Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned
This work addresses the need for improved information access to scientific literature for domain experts during the COVID-19 pandemic, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of providing information access to the COVID-19 Open Research Dataset by deploying a neural search engine called Neural Covidex, which uses neural ranking architectures to help domain experts during the pandemic, though no concrete performance numbers are provided.
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.