Coronavirus disease (COVID-19) has been declared as a pandemic by WHO with thousands of cases being reported each day. Numerous scientific articles are being published on the disease raising the need for a service which can organize, and query them in a reliable fashion. To support this cause we present AWS CORD-19 Search (ACS), a public, COVID-19 specific, neural search engine that is powered by several machine learning systems to support natural language based searches. ACS with capabilities such as document ranking, passage ranking, question answering and topic classification provides a scalable solution to COVID-19 researchers and policy makers in their search and discovery for answers to high priority scientific questions. We present a quantitative evaluation and qualitative analysis of the system against other leading COVID-19 search platforms. ACS is top performing across these systems yielding quality results which we detail with relevant examples in this work.
@article{arxiv.2007.09186,
title = {AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature},
author = {Parminder Bhatia and Lan Liu and Kristjan Arumae and Nima Pourdamghani and Suyog Deshpande and Ben Snively and Mona Mona and Colby Wise and George Price and Shyam Ramaswamy and Xiaofei Ma and Ramesh Nallapati and Zhiheng Huang and Bing Xiang and Taha Kass-Hout},
journal= {arXiv preprint arXiv:2007.09186},
year = {2020}
}