id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-119307-rlc2f6zw	Zhang, Edwin	Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset	2020-07-14		.txt	text/plain	4756	262	58	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. 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. 2. Leveraging our own infrastructure, we explored the use of sequence-to-sequence transformer models for text ranking, combined with a simple classification-based feedback approach to exploit existing relevance judgments. In the latest round 3 results, we report the highest-scoring run that exploits relevance judgments in a user feedback setting and the secondhighest fully automatic run, affirming the quality of our own ranking models (2). Despite the success of BERT for document ranking (Dai and Callan, 2019; MacAvaney et al., 2019; Yilmaz et al., 2019) , there is evidence that ranking with sequence-to-sequence models can achieve even better effectiveness, particularly in zero-shot and other settings with limited training data (Nogueira et al., 2020) , such as for TREC-COVID.	./cache/cord-119307-rlc2f6zw.txt	./txt/cord-119307-rlc2f6zw.txt