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:
this https URL

本文提出了一种基于特定领域预训练的垂直搜索通用方法，并以生物医学领域为案例研究，使用自我监督学习的方法解决注释瓶颈问题，系统性能优于 TREC-COVID 比赛中的最佳系统，可扩展到 PubMed 上的数千万篇文章，并作为微软生物医学搜索的新搜索体验。