Aspect-Based Passage Retrieval with Contextualized Discourse Vectors

Abstract

Passage retrieval is the task of retrieving only the portions of a document that are relevant to a particular information need. One application medical doctors and researchers face is the challenge of reading a large amount of novel literature. For example, since the outbreak of Coronavirus disease 2019 (COVID-19), tens of thousands of papers have been published each month about the disease. We demonstrate how we can support healthcare professionals in this exploratory research task with our neural passage retrieval system based on Contextualized Discourse Vectors (CDV). CDV captures the discourse of long documents on sentence level and allows to query a large corpus with medical entities and aspects. Our demonstration covers over 27,000 diseases and 14,000 clinical aspects including symptoms, diagnostics, treatments and medications. It returns passages and highlights sentences to effectively answer clinical queries with up to 65% Recall@1. We showcase our system on the COVID-19 Open Research Dataset (CORD-19), Orphanet and Wikipedia diseases corpora.1 https://allenai.org/. 2 https://covid19-research-explorer.appspot.com/.3 https://spike.covid-19.apps.allenai.org/search/covid19.