提出协作证据检索框架，提升文档级关系抽取精度
CDER: Collaborative Evidence Retrieval for Document-level Relation Extraction
- 构建注意力图模型捕捉实体对间协同模式
- 在基准数据集上证据召回率显著提升
- 适合研究文档级关系抽取与信息检索的学者
文档级关系抽取（DocRE）旨在识别文档中跨多个句子的实体间关系。关键证据句对精准定位实体对关系至关重要，有助于聚焦核心文本片段，从而提升DocRE性能。然而，现有证据检索系统常忽略同一文档中语义相似实体对间的协作特性，限制了检索效果。为此，我们提出新型证据检索框架CDER。CDER采用基于注意力图的架构以捕捉协作模式，并引入动态子结构增强检索鲁棒性。在标准DocRE数据集上的实验表明，CDER不仅在证据检索任务中表现优异，还提升了现有DocRE系统的整体性能。
原文摘要 · Abstract (English)
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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