Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
This work addresses the need for domain experts to efficiently search and access COVID-19 research data, though it is incremental as it builds on existing neural ranking and fusion-based methods.
The authors tackled the problem of providing information access to the COVID-19 Open Research Dataset by developing Covidex, a search engine using neural ranking models and keyword search infrastructure, which achieved the highest-scoring run in round 3 of the TREC-COVID challenge and the second-highest fully automatic run.
We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. Our system has been online and serving users since late March 2020. The Covidex is the user application component of our three-pronged strategy to develop technologies for helping domain experts tackle the ongoing global pandemic. In addition, we provide robust and easy-to-use keyword search infrastructure that exploits mature fusion-based methods as well as standalone neural ranking models that can be incorporated into other applications. These techniques have been evaluated in the ongoing TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the highest-scoring runs in rounds 1, 2, and 3. In round 3, we report the highest-scoring run that takes advantage of previous training data and the second-highest fully automatic run.