Papers by Xiaoli Li

Evaluating and Enhancing the Robustness of Code Pre-trained Models through Structure-Aware Adversarial Samples Generation (2023.findings-emnlp)

Challenge:	Existing models for text-to-table generation are order-insensitive, but suffer from errors . a novel sequence-tosequence&set model generates table body rows in parallel .
Approach:	They propose a sequence-to-sequence generation task that serializes each table into a token sequence during training by concatenating all rows in a top-down order.
Outcome:	The proposed model outperforms baselines on commonly-used datasets.

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Nuo Chen, Qiushi Sun, Jianing Wang, Ming Gao, Xiaoli Li, Xiang Li

Robustness in NLP

Challenge:	Pre-trained code models have made significant strides in the field of neural code intelligence, but they are susceptible to adversarial attacks that subtly modify the input sequence and can impair generalization.
Approach:	They propose a set of novel robustness evaluation methods based on the intrinsic structure of the code to explore the impact of imperceptible perturbation.
Outcome:	The proposed methods have demonstrated their effectiveness across a wide range of models and tasks, and are able to predict the performance of perturbed models.

DocOIE: A Document-level Context-Aware Dataset for OpenIE (2021.findings-acl)

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Kuicai Dong, Zhao Yilin, Aixin Sun, Jung-Jae Kim, Xiaoli Li

Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder (2020.emnlp-main)

Challenge:	Existing solutions focus on extracting tuples at sentence level, but sentences exist as part of a document rather than standalone.
Approach:	They propose to annotate 800 sentences from 80 documents to form a DocOIE dataset . they propose to use document-level context to improve OpenIE performance .
Outcome:	The proposed OpenIE model improves performance by incorporating documentlevel context into the dataset.

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Wenyue Zhang, Xiaoli Li, Yang Li, Suge Wang, Deyu Li, Jian Liao, Jianxing Zheng

Sentiment Analysis

Challenge:	Existing methods for detecting public sentiment drift are not designed for sentiment drift detection.
Approach:	They propose a Hierarchical Variational Auto-Encoder model to learn better distribution representation and a new drift measure to directly evaluate distribution changes between historical and new data.
Outcome:	The proposed model performs better than three existing state-of-the-art methods.

Hierarchical Enhancement Framework for Aspect-based Argument Mining (2023.findings-emnlp)

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Yujie Fu, Yang Li, Suge Wang, Xiaoli Li, Deyu Li, Jian Liao, JianXing Zheng

Reasoning

Challenge:	Existing methods have primarily treated ABAM as a nested named entity recognition problem, overlooking the need for tailored strategies to effectively address the specific challenges of ABA M tasks.
Approach:	They propose a layer-based Hierarchical Enhancement Framework (HEF) for Aspect-Based Argument Mining and introduce three new components to improve the performance and accuracy.
Outcome:	Experiments on multiple datasets and tasks verify the effectiveness of the proposed framework and components.

CVRH: Cross-modal Variational Role Hypergraph Network via Semantic Enhancement for Multi-modal Event Argument Extraction (2026.findings-acl)

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Bangze Pan, Yang Li, Ruili Pu, Suge Wang, Jian Liao, JianXing Zheng, Xiaoli Li, Deyu Li

Event Extraction

Challenge:	Existing methods focus on weakly aligning uni-modal representations and generatively data augmentation techniques, but they ignore the potential impact of event role information on MEAE.
Approach:	They propose a cross-modal variational role hypergraph network via semantic enhancement to model high-order role correlations among cross-mod arguments in multi-modal documents.
Outcome:	The proposed method achieves a 6.9% improvement in F1-score on the M2E2 benchmark compared to current state-of-the-art methods.

MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction (2023.acl-long)

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Wang Jing, Aixin Sun, Hao Zhang, Xiaoli Li

Multimodality

Challenge:	Natural language video localization (NLVL) aims to localize a temporal moment from an untrimmed video that semantically corresponds to a given text query.
Approach:	They propose a proposal-based solution that generates proposals and selects the best matching proposal.
Outcome:	The proposed solution is faster than existing approaches on three public datasets.

Document-Level Event Extraction via Information Interaction Based on Event Relation and Argument Correlation (2024.lrec-main)

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Bangze Pan, Yang Li, Suge Wang, Xiaoli Li, Deyu Li, Jian Liao, Jianxing Zheng

Open-World Attribute Mining for E-Commerce Products with Multimodal Self-Correction Instruction Tuning (2025.acl-long)

Challenge:	Document-level Event Extraction (DEE) is a vital task in NLP . current approaches overlook intricate relationships among events and subtle correlations among arguments within a document .
Approach:	They propose a document-level event extraction tool that integrates event relationships and argument correlation graphs to model the relationship among events.
Outcome:	The proposed network outperforms existing models and large language models in terms of F1-score across two benchmark datasets.

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Jiaqi Li, Yanming Li, Xiaoli Shen, Chuanyi Zhang, Guilin Qi, Sheng Bi

Multimodality

Challenge:	Current AM methods focus on extracting attributes from unimodal text, underutilizing multimodal data.
Approach:	They propose a framework for multimodal self-correction instruction tuning to extract new attributes from images and text with Multimodal Large Language Models.
Outcome:	The proposed framework outperforms state-of-the-art methods on two datasets.

MHGRL: An Effective Representation Learning Model for Electronic Health Records (2024.lrec-main)

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Feiyan Liu, Liangzhi Li, Xiaoli Wang, Feng Luo, Chang Liu, Jinsong Su, Yiming Qian

Multimodality

Challenge:	Effective EHR representations are key to achieving high performance in healthcare applications.
Approach:	They propose a multimodal heterogeneous graph-enhanced representation learning to learn EHR representations using medical ontology and textual notes.
Outcome:	The proposed model outperforms baseline models on two real clinical datasets in downstream tasks.

Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs (2024.emnlp-main)

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Liu Ran, Zhongzhou Liu, Xiaoli Li, Yuan Fang

Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization (2021.emnlp-main)

Challenge:	Existing methods to predict instances for missing relations on knowledge graphs are limited by their limited training examples.
Approach:	They propose a context-aware adapter for few-shot relation learning in KGs . they propose tunable relation adaptation and contextual information for each relation .
Outcome:	Experiments on three benchmark KGs validate the superiority of RelAdapter over state-of-the-art methods.

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Yong Guan, Shaoru Guo, Ru Li, Xiaoli Li, Hu Zhang

Differentiable Window for Dynamic Local Attention (2020.acl-main)

Challenge:	Existing graph-based methods only consider word relations or structure information, which neglect the correlation between them.
Approach:	They propose a Dual Graph network for Abstractive Sentence Summarization that captures word relations and structure information from sentences.
Outcome:	The proposed model outperforms state-of-the-art methods on two popular benchmark datasets.

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Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, Xiaoli Li

Challenge:	Existing general purpose components for learning differentiable windows are hard to optimize.
Approach:	They propose a new neural module and general purpose component for dynamic window selection that can enable more focused attentions over the input regions.
Outcome:	The proposed approach improves on a myriad of NLP tasks including machine translation, sentiment analysis, subject-verb agreement and language modeling.

RST Parsing from Scratch (2021.naacl-main)

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Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, Xiaoli Li

HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution (2026.findings-acl)

Challenge:	Fig. 1 shows a document level discourse parser that performs top-down end-to-end parsing without requiring segmentation .
Approach:	They propose a top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory framework.
Outcome:	The proposed model outperforms existing methods in end-to-end parsing and parse with gold segmentation without handcrafted features.

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Hanhua Hong, Yizhi LI, Jiaoyan Chen, Sophia Ananiadou, Xiaoli Li, Jung-jae Kim, Chenghua Lin

Text Generation

Challenge:	Existing approaches to automate computational research use fixed sequential agent pipelines with weak global coordination, which limits their robustness and overall performance.
Approach:	They propose a hierarchical multi-agent framework for end-to-end paper reproduction that employs supervisory manager agents to coordinate specialised agents across fine-grained stages.
Outcome:	The proposed framework improves the paper2code benchmark and significantly reduces hallucination in the evaluation.

GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation (2021.findings-acl)

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Hongye Tan, Xiaoyue Wang, Yu Ji, Ru Li, Xiaoli Li, Zhiwei Hu, Yunxiao Zhao, Xiaoqi Han

Responsible & Trustworthy NLP

Challenge:	Existing machine reading comprehension datasets lack an explainable evaluation of systems' reasoning capabilities.
Approach:	They propose a dataset with multi-choice questions that evaluates MRC systems' reasoning process . they use sentence-level relevant supporting facts, error reason of distractors to evaluate MRC .
Outcome:	The proposed dataset is more challenging and useful for identifying limitations of existing MRC systems in an explainable way.

Inference Helps PLMs’ Conceptual Understanding: Improving the Abstract Inference Ability with Hierarchical Conceptual Entailment Graphs (2024.emnlp-main)

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Juncai Li, Ru Li, Xiaoli Li, Qinghua Chai, Jeff Pan

Reasoning

Challenge:	Existing approaches to abstract inference ignore the polysemy and hierarchical nature of concepts . prevailing approaches disregard how arguments might entail differently across various concept levels, thereby missing potential enlargement connections.
Approach:	They propose a framework that organizes arguments hierarchically and delves into entailment relations at diverse concept levels.
Outcome:	The proposed framework improves the model's generalization and reasoning prowess in natural language inference.

Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification (D19-1)

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Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, Xiaoli Li

Syntactic Multi-view Learning for Open Information Extraction (2022.emnlp-main)

Challenge:	Existing studies on short text classification focus on long texts and achieve unsatisfactory performance due to the sparsity and limited labeled data.
Approach:	They propose a heterogeneous graph neural network based method for semi-supervised short text classification that leverages the full advantage of few labeled data and large unlabeled data through information propagation along the graph.
Outcome:	The proposed method outperforms state-of-the-art methods across six benchmark datasets significantly.

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Kuicai Dong, Aixin Sun, Jung-Jae Kim, Xiaoli Li

Memorization ≠ Understanding: Do Large Language Models Have the Ability of Scenario Cognition? (2025.emnlp-main)

Challenge:	Open Information Extraction (OpenIE) aims to generate structured tuples from unstructured open-domain text.
Approach:	They propose to model constituency and dependency trees into word-level graphs and combine them with sentential semantic representations to extract relational tuples.
Outcome:	The proposed model integrates constituency and dependency trees into word-level graphs and enables neural OpenIE to learn from syntactic structures.

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Boxiang Ma, Ru Li, null Wang Yuanlong, Hongye Tan, Xiaoli Li

Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization (2025.acl-long)

Challenge:	Large language models (LLMs) have demonstrated impressive performance across NLP tasks.
Approach:	They propose a framework to assess LLMs’ scenario cognition . they examine the ability to link semantic scenario elements with their arguments in context .
Outcome:	The proposed framework assesses large language models’ scenario cognition . it shows that current models rely on superficial memorization, failing to achieve robust semantic scenario cognition even in simple cases.

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Yao Xiao, Hai Ye, Linyao Chen, Hwee Tou Ng, Lidong Bing, Xiaoli Li, Roy Ka-Wei Lee

Decomposed Prompt Tuning via Low-Rank Reparameterization (2023.findings-emnlp)

Challenge:	Large language models generate unintended outputs due to their unsupervised nature.
Approach:	They propose a method to construct preference pairs of selected and rejected LLMs by repeated random sampling to improve alignment performance.
Outcome:	The proposed method improves performance as the sample size increases.

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Yao Xiao, Lu Xu, Jiaxi Li, Wei Lu, Xiaoli Li

A Conditional Splitting Framework for Efficient Constituency Parsing (2021.acl-long)

Challenge:	Pre-trained language models have achieved remarkable performance on various tasks.
Approach:	They propose a decomposed prompt tuning approach that utilizes low-rank matrices to initialize the soft prompt.
Outcome:	The proposed method significantly reduces the number of trainable parameters while maintaining effectiveness.

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Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, Xiaoli Li

InstructEd: Soft-Instruction Tuning for Model Editing with Hops (2024.findings-acl)

Challenge:	Developing efficient and effective parsing solutions has always been a key focus in NLP.
Approach:	They propose a generic seq2seq parsing framework that casts constituency parsers into a series of conditional splitting decisions.
Outcome:	The proposed framework outperforms state-of-the-art (SoTA) methods in discourse parsing . it is based on a syntactic and discourse parsed model and is linear in number of nodes .

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XiaoQi Han, Ru Li, Xiaoli Li, Jiye Liang, Zifang Zhang, Jeff Pan

Efficient Constituency Parsing by Pointing (2020.acl-main)

Challenge:	Existing methods for model editing are limited due to excessive memorization and knowledge conflict issues.
Approach:	They propose to insert soft instructions into the attention module to facilitate interactions between instructions and questions and to understand and utilize new facts.
Outcome:	The proposed method achieves 10% improvement in one-hop (multi-hop) model editing on three datasets with LLaMAs and GPT2 .

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Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, Xiaoli Li

AGR: Reinforced Causal Agent-Guided Self-explaining Rationalization (2024.acl-short)

Challenge:	Constituency parsing is a core task in natural language processing (NLP) Existing methods for constituency paring are greedy transition-based or globally optimized.
Approach:	They propose a constituency parsing model that casts the problem into a series of pointing tasks.
Outcome:	The proposed model achieves 92.78 F1 without pre-trained models, which is faster than existing models.

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Yunxiao Zhao, Zhiqiang Wang, Xiaoli Li, Jiye Liang, Ru Li

Responsible & Trustworthy NLP

Challenge:	Existing rationalization approaches are susceptible to degeneration due to lack of effective control over the learning direction of the model during training.
Approach:	They propose an agent-guided rationalization approach that guides the next step of the model based on its current training state.
Outcome:	The proposed approach outperforms state-of-the-art methods on BeerAdvocate and HotelReview datasets.

Open Information Extraction via Chunks (2023.emnlp-main)

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Kuicai Dong, Aixin Sun, Jung-jae Kim, Xiaoli Li

Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization (2021.emnlp-main)

Challenge:	Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments.
Approach:	They propose to split a sentence into tokens and recognize token spans as tuple relations and arguments.
Outcome:	The proposed model achieves state-of-the-art on multiple OIE datasets showing that SaC has better properties than sentence as token sequence.

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Yong Guan, Shaoru Guo, Ru Li, Xiaoli Li, Hongye Tan

A Knowledge-Guided Framework for Frame Identification (2021.acl-long)

Challenge:	Sentence-level extractive text summarization is difficult to model the importance of sentences.
Approach:	They propose a Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization that leverages Frame semantics to model sentences from both intra-sentence level and inter-sentent level.
Outcome:	The proposed model outperforms six state-of-the-art methods on two benchmark corpus datasets.

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Xuefeng Su, Ru Li, Xiaoli Li, Jeff Z. Pan, Hu Zhang, Qinghua Chai, Xiaoqi Han