TY - JOUR
AU - Sosa, Daniel N.
AU - Suresh, Malavika
AU - Potts, Christopher
AU - Altman, Russ B.
PY - 2023
DA - September
TI - Detecting contradictory COVID-19 drug efficacy claims from biomedical literature
JO - Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
EP - 713
VL - 2
DO - https://doi.org/10.18653/v1/2023.acl-short.61
AB - 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.
PB - ACL Association for Computational Linguistics
UR - https://rgu-repository.worktribe.com/output/2098528
KW - Coronavirus (COVID-19)
ER