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Multi-hop Evidence Retrieval for Cross-document Relation Extraction (2212.10786v2)

Published 21 Dec 2022 in cs.CL, cs.IR, and cs.LG

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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Authors (5)
  1. Keming Lu (35 papers)
  2. I-Hung Hsu (21 papers)
  3. Wenxuan Zhou (61 papers)
  4. Mingyu Derek Ma (27 papers)
  5. Muhao Chen (159 papers)
Citations (11)

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