Talk to Papers: Bringing Neural Question Answering to Academic Search

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.

demo paper accepted at ACL 2020

References in corpus (2)

• Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
• Learning Autocomplete Systems as a Communication Game

Cited by in corpus (1)

• Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation