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Talk to Papers: Bringing Neural Question Answering to Academic Search
[article]
2020
arXiv
pre-print
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We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It's designed to enable researchers to use natural language queries to find precise answers and extract insights from a massive amount of academic papers. We present a large improvement over classic search engine baseline on several standard QA datasets and provide the community a collaborative data collection tool to curate the first
arXiv:2004.02002v3
fatcat:wa4pb3xsujavljo5q3k234cqh4