@inproceedings{gi-etal-2021-verdict,
title = "Verdict Inference with Claim and Retrieved Elements Using {R}o{BERT}a",
author = "Gi, In-Zu and
Fang, Ting-Yu and
Tsai, Richard Tzong-Han",
editor = "Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)",
month = nov,
year = "2021",
address = "Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.fever-1.7",
doi = "10.18653/v1/2021.fever-1.7",
pages = "60--65",
abstract = "Automatic fact verification has attracted recent research attention as the increasing dissemination of disinformation on social media platforms. The FEVEROUS shared task introduces a benchmark for fact verification, in which a system is challenged to verify the given claim using the extracted evidential elements from Wikipedia documents. In this paper, we propose our 3rd place three-stage system consisting of document retrieval, element retrieval, and verdict inference for the FEVEROUS shared task. By considering the context relevance in the fact extraction and verification task, our system achieves 0.29 FEVEROUS score on the development set and 0.25 FEVEROUS score on the blind test set, both outperforming the FEVEROUS baseline.",
}
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<abstract>Automatic fact verification has attracted recent research attention as the increasing dissemination of disinformation on social media platforms. The FEVEROUS shared task introduces a benchmark for fact verification, in which a system is challenged to verify the given claim using the extracted evidential elements from Wikipedia documents. In this paper, we propose our 3rd place three-stage system consisting of document retrieval, element retrieval, and verdict inference for the FEVEROUS shared task. By considering the context relevance in the fact extraction and verification task, our system achieves 0.29 FEVEROUS score on the development set and 0.25 FEVEROUS score on the blind test set, both outperforming the FEVEROUS baseline.</abstract>
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%0 Conference Proceedings
%T Verdict Inference with Claim and Retrieved Elements Using RoBERTa
%A Gi, In-Zu
%A Fang, Ting-Yu
%A Tsai, Richard Tzong-Han
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Dominican Republic
%F gi-etal-2021-verdict
%X Automatic fact verification has attracted recent research attention as the increasing dissemination of disinformation on social media platforms. The FEVEROUS shared task introduces a benchmark for fact verification, in which a system is challenged to verify the given claim using the extracted evidential elements from Wikipedia documents. In this paper, we propose our 3rd place three-stage system consisting of document retrieval, element retrieval, and verdict inference for the FEVEROUS shared task. By considering the context relevance in the fact extraction and verification task, our system achieves 0.29 FEVEROUS score on the development set and 0.25 FEVEROUS score on the blind test set, both outperforming the FEVEROUS baseline.
%R 10.18653/v1/2021.fever-1.7
%U https://aclanthology.org/2021.fever-1.7
%U https://doi.org/10.18653/v1/2021.fever-1.7
%P 60-65
Markdown (Informal)
[Verdict Inference with Claim and Retrieved Elements Using RoBERTa](https://aclanthology.org/2021.fever-1.7) (Gi et al., FEVER 2021)
ACL