SLEDGE: A Simple Yet Effective Baseline for Coronavirus Scientific Knowledge Search

By Sean MacAvaney, Arman Cohan, Nazli Goharian. 2020

23 citations recorded by the source corpus.

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

Subfields: Molecular Biology

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Citations in the corpus

MS MARCO: Benchmarking Ranking Models in the Large-Data Regime (2021, methodology): “Similar to Med-MARCO [48, 49], Hamzei et al. [31] studies place-related subset of the MS MARCO dataset.”
Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset (2020, methodology): “Since in the beginning we did not have training data speciﬁc to COVID-19, we ﬁne-tuned our model on the MS MARCO passage dataset (Nguyen et al., 2016 We ﬁne-tuned our monoT5 model with a constant learning rate of 10 − 3 for 10k iterations with class-balanced batches of size 128.”