论文标题
跨文档关系提取的多跳证据检索
Multi-hop Evidence Retrieval for Cross-document Relation Extraction
论文作者
论文摘要
关系提取（RE）已扩展到跨文档的场景，因为单个文档中并不简单地描述了许多关系。这不可避免地带来了有效的开放空间证据检索以支持跨文档关系的推断的挑战，以及在实体之上的多跳推理的挑战，以及散布在一套开放文档中的证据。为了应对这些挑战，我们提出了Mr.Cod（跨文档关系提取的多跳证据检索），这是一种基于证据路径挖掘和排名的多跳证据检索方法。我们探索猎犬的多种变体以显示证据检索对于跨文档RE至关重要。我们还为此环境提出了一个上下文密集的检索器。关于编码的实验表明，与Mr.Cod先生的证据检索有效地获取了交叉文件的证据，并在封闭环境和开放环境中提高了端到端的性能。
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.
