proposed framework outperforms baselines on two popular QG datasets. |
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods (2024.lrec-main)
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Yujuan Fu, Giridhar Kaushik Ramachandran, Nicholas J. Dobbins, Namu Park, Michael Leu, Abby R. Rosenberg, Kevin Lybarger, Fei Xia, Özlem Uzuner, Meliha Yetisgen
| Challenge: | Social determinants of health (SDoH) are often studied in the electronic health record (EHR) however, there are difficulties in documenting SDoH in a tabular format due to the lack of a comprehensive SDoh tool. |
| Approach: | They propose to annotate social history sections from 1,260 clinical notes from pediatric patients within the University of Washington (UW) hospital system. |
| Outcome: | The proposed corpus captures ten distinct health determinants including living and economic stability, prior trauma, education access, substance use history, and mental health with an overall annotator agreement of 81.9 F1. |
SHARP: Self-adaptive Harmful Category-aware Prompt Generation for Black-box Jailbreaking (2026.acl-long)
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| Challenge: | Existing methods for jailbreak ignore the semantic differences between categories of harmful questions, leading to inconsistent success rates and reduced overall attack effectiveness. |
| Approach: | They propose a category-aware jailbreak framework that incorporates the semantic category of harmful questions into prompt generation. |
| Outcome: | The proposed framework improves attack success rates and category alignment and achieves better cross-category robustness compared to the state-of-the-art (SOTA) baselines. |
PDF-to-Text Reanalysis for Linguistic Data Mining (L18-1)
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| Challenge: | In the 1990s, extracting semistructured text from scientific writing in PDF files was largely a computer vision and OCR problem. |
| Approach: | They propose a system for the reanalysis of PDF-extracted text that performs block detection, respacing, and tabular data analysis for linguistic data mining. |
| Outcome: | The proposed system eliminates the extreme verbosity of XML output while leaving important positional information available for downstream processes. |
Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model (2023.eacl-main)
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| Challenge: | Existing taxonomies focus on adding concepts to the leaf nodes of the existing tree, which does not fully utilize the taxonomy’s knowledge and is unable to update the original taxomy structure. |
| Approach: | They propose a two-stage method called ATTEMPT for taxonomy completion that inserts new concepts into the correct position by finding a parent node and labeling child nodes. |
| Outcome: | The proposed method performs best on taxonomy completion and extension tasks, surpassing existing methods. |
Summarizing Medical Conversations via Identifying Important Utterances (2020.coling-main)
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| Challenge: | Applying natural language processing (NLP) techniques to the medical field is a prevailing trend nowadays and has great potential in many applications, such as key information extraction in medical literature. |
| Approach: | They propose to use a hierarchical encoder-tagger model to generate medical conversation summarization by identifying important utterances. |
| Outcome: | The proposed model outperforms baseline models and models and adds conversation-related features to improve performance. |
NLPStatTest: A Toolkit for Comparing NLP System Performance (2020.aacl-demo)
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| Challenge: | Statistical significance testing is used to compare NLP system performance, but p-values alone are insufficient because statistical significance differs from practical significance. |
| Approach: | They propose a three-stage procedure for comparing NLP system performance and a toolkit that automates the process. |
| Outcome: | The proposed procedure is based on a three-stage procedure and compares it with existing statistical testing toolkits. |
ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models (2026.findings-acl)
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Tingyun li, Zishang Jiang, Jinyi Han, Xinyi Wang, Sihang Jiang, Han Xia, Zhaoqian Dai, Ma Shuguang, Fei Yu, Jiaqing Liang, Yanghua Xiao
| Challenge: | Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability. |
| Approach: | They propose a token-level dual-process framework that explicitly decouples efficiency and correctness signals during training. |
| Outcome: | The proposed framework reduces inference cost while maintaining strong reasoning ability across multiple benchmarks. |
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks (2020.emnlp-main)
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| Challenge: | Existing studies have focused on supertagging but have not tapped into contextual information. |
| Approach: | They propose to build a graph from chunks extracted from a lexicon and apply attention over it to enhance supertagging by leveraging contextual information. |
| Outcome: | The proposed approach outperforms previous studies in terms of supertagging and parsing. |
Learning Multimodal Contrast with Cross-modal Memory and Reinforced Contrast Recognition (2024.findings-acl)
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| Challenge: | Using a memory module, we learn multimodal contrast using encoding-decoding paradigm . multimodal information are used in many applications, including news feeding, social media, etc. |
| Approach: | They propose an LLM-based approach for learning multimodal contrast following the encoding-decoding paradigm . they use a memory module with reinforced contrast recognition to enhance learning . |
| Outcome: | The proposed approach outperforms baseline and state-of-the-art studies on four English and Chinese benchmark datasets. |
Dialogue Summarization with Mixture of Experts based on Large Language Models (2024.acl-long)
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| Challenge: | Existing studies for dialogue summarization use one model at a time or treat it as a black box. |
| Approach: | They propose an LLM-based approach with role-oriented routing and fusion generation to utilize mixture of experts for dialogue summarization. |
| Outcome: | The proposed approach produces informative and accurate dialogue summarization on widely used datasets. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams (2020.coling-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging can be performed in a sequential order . existing studies have shown that jointly performing them can be effective . |
| Approach: | They propose a character-based neural model enhanced by multi-channel attention of n-grams. |
| Outcome: | The proposed model outperforms baseline models on five benchmark datasets. |
MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs (2022.emnlp-demos)
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| Challenge: | Existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability. |
| Approach: | They propose a medical conversational question-answering system based on the knowledge graph to improve scalability and controllability. |
| Outcome: | The proposed system can conduct knowledge-grounded dialogues with users, using a Chinese medical knowledge graph and a large-scale dataset. |
One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)
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Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li
| Challenge: | Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality. |
| Approach: | They propose a method that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. |
| Outcome: | Nuggets outperforms existing methods on MT-Bench and Alpaca-Eval benchmarks. |
Improving Constituency Parsing with Span Attention (2020.findings-emnlp)
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| Challenge: | Constituency parsing is a fundamental task for natural language understanding . n-grams are a conventional type of feature for contextual information . experimental results show that neural parsers with no grammar rules outperform statistical ones . |
| Approach: | They propose to incorporate n-grams into span representations by weighting them according to their contributions to the parsing process. |
| Outcome: | The proposed approach outperforms existing statistical grammar-based models on Arabic, Chinese, and English datasets. |
Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions (2025.findings-naacl)
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| Challenge: | Large language models have demonstrated great performance across various benchmarks, but data contamination is a concern in their evaluation. |
| Approach: | They analyze 50 papers on data contamination detection and test three of them as case studies to identify the possibility of data contamination. |
| Outcome: | The proposed methods can detect membership inference attacks on instance-level data, and can perform similar to random guessing on LLM pretraining datasets. |
Challenging Large Language Models with New Tasks: A Study on their Adaptability and Robustness (2024.findings-acl)
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| Challenge: | Existing evaluation approaches for large language models (LLMs) rely on existing tasks and benchmarks, raising concerns about test set contamination and the genuine comprehension abilities of LLMs. |
| Approach: | They propose to evaluate LLMs by designing new tasks, automatically generating evaluation datasets for the tasks, and conducting detailed error analyses to scrutinize LLM's adaptability to new tasks. |
| Outcome: | The proposed method examines LLMs’ adaptability to new tasks, their sensitivity to prompt variations, and their error tendencies. |
Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking (2022.findings-acl)
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| Challenge: | Existing studies require modifications to existing baseline architectures to leverage syntactic information. |
| Approach: | They propose to leverage syntactic information to improve relation extraction by training a syntax-induced encoder on auto-parsed data through dependency masking. |
| Outcome: | The proposed approach outperforms baseline models and achieves state-of-the-art results on two English datasets. |
Diversify Question Generation with Retrieval-Augmented Style Transfer (2023.emnlp-main)
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| Challenge: | Existing question generation systems focus on the internal knowledge within the textual passage or the semantic word space for diverse content planning. Existing solutions focus on relying on the knowledge of the text and the semantic words, but have not considered the potential of external knowledge for expression diversity. |
| Approach: | They propose a framework for Retrieval-Augmented Style Transfer that utilizes the style of diverse templates for question generation. |
| Outcome: | The proposed framework outperforms baselines on diversity while being comparable in terms of consistency scores. |
A Systematic Survey of Claim Verification: Corpora, Systems, and Case Studies (2025.findings-emnlp)
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| Challenge: | This survey analyses 198 studies published between January 2022 and March 2025 . |
| Approach: | This survey synthesizes recent advances in CV corpus creation and system design. |
| Outcome: | The results of this study are synthesized from 198 studies published between January 2022 and March 2025. |
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation (2025.findings-naacl)
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| Challenge: | Existing methods to enhance credibility and verifiability of large language models (LLMs) mainly focus on passage-level or paragraph-level references or citations, which fall short in verifikatability. |
| Approach: | They propose a method that provides sentence-level citations in LLM-generated responses. |
| Outcome: | The proposed method achieves 90% accuracy in long-form question-answering tasks. |
Aspect-based Sentiment Analysis with Context Denoising (2024.findings-naacl)
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| Challenge: | Existing approaches to ABSA use text encoders to locate important context features or remove them from input. |
| Approach: | They propose to improve ABSA with context denoising to remove noise from text . they use diffusion networks to perform denoizing process to gradually eliminate noise . paper shows that aspect-based sentiment analysis is effective for fine-grained analysis . |
| Outcome: | The proposed approach improves ABSA on five widely used ABSA datasets. |
MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes (2025.findings-acl)
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| Challenge: | Several studies have shown that large language models can answer medical questions correctly, outperforming the average human score in some medical exams. |
| Approach: | They introduce MEDEC, the first publicly available benchmark for medical error detection and correction in clinical notes. |
| Outcome: | The proposed model outperforms medical doctors in errors detection and correction tasks. |
Soft Contextual Data Augmentation for Neural Machine Translation (P19-1)
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| Challenge: | Existing methods for enhancing training data are limited in natural language tasks due to text characteristics. |
| Approach: | They propose a data augmentation method that softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words. |
| Outcome: | The proposed method outperforms baseline methods on small and large scale machine translation datasets. |
Constructing a Chinese Medical Conversation Corpus Annotated with Conversational Structures and Actions (L18-1)
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| Challenge: | Recent studies have found that patients' advocacy for antibiotic treatment is consequential on antibiotic over-prescribing. |
| Approach: | They propose to analyze a manually transcribed corpus of medical dialogue in Chinese pediatric consultations with annotation of conversational structures and actions. |
| Outcome: | The proposed corpus can shed light on ways to improve physician-patient communication in order to reduce antibiotic over-prescribing. |
ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition (2022.lrec-1)
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| Challenge: | Chinese word segmentation and named entity recognition are important tasks in natural language processing. |
| Approach: | They develop a Chinese medical corpus annotated with Chinese word boundary and medical term information to address this problem. |
| Outcome: | The proposed corpus will be a valuable resource for Chinese word segmentation and named entity recognition research on the medical domain. |
Large Language Models Are No Longer Shallow Parsers (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have reshaped the field of natural language processing (NLP) however, fundamental NLP tasks that involve linguistic analysis still play essential roles in the field. |
| Approach: | They propose to use constituency parsing to improve performance of LLMs on deep syntactic parse trees to prompt LLM chunking, filter out low-quality chunks and add remaining chunks to prompts to instruct LLM for parser. |
| Outcome: | The proposed approach improves LLMs' performance on constituency parsing on English and Chinese benchmark datasets. |
Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing (2024.lrec-main)
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| Challenge: | Existing methods for graph processing rely on assumptions about data relations that are inadequate when handling large and complex graph data. |
| Approach: | They propose a large language model enhanced by an uncertainty-aware module to provide a confidence score on the generated graph data. |
| Outcome: | The proposed approach surpasses state-of-the-art algorithms by a substantial margin on ten datasets. |
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis (2022.lrec-1)
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| Challenge: | Existing ABSA models do not pay attention to aspect terms and their contexts . a discriminator is introduced to improve ABSA, allowing for better understanding of aspect terms . |
| Approach: | They propose to improve ABSA by complementary learning of aspect terms . they explicitly recover aspect terms from each input sentence to better understand aspects . |
| Outcome: | The proposed approach improves ABSA on five widely used English benchmark datasets. |
Large Language Models are Better Reasoners with Self-Verification (2023.findings-emnlp)
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| Challenge: | Existing methods to solve complex natural language processing tasks require multiple steps to verify the answers. |
| Approach: | They propose to use chain of thought prompting to solve reasoning tasks with large language models. |
| Outcome: | The proposed method can improve reasoning performance on arithmetic, commonsense, and logical reasoning datasets. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge (2020.acl-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging are important fundamental tasks in natural language processing. |
| Approach: | They propose a neural model for Chinese word segmentation and part-of-speech tagging . they incorporate context features and syntactic knowledge for each input character . |
| Outcome: | The proposed model can learn and benefit from existing tools, but its quality may be poor. |