Papers by Lishuang Li
Rule-Guided Extraction: A Hierarchical Rule Optimization Framework for Document-Level Event Argument Extraction (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Document-level event argument extraction (EAE) is a critical task in natural language processing. |
| Approach: | They propose an LLM-driven HiErarchical Rule Optimization framework that iteratively generates and selects optimal hierarchical rules. |
| Outcome: | The proposed framework outperforms few-shot supervised methods and outperformed state-of-the-art prompting baselines. |
Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | Few-shot Event Detection (FSED) requires limited labeled data and expensive manual labeling. |
| Approach: | They propose a prototype-based prompt-instance Interaction with causal Intervention model to utilize both prompts and verbalizers and effectively eliminate all biases. |
| Outcome: | The proposed model utilizes both prompts and verbalizers and eliminates all biases on RAMS and ACE datasets. |
Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models with reasoning are single-granularity based on one element information, ignoring complementary fact of different granularities. |
| Approach: | They propose a document-level biomedical relation extraction model called FILR . it uses multi-dimensional information fusion and multi-granularity logic to obtain rich inferences . |
| Outcome: | The proposed model extracts all relation facts from biomedical documents . it is based on multi-dimensional information fusion and multi-granularity logic reasoning . the proposed model achieves state-of-the-art performance on two widely used biomedically corpora . |
t-HNE: A Text-guided Hierarchical Noise Eliminator for Multimodal Sentiment Analysis (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for multimodal sentiment analysis assume that all modalities contribute equally to model performance. |
| Approach: | They propose a text-guided Hierarchical Noise Eliminator model that extracts modality-consistent information from unimodal data and integrates it into multimodal representations for sentiment classification. |
| Outcome: | The proposed model reduces noise caused by modality inconsistency by maximizing mutual information between textual representations and visual and acoustic representations. |
GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to analyzing large language models are limited by their pre-trained knowledge of Small Language Models(SLMs). |
| Approach: | They propose a global distance-aware modeling approach based on Large Language Models (LLMs) they first present a distance-based graph structure utilizing Graph Attention Network(GAT) |
| Outcome: | The proposed approach significantly improves minority relation class learning ability on two publicly available datasets, TB-Dense and MATRES. |
Event Representation Learning with Multi-Grained Contrastive Learning and Triple-Mixture of Experts (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for learning event representations fail to capture hidden feature information between events. |
| Approach: | They propose a method that extends the random masked language model by incorporating a specialized MLM to capture different grammatical structures within events. |
| Outcome: | The proposed method outperforms baselines in hard similarity and transitive sentence similarity tasks, highlighting the superiority of the proposed method. |
Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in Zero-Shot Event-Relational Reasoning (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for zero-shot event-relational reasoning require large computational resources and lack interpretability. |
| Approach: | They propose a method for Reasoning-Oriented Locating and Editing which locates and edits key modules of the language model for reasoning about event relations. |
| Outcome: | The proposed method improves interpretability and efficiency with reduced computational cost and achieves SOTA results in zero-shot event-relational reasoning. |
Temporal Cognitive Tree: A Hierarchical Modeling Approach for Event Temporal Relation Extraction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies focus on locating relative position of event pairs on timeline . hierarchical modeling approach neglects multidimensional information in temporal relation and hierarchy of reasoning. |
| Approach: | They propose a novel hierarchical modeling approach that mimics human logical reasoning by introducing a Temporal Cognitive Tree. |
| Outcome: | The proposed model outperforms existing methods on TB-Dense and MATRES datasets. |