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Self-supervised Answer Retrieval on Clinical Notes
[article]
2021
arXiv
pre-print
Retrieving answer passages from long documents is a complex task requiring semantic understanding of both discourse and document context. We approach this challenge specifically in a clinical scenario, where doctors retrieve cohorts of patients based on diagnoses and other latent medical aspects. We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. In addition, we contribute a novel retrieval dataset based on
arXiv:2108.00775v1
fatcat:dku5pllocnb2xeserzsenvy7dm