COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic
Arkadiy Saakyan, Tuhin Chakrabarty, Smaranda Muresan
Abstract
We introduce a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domain-specific datasets for detecting misinformation.
BibTeX
@inproceedings{saakyan-etal-2021-covid,
title = "{COVID}-Fact: Fact Extraction and Verification of Real-World Claims on {COVID}-19 Pandemic",
author = "Saakyan, Arkadiy and
Chakrabarty, Tuhin and
Muresan, Smaranda",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.165/",
doi = "10.18653/v1/2021.acl-long.165",
pages = "2116--2129"
}