Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (11): 7-15.DOI: 10.3778/j.issn.1002-8331.1901-0056

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Review of Research on Task-Oriented Spoken Language Understanding

HOU Lixian, LI Yanling, LI Chengcheng   

  1. College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China
  • Online:2019-06-01 Published:2019-05-30



  1. 内蒙古师范大学 计算机科学技术学院,呼和浩特 010022

Abstract: Spoken Language Understanding(SLU), which includes two key sub-tasks slot filling and intent detection, is an important function module of the dialogue system. In recent years, joint recognition methods to solve slot filling and intent detection have become the mainstream methods of SLU. This paper introduces the methods of two sub-tasks, which develop from independent modeling to joint modeling. It focuses on the joint modeling methods based on deep neural network, analyzes current problems and future development trend of two sub-tasks.

Key words: slot filling, intent detection, joint recognition, deep neural network

摘要: 口语理解是对话系统重要的功能模块,语义槽填充和意图识别是面向任务口语理解的两个关键子任务。近年来,联合识别方法已经成为解决口语理解中语义槽填充和意图识别任务的主流方法,介绍两个任务由独立建模到联合建模的方法,重点介绍基于深度神经网络的语义槽填充和意图识别联合建模方法,并总结了目前存在的问题以及未来的发展趋势。

关键词: 语义槽填充, 意图识别, 联合识别, 深度神经网络