@inproceedings{wang-etal-2023-check-covid,
title = "Check-{COVID}: Fact-Checking {COVID}-19 News Claims with Scientific Evidence",
author = "Wang, Gengyu and
Harwood, Kate and
Chillrud, Lawrence and
Ananthram, Amith and
Subbiah, Melanie and
McKeown, Kathleen",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.888",
doi = "10.18653/v1/2023.findings-acl.888",
pages = "14114--14127",
abstract = "We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles. This approach to fact-checking is particularly challenging as it requires checking internet text written in everyday language against evidence from journal articles written in formal academic language. Check-COVID contains 1, 504 expert-annotated news claims about the coronavirus paired with sentence-level evidence from scientific journal articles and veracity labels. It includes both extracted (journalist-written) and composed (annotator-written) claims. Experiments using both a fact-checking specific system and GPT-3.5, which respectively achieve F1 scores of 76.99 and 69.90 on this task, reveal the difficulty of automatically fact-checking both claim types and the importance of in-domain data for good performance. Our data and models are released publicly at \url{https://github.com/posuer/Check-COVID}.",
}
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<abstract>We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles. This approach to fact-checking is particularly challenging as it requires checking internet text written in everyday language against evidence from journal articles written in formal academic language. Check-COVID contains 1, 504 expert-annotated news claims about the coronavirus paired with sentence-level evidence from scientific journal articles and veracity labels. It includes both extracted (journalist-written) and composed (annotator-written) claims. Experiments using both a fact-checking specific system and GPT-3.5, which respectively achieve F1 scores of 76.99 and 69.90 on this task, reveal the difficulty of automatically fact-checking both claim types and the importance of in-domain data for good performance. Our data and models are released publicly at https://github.com/posuer/Check-COVID.</abstract>
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%0 Conference Proceedings
%T Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence
%A Wang, Gengyu
%A Harwood, Kate
%A Chillrud, Lawrence
%A Ananthram, Amith
%A Subbiah, Melanie
%A McKeown, Kathleen
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wang-etal-2023-check-covid
%X We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles. This approach to fact-checking is particularly challenging as it requires checking internet text written in everyday language against evidence from journal articles written in formal academic language. Check-COVID contains 1, 504 expert-annotated news claims about the coronavirus paired with sentence-level evidence from scientific journal articles and veracity labels. It includes both extracted (journalist-written) and composed (annotator-written) claims. Experiments using both a fact-checking specific system and GPT-3.5, which respectively achieve F1 scores of 76.99 and 69.90 on this task, reveal the difficulty of automatically fact-checking both claim types and the importance of in-domain data for good performance. Our data and models are released publicly at https://github.com/posuer/Check-COVID.
%R 10.18653/v1/2023.findings-acl.888
%U https://aclanthology.org/2023.findings-acl.888
%U https://doi.org/10.18653/v1/2023.findings-acl.888
%P 14114-14127
Markdown (Informal)
[Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence](https://aclanthology.org/2023.findings-acl.888) (Wang et al., Findings 2023)
ACL