Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature
Daniel Sosa, Malavika Suresh, Christopher Potts, Russ Altman
Abstract
The COVID-19 pandemic created a deluge of questionable and contradictory scientific claims about drug efficacy – an “infodemic” with lasting consequences for science and society. In this work, we argue that NLP models can help domain experts distill and understand the literature in this complex, high-stakes area. Our task is to automatically identify contradictory claims about COVID-19 drug efficacy. We frame this as a natural language inference problem and offer a new NLI dataset created by domain experts. The NLI framing allows us to create curricula combining existing datasets and our own. The resulting models are useful investigative tools. We provide a case study of how these models help a domain expert summarize and assess evidence concerning remdisivir and hydroxychloroquine.
BibTeX
@inproceedings{sosa-etal-2023-detecting,
title = "Detecting Contradictory {COVID}-19 Drug Efficacy Claims from Biomedical Literature",
author = "Sosa, Daniel and
Suresh, Malavika and
Potts, Christopher and
Altman, Russ",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.61/",
doi = "10.18653/v1/2023.acl-short.61",
pages = "694--713"
}