面向任务的基于深度学习的多轮对话系统与技术

自然语言是人类智慧的结晶,以自然语言的形式与计算机进行交互是人们长久以来的期待。随着自然语言处理技术的发展与深度学习方法的兴起,人机对话系统成为了新的研究热点。人机对话系统按照功能可以分为任务导向型对话系统、闲聊型对话系统、问答型对话系统。任务导向型对话系统是一种典型的人机对话系统,旨在帮助用户完成某些特定的任务,有着十分重要的学术意义和应用价值。文中系统地阐述了一种在实际工程应用中的任务导向型对话系统的通用框架,主要包括自然语言理解、对话管理以及自然语言生成3个部分;介绍了上述各部分所采用的经典深度学习和机器学习方法。最后,对自然语言理解任务进行了实证性的实验验证与分析,结果表明文中内容可以为任务导向型对话系统的构建提供有效指导。

Task-oriented Dialogue System and Technology Based on Deep Learning

Natural language is the crystallization of human wisdom,and interacting with computers in the form of natural language has long been expected.With the development of natural language processing technology and the rise of deep learning methods,human-computer dialogue systems have become a new research hotspot.Human-computer dialogue systems can be divided into task-oriented dialogue systems,chit-chat-oriented dialogue systems,and question-and-answer dialogue systems accor-ding to their functions.The task-oriented dialogue system is a typical man-machine dialogue system,which aims to help users complete certain specific tasks,and has very important academic significance and application value.This paper systematically illustrates the general framework of task-oriented dialogue systems in practical engineering applications,including natural language understanding,dialogue management,and natural language generation.Then,the classical deep learning and machine learning methods used in the above parts are introduced.Finally,the task of natural language understanding is empirically verified and analyzed.This paper can provide effective guidance for the construction of a task-oriented dialogue system.