  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.
