SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search

• URL: http://arxiv.org/abs/2010.05987v1
• Date: Mon, 12 Oct 2020 19:28:29 GMT
• Title: SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search
• Abstract summary: We present a zero-shot ranking algorithm that adapts to COVID-related scientific literature. Despite not relying on TREC-COVID data, our method outperforms models that do.
• Score: 40.443716693689794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus. Clinicians, researchers, and policy-makers need to be able to search these articles effectively. In this work, we present a zero-shot ranking algorithm that adapts to COVID-related scientific literature. Our approach filters training data from another collection down to medical-related queries, uses a neural re-ranking model pre-trained on scientific text (SciBERT), and filters the target document collection. This approach ranks top among zero-shot methods on the TREC COVID Round 1 leaderboard, and exhibits a P@5 of 0.80 and an nDCG@10 of 0.68 when evaluated on both Round 1 and 2 judgments. Despite not relying on TREC-COVID data, our method outperforms models that do. As one of the first search methods to thoroughly evaluate COVID-19 search, we hope that this serves as a strong baseline and helps in the global crisis.

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