@inproceedings{singh-etal-2019-improving,
title = "Improving Evidence Detection by Leveraging Warrants",
author = "Singh, Keshav and
Reisert, Paul and
Inoue, Naoya and
Kavumba, Pride and
Inui, Kentaro",
editor = "Thorne, James and
Vlachos, Andreas and
Cocarascu, Oana and
Christodoulopoulos, Christos and
Mittal, Arpit",
booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6610",
doi = "10.18653/v1/D19-6610",
pages = "57--62",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Improving Evidence Detection by Leveraging Warrants
%A Singh, Keshav
%A Reisert, Paul
%A Inoue, Naoya
%A Kavumba, Pride
%A Inui, Kentaro
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F singh-etal-2019-improving
%X 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.
%R 10.18653/v1/D19-6610
%U https://aclanthology.org/D19-6610
%U https://doi.org/10.18653/v1/D19-6610
%P 57-62
Markdown (Informal)
[Improving Evidence Detection by Leveraging Warrants](https://aclanthology.org/D19-6610) (Singh et al., 2019)
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.