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        <title>Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature</title>
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        <namePart type="given">Daniel</namePart>
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    <name type="personal">
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        <namePart type="given">Russ</namePart>
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            <namePart type="given">Naoaki</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.acl-short.61</identifier>
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        <date>2023-07</date>
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%0 Conference Proceedings
%T Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature
%A Sosa, Daniel
%A Suresh, Malavika
%A Potts, Christopher
%A Altman, Russ
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sosa-etal-2023-detecting
%X 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.
%R 10.18653/v1/2023.acl-short.61
%U https://aclanthology.org/2023.acl-short.61
%U https://doi.org/10.18653/v1/2023.acl-short.61
%P 694-713

Markdown (Informal)

[Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature](https://aclanthology.org/2023.acl-short.61) (Sosa et al., ACL 2023)

• Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature (Sosa et al., ACL 2023)

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.