Talk to Papers: Bringing Neural Question Answering to Academic Search
arXiv:2004.02002
Abstract
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 natural language processing research QA dataset via a community effort.
arXiv commentdemo paper accepted at ACL 2020
References in corpus (6)
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