Relation Extraction (RE) has been extended to cross-document scenarios because many relations are not simply described in a single document. This inevitably brings the challenge of efficient open-space evidence retrieval to support the inference of cross-document relations, along with the challenge of multi-hop reasoning on top of entities and evidence scattered in an open set of documents. To combat these challenges, we propose MR.COD (Multi-hop evidence retrieval for Cross-document relation extraction), which is a multi-hop evidence retrieval method based on evidence path mining and ranking. We explore multiple variants of retrievers to show evidence retrieval is essential in cross-document RE. We also propose a contextual dense retriever for this setting. Experiments on CodRED show that evidence retrieval with MR.COD effectively acquires crossdocument evidence and boosts end-to-end RE performance in both closed and open settings.

翻译：关系抽取（RE）已被扩展至跨文档场景，因为许多关系无法简单地在单篇文档中描述。这不可避免地带来了高效开放空间证据检索以支持跨文档关系推理的挑战，以及在开放文档集合中基于分散实体与证据进行多跳推理的挑战。为应对这些挑战，我们提出MR.COD（跨文档关系抽取的多跳证据检索），这是一种基于证据路径挖掘与排序的多跳证据检索方法。我们探索了多种检索器变体，以证明证据检索在跨文档关系抽取中的关键作用，并针对该场景提出了一种上下文稠密检索器。在CodRED上的实验表明，基于MR.COD的证据检索能够有效获取跨文档证据，并在封闭与开放两种设置下提升端到端关系抽取性能。