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        <title>Multi-hop Evidence Retrieval for Cross-document Relation Extraction</title>
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        <namePart type="given">Keming</namePart>
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    <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 cross-document evidence and boosts end-to-end RE performance in both closed and open settings.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.657</identifier>
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%0 Conference Proceedings
%T Multi-hop Evidence Retrieval for Cross-document Relation Extraction
%A Lu, Keming
%A Hsu, I-Hung
%A Zhou, Wenxuan
%A Ma, Mingyu Derek
%A Chen, Muhao
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lu-etal-2023-multi
%X 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 cross-document evidence and boosts end-to-end RE performance in both closed and open settings.
%R 10.18653/v1/2023.findings-acl.657
%U https://aclanthology.org/2023.findings-acl.657
%U https://doi.org/10.18653/v1/2023.findings-acl.657
%P 10336-10351

Markdown (Informal)

[Multi-hop Evidence Retrieval for Cross-document Relation Extraction](https://aclanthology.org/2023.findings-acl.657) (Lu et al., Findings 2023)

• Multi-hop Evidence Retrieval for Cross-document Relation Extraction (Lu et al., Findings 2023)

ACL

• Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma, and Muhao Chen. 2023. Multi-hop Evidence Retrieval for Cross-document Relation Extraction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10336–10351, Toronto, Canada. Association for Computational Linguistics.