Detecting contradictory COVID-19 drug efficacy claims from biomedical literature

Authors: Sosa, Daniel N. ;

Suresh, Malavika;

Potts, Christopher;

Altman, Russ B.

Source: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Full text

: https://doi.org/10.18653/v1/2023.acl-short.61

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.

Rights

https://creativecommons.org/licenses/by/4.0/

Cite as

Sosa, D., Suresh, M., Potts, C. & Altman, R. 2023, 'Detecting contradictory COVID-19 drug efficacy claims from biomedical literature', Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2, pp. 694-713. https://doi.org/10.18653/v1/2023.acl-short.61

Downloadable citations

HTML BIB RIS

Identifiers

DOI: https://doi.org/10.18653/v1/2023.acl-short.61

Repository URI: https://rgu-repository.worktribe.com/output/2098528