# Question Answering from Unstructured Text by Retrieval and Comprehension

Source: [Question Answering from Unstructured Text by Retrieval and Comprehension](<https://www.sota2.com/research/paper/68fa63391e209131ce9fd956>)

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

Data updated: 2025-10-25T20:36:15.201Z

Authors: Yusuke Watanabe, Bhuwan Dhingra, Ruslan Salakhutdinov

Published: 2017-03-26T23:48:06.000Z

## Abstract

Open domain Question Answering (QA) systems must interact with external knowledge sources, such as web pages, to find relevant information. Information sources like Wikipedia, however, are not well structured and difficult to utilize in comparison with Knowledge Bases (KBs). In this work we present a two-step approach to question answering from unstructured text, consisting of a retrieval step and a comprehension step. For comprehension, we present an RNN based attention model with a novel mixture mechanism for selecting answers from either retrieved articles or a fixed vocabulary. For retrieval we introduce a hand-crafted model and a neural model for ranking relevant articles. We achieve state-of-the-art performance on W IKI M OVIES dataset, reducing the error by 40%. Our experimental results further demonstrate the importance of each of the introduced components.

## 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/1703.08885>)

[PDF](<https://arxiv.org/pdf/1703.08885.pdf>)