Multi-hop Evidence Retrieval for Cross-document Relation Extraction

Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma, Muhao Chen

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.
Anthology ID:
2023.findings-acl.657
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10336–10351
Language:
URL:
https://aclanthology.org/2023.findings-acl.657
DOI:
10.18653/v1/2023.findings-acl.657
Bibkey:
Cite (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.
Cite (Informal):
Multi-hop Evidence Retrieval for Cross-document Relation Extraction (Lu et al., Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2023.findings-acl.657.pdf
PDF Search