# Talk to Papers: Bringing Neural Question Answering to Academic Search

Source: [Talk to Papers: Bringing Neural Question Answering to Academic Search](<https://www.sota2.com/research/paper/68fa67971e209131cea17488>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:18:12.808Z

Authors: Tianchang Zhao, Kyusong Lee

Published: 2020-04-04T19:19:55.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.02002>)

[PDF](<https://arxiv.org/pdf/2004.02002.pdf>)