论文标题
基于文本的问题从信息检索和深度神经网络的角度回答：调查
Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey
论文作者
论文摘要
基于文本的问题回答（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。通过信息检索技术对这一研究进行了广泛的研究，近年来通过考虑深层神经网络方法受到了越来越多的关注。深度学习方法是本文的主要重点，它提供了一种强大的技术，可以学习多层表示和文本之间的互动。在本文中，我们提供了针对质量保证任务提出的不同模型的全面概述，包括传统信息检索观点以及最新的深度神经网络观点。我们还介绍了众所周知的数据集，以进行任务，并在文献中提供了可用的结果，以在不同的技术之间进行比较。
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 techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between questions and texts. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both traditional information retrieval perspective, and more recent deep neural network perspective. We also introduce well-known datasets for the task and present available results from the literature to have a comparison between different techniques.
