SLEDGE: A Simple Yet Effective Baseline for Coronavirus Scientific Knowledge Search
Year: 2020
Type: preprint
Abstract: 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 this https URL
Source: arXiv (Cornell University)
Authors: Sean MacAvaney, Arman Cohan, Nazli Goharian
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3022143756