Nothing Special   »   [go: up one dir, main page]

Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence

Gengyu Wang, Kate Harwood, Lawrence Chillrud, Amith Ananthram, Melanie Subbiah, Kathleen McKeown


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.
Anthology ID:
2023.findings-acl.888
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14114–14127
Language:
URL:
https://aclanthology.org/2023.findings-acl.888
DOI:
10.18653/v1/2023.findings-acl.888
Bibkey:
Cite (ACL):
Gengyu Wang, Kate Harwood, Lawrence Chillrud, Amith Ananthram, Melanie Subbiah, and Kathleen McKeown. 2023. Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence. In Findings of the Association for Computational Linguistics: ACL 2023, pages 14114–14127, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence (Wang et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-acl.888.pdf