面向任务的基于深度学习的多轮对话系统与技术

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, chi-chat-oriented dialogue systems, and question-and-answer dialogue systems according 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.