CDER: Collaborative Evidence Retrieval for Document-level Relation Extraction

• URL: http://arxiv.org/abs/2504.06529v1
• Date: Wed, 09 Apr 2025 02:10:21 GMT
• Title: CDER: Collaborative Evidence Retrieval for Document-level Relation Extraction
• Authors: Khai Phan Tran, Xue Li,
• Abstract summary: Document-level Relation Extraction (DocRE) involves identifying relations between entities across multiple sentences in a document.<n>Existing evidence retrieval systems often overlook the collaborative nature among semantically similar entity pairs in the same document, hindering the effectiveness of the evidence retrieval task.<n>We propose a novel evidence retrieval framework, namely CDER. CDER employs an attentional graph-based architecture to capture collaborative patterns and incorporates a dynamic sub-structure for additional robustness in evidence retrieval.
• Score: 3.9499087751190243
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Document-level Relation Extraction (DocRE) involves identifying relations between entities across multiple sentences in a document. Evidence sentences, crucial for precise entity pair relationships identification, enhance focus on essential text segments, improving DocRE performance. However, existing evidence retrieval systems often overlook the collaborative nature among semantically similar entity pairs in the same document, hindering the effectiveness of the evidence retrieval task. To address this, we propose a novel evidence retrieval framework, namely CDER. CDER employs an attentional graph-based architecture to capture collaborative patterns and incorporates a dynamic sub-structure for additional robustness in evidence retrieval. Experimental results on the benchmark DocRE dataset show that CDER not only excels in the evidence retrieval task but also enhances overall performance of existing DocRE system.

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