Talk to papers: Bringing neural question answering to academic search

Abstract

We introduce Talk to Papers1, 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.