This survey explores how Deep Learning has battled the COVID-19 pandemic and provides
directions for future research on COVID-19. We cover Deep Learning applications in Natural …

Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

Existing neural information retrieval (IR) models have often been studied in homogeneous
and narrow settings, which has considerably limited insights into their out-of-distribution …

Neural information retrieval (IR) has greatly advanced search and other knowledge-
intensive language tasks. While many neural IR methods encode queries and documents …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

Abstract The C ovid-19 Open Research Dataset (CORD-19) is a growing 1 resource of
scientific papers on C ovid-19 and related historical coronavirus research. CORD-19 is …

This paper introduces a simple yet effective query expansion approach, denoted as
query2doc, to improve both sparse and dense retrieval systems. The proposed method first …

Decoder transformers have continued increasing in scale reaching hundreds of billions of
parameters. Due to their scale the same decoder sets state-of-the-art results on various …

Dense retrieval approaches can overcome the lexical gap and lead to significantly improved
search results. However, they require large amounts of training data which is not available …