Papers by Chengxiang Tan
SegDRE: A Salient Entity Guided Approach to Document-Level Relation Extraction (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing models struggle to address two major bottlenecks in Document-level Relation Extraction: extreme class imbalance and complexity of multi-hop reasoning. |
| Approach: | They propose a method that decouples the extraction space into dense and sparse scenarios. |
| Outcome: | The proposed approach yields consistent improvements over various backbone models and achieves advanced performance compared to existing enhancement methods. |
Continual Few-shot Relation Extraction via Adaptive Gradient Correction and Knowledge Decomposition (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to learn new relations with limited samples neglect the instability of embeddings in the process of different task training, which leads to catastrophic forgetting. |
| Approach: | They propose a method to analyze catastrophic forgetting by limiting embedding instability . they propose to decompose knowledge into general and task-related knowledge . |
| Outcome: | The proposed method outperforms the state-of-the-art model and improves the following degree of embeddings. |
Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for document-level multi-event extraction neglect the fine-grained difference between events in multi-documents, which leads to event confusion and missing. |
| Approach: | They propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library. |
| Outcome: | The proposed method outperforms the state-of-the-art method in the recall of multi-events. |