Improving Evidence Detection by Leveraging Warrants

Keshav Singh, Paul Reisert, Naoya Inoue, Pride Kavumba, Kentaro Inui


Abstract
Recognizing the implicit link between a claim and a piece of evidence (i.e. warrant) is the key to improving the performance of evidence detection. In this work, we explore the effectiveness of automatically extracted warrants for evidence detection. Given a claim and candidate evidence, our proposed method extracts multiple warrants via similarity search from an existing, structured corpus of arguments. We then attentively aggregate the extracted warrants, considering the consistency between the given argument and the acquired warrants. Although a qualitative analysis on the warrants shows that the extraction method needs to be improved, our results indicate that our method can still improve the performance of evidence detection.
Anthology ID:
D19-6610
Volume:
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
57–62
Language:
URL:
https://aclanthology.org/D19-6610
DOI:
10.18653/v1/D19-6610
Bibkey:
Cite (ACL):
Keshav Singh, Paul Reisert, Naoya Inoue, Pride Kavumba, and Kentaro Inui. 2019. Improving Evidence Detection by Leveraging Warrants. In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 57–62, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Improving Evidence Detection by Leveraging Warrants (Singh et al., EMNLP 2019)
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PDF:
https://preview.aclanthology.org/update-css-js/D19-6610.pdf