Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset

By Edwin Zhang, Nikhil Gupta, Raphael Tang, Xiao Han, Ronak Pradeep, Kuang Lu, Yue Zhang, Rodrigo Nogueira, Kyunghyun Cho, Hui Fang and 1 more. 2020

64 citations recorded by the source corpus.

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 multi-round TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the best systems. In round 3, we submitted the highest-scoring run that took advantage of previous training data and the second-highest fully automatic run. In rounds 4 and 5, we submitted the highest-scoring fully automatic runs.

Subfields: Artificial Intelligence, Information Retrieval & Search

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Citations in the corpus

Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face (2023, background): “in COVID-related datasets [33], news quotes [30], or medical literature [24].”
Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations (2021, methodology): “On top of PyGaggle, we have been working on faceted search interfaces to provide a complete end-to-end search application: this was initially demonstrated in our Covidex [31] search engine for COVID-19 scientiﬁc articles.”
Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations (2021, methodology): “Experimental results on two popular leaderboards show that our toolkit provides a firm foundation for building multi-stage ranking architectures for a range of retrieval tasks, for example, searching the COVID-19 literature [43] and podcasts [42].”
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime (2021, methodology): “quently, several groups benchmarking on TREC-COVID employed this subset for model training [46, 83, 88, 89], while others explored finetuning on the full MS MARCO for this task [5, 40, 46, 53, 63, 71].”