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.- Anthology ID:
- 2023.acl-short.61
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 694–713
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.61
- DOI:
- Cite (ACL):
- Daniel Sosa, Malavika Suresh, Christopher Potts, and Russ Altman. 2023. Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 694–713, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature (Sosa et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.acl-short.61.pdf