Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature
Daniel Sosa, Malavika Suresh, Christopher E. Potts, Russ Biagio Altman · 2023
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. 1