Multi-hop Evidence Retrieval for Cross-document Relation Extraction

• URL: http://arxiv.org/abs/2212.10786v2
• Date: Mon, 5 Jun 2023 00:43:13 GMT
• Title: Multi-hop Evidence Retrieval for Cross-document Relation Extraction
• Authors: Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma and Muhao Chen
• Abstract summary: 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. 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.
• Score: 23.98136192661566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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