@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",
    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.",
}

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.
Sosa, Daniel Suresh, Malavika Potts, Christopher Altman, Russ
Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature