@inproceedings{sathe-park-2021-automatic,
title = "Automatic Fact-Checking with Document-level Annotations using {BERT} and Multiple Instance Learning",
author = "Sathe, Aalok and
Park, Joonsuk",
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-1.11",
doi = "10.18653/v1/2021.fever-1.11",
pages = "101--107",
abstract = "Automatic fact-checking is crucial for recognizing misinformation spreading on the internet. Most existing fact-checkers break down the process into several subtasks, one of which determines candidate evidence sentences that can potentially support or refute the claim to be verified; typically, evidence sentences with gold-standard labels are needed for this. In a more realistic setting, however, such sentence-level annotations are not available. In this paper, we tackle the natural language inference (NLI) subtask{---}given a document and a (sentence) claim, determine whether the document supports or refutes the claim{---}only using document-level annotations. Using fine-tuned BERT and multiple instance learning, we achieve 81.9{\%} accuracy, significantly outperforming the existing results on the WikiFactCheck-English dataset.",
}
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<abstract>Automatic fact-checking is crucial for recognizing misinformation spreading on the internet. Most existing fact-checkers break down the process into several subtasks, one of which determines candidate evidence sentences that can potentially support or refute the claim to be verified; typically, evidence sentences with gold-standard labels are needed for this. In a more realistic setting, however, such sentence-level annotations are not available. In this paper, we tackle the natural language inference (NLI) subtask—given a document and a (sentence) claim, determine whether the document supports or refutes the claim—only using document-level annotations. Using fine-tuned BERT and multiple instance learning, we achieve 81.9% accuracy, significantly outperforming the existing results on the WikiFactCheck-English dataset.</abstract>
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%0 Conference Proceedings
%T Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning
%A Sathe, Aalok
%A Park, Joonsuk
%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 sathe-park-2021-automatic
%X Automatic fact-checking is crucial for recognizing misinformation spreading on the internet. Most existing fact-checkers break down the process into several subtasks, one of which determines candidate evidence sentences that can potentially support or refute the claim to be verified; typically, evidence sentences with gold-standard labels are needed for this. In a more realistic setting, however, such sentence-level annotations are not available. In this paper, we tackle the natural language inference (NLI) subtask—given a document and a (sentence) claim, determine whether the document supports or refutes the claim—only using document-level annotations. Using fine-tuned BERT and multiple instance learning, we achieve 81.9% accuracy, significantly outperforming the existing results on the WikiFactCheck-English dataset.
%R 10.18653/v1/2021.fever-1.11
%U https://aclanthology.org/2021.fever-1.11
%U https://doi.org/10.18653/v1/2021.fever-1.11
%P 101-107
Markdown (Informal)
[Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning](https://aclanthology.org/2021.fever-1.11) (Sathe & Park, FEVER 2021)
ACL