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We introduce Talk to Papers, which exploits the recent open-domain questionanswering (QA) techniques to improve the current experience of academic search.It's designed to enable researchers to use natural language queries to findprecise answers and extract insights from a massive amount of academic papers.We present a large improvement over classic search engine baseline on severalstandard QA datasets and provide the community a collaborative data collectiontool to curate the first natural language processing research QA dataset via acommunity effort.

Talk to Papers: Bringing Neural Question Answering to Academic Search