基于深度神经网络的对话系统研究综述

Review of Dialogue Systems Based on Deep Neural Networks

随着深度学习技术的崛起,自然语言处理应用取得了显著进展,特别是在对话系统研究中。为此,阐述对话系统基本流程,全面梳理基于深度学习的对话系统技术,包括卷积神经网络、循环神经网络和注意力机制三大类关键技术。同时,介绍3种模型的基本原理,并从信息抽取、对话状态追踪和对话生成方面深入分析比较了各基本模型及其衍生模型在对话任务上的应用、特点和优缺点。最后,指出对话任务中依旧存在的问题,并提出可行解决方案。

With the rise of deep learning technologies,significant advancements have been achieved in the field of natural language processing(NLP),particularly in the domain of dialogue systems.This paper begins by providing an overview of the fundamental processes involved in dialogue systems.Subsequently,it comprehensively reviews deep learning-based techniques for dialogue systems,encompassing three key categories:convolutional neural network(CNN),recurrent neural network(RNN),and attention mechanism(AM).The paper introduces the principles of these models,and then provides an in-depth analysis and comparison of the applications,characteristics,and advantages and disadvantages of various basic models and their derivative models in dialogue tasks form the perspectives of information extraction,dialogue state tracking,and dialogue generation.Finally,this paper enumerates persisting challenges within dialogue tasks,and proposes feasible solutions.