Talk to Papers: Bringing Neural Question Answering to Academic Search
Tiancheng Zhao, Kyusong Lee · 2020
We introduce Talk to Papers 1 , 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.