SLEDGE: A Simple Yet Effective Baseline for COVID-19 Scientific Knowledge Search
This provides a baseline tool for clinicians, researchers, and policy-makers to search COVID-19 scientific articles, but it is incremental as it adapts existing methods to a new domain.
The authors tackled the problem of searching the rapidly growing COVID-19 literature by developing SLEDGE, a search system that uses SciBERT for re-ranking articles, achieving a top leaderboard score of nDCG@10 0.6844 on the TREC-COVID challenge.
With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of literature on the virus. Clinicians, researchers, and policy-makers need a way to effectively search these articles. In this work, we present a search system called SLEDGE, which utilizes SciBERT to effectively re-rank articles. We train the model on a general-domain answer ranking dataset, and transfer the relevance signals to SARS-CoV-2 for evaluation. We observe SLEDGE's effectiveness as a strong baseline on the TREC-COVID challenge (topping the learderboard with an nDCG@10 of 0.6844). Insights provided by a detailed analysis provide some potential future directions to explore, including the importance of filtering by date and the potential of neural methods that rely more heavily on count signals. We release the code to facilitate future work on this critical task at https://github.com/Georgetown-IR-Lab/covid-neural-ir