Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned

TL;DR

This paper introduces Neural Covidex, a neural search engine designed for rapid deployment to access COVID-19 research data, aiming to assist experts in evidence-based decision making during the pandemic.

Contribution

The paper presents a neural search engine for COVID-19 literature that was quickly developed and deployed, highlighting lessons learned in rapid AI tool deployment.

Findings

Successful deployment of a neural search engine for COVID-19 research

Insights into rapid development and deployment of AI tools during a pandemic

Potential to improve scientific literature access for domain experts

Abstract

We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. This web application exists as part of a suite of tools that we have developed over the past few weeks to help domain experts tackle the ongoing global pandemic. We hope that improved information access capabilities to the scientific literature can inform evidence-based decision making and insight generation. This paper describes our initial efforts and offers a few thoughts about lessons we have learned along the way.