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.- Anthology ID:
- 2020.emnlp-main.341
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4171–4179
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.341
- DOI:
- 10.18653/v1/2020.emnlp-main.341
- Cite (ACL):
- Sean MacAvaney, Arman Cohan, and Nazli Goharian. 2020. SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4171–4179, Online. Association for Computational Linguistics.
- Cite (Informal):
- SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search (MacAvaney et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.emnlp-main.341.pdf
- Data
- CORD-19, MS MARCO