Text-based question answering from information retrieval and deep neural network perspectives: A survey,WIREs Data Mining and Knowledge Discovery

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Text-based question answering from information retrieval and deep neural network perspectives: A survey
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2021-05-24 , DOI: 10.1002/widm.1412
Zahra Abbasiantaeb ₁ , Saeedeh Momtazi ₁

Affiliation

Text-based question answering (QA) is a challenging task which aims at finding short concrete answers for users' questions. This line of research has been widely studied with information retrieval (IR) techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning (DL) approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between the questions and the answer sentences. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both a traditional IR perspective and a more recent deep neural network environment. We also introduce well-known datasets for the task and present available results from the literature to have a comparison between different techniques.

中文翻译：

从信息检索和深度神经网络的角度来看基于文本的问答：一项调查

基于文本的问答（QA）是一项具有挑战性的任务，旨在为用户的问题找到简短的具体答案。这方面的研究已经通过信息检索 (IR) 技术进行了广泛的研究，并且近年来通过考虑深度神经网络方法而受到越来越多的关注。深度学习 (DL) 方法是本文的主要焦点，它提供了一种强大的技术来学习问题和答案句子之间的多层表示和交互。在本文中，我们全面概述了为 QA 任务提出的不同模型，包括传统的 IR 视角和更新的深度神经网络环境。

更新日期：2021-05-24

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