@article{Sosa_D-2023_149786,
title = {Detecting contradictory COVID-19 drug efficacy claims from biomedical literature},
author = {Sosa, D. and Suresh, M. and Potts, C. and Altman, R.},
month = {sep},
year = {2023},
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.},
pages = {694-713},
volume = {2},
journal = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
publisher = {ACL Association for Computational Linguistics},
url = {https://doi.org/10.18653/v1/2023.acl-short.61},
}
