Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature

TL;DR

This paper introduces a domain-specific pretraining approach for vertical search in biomedical literature, demonstrating competitive performance without relevance labels and scalable deployment on large datasets like PubMed.

Contribution

The paper presents a simple, label-free pretraining method tailored for biomedical search, achieving high performance and scalability in a real-world system.

Findings

Outperforms or matches top systems in TREC-COVID evaluation.

Scales to tens of millions of articles using cloud infrastructure.

Deployed as Microsoft Biomedical Search for real-world use.

Abstract

Information overload is a prevalent challenge in many high-value domains. A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months. In general, biomedical literature expands by two papers every minute, totalling over a million new papers every year. Search in the biomedical realm, and many other vertical domains is challenging due to the scarcity of direct supervision from click logs. Self-supervised learning has emerged as a promising direction to overcome the annotation bottleneck. We propose a general approach for vertical search based on domain-specific pretraining and present a case study for the biomedical domain. Despite being substantially simpler and not using any relevance labels for training or development, our method performs comparably or better than the best systems in the official TREC-COVID evaluation, a COVID-related biomedical search competition. Using distributed computing in modern cloud infrastructure, our system can scale to tens of millions of articles on PubMed and has been deployed as Microsoft Biomedical Search, a new search experience for biomedical literature: https://aka.ms/biomedsearch.