Abstract: Spoken Language Understanding（SLU） is a vital part of the human-machine dialogue system, which includes an important sub-task called intent detection. The accuracy of intent detection is directly related to the performance of semantic slot filling, and it is helpful to the following research of the dialogue system. Considering the difficulty of intent detection in human-machine dialogue system, the traditional machine learning methods cannot understand the deep semantic information of user’s discourse. This paper mainly analyzes, compares and summarizes the deep learning methods applied in the research of intent detection in recent years, and further considers how to apply deep learning model to multi-intent detection task, so as to promote the research of multi-intent detection methods based on deep neural network.

Key words: intent detection, Spoken Language Understanding（SLU）, dialogue system, artificial intelligence

摘要： 口语理解是人机对话系统的重要组成部分，而意图识别是口语理解中的一个子任务，而且至关重要。意图识别的准确性直接关系到语义槽填充的性能并且有助于后续对话系统的研究。考虑到人机对话系统中意图识别的困难，传统的机器学习方法无法理解用户话语的深层语义信息，主要对近些年应用在意图识别研究方面的深度学习方法进行分析、比较和总结，进一步思考如何将深度学习模型应用到多意图识别任务中，从而推动基于深度神经网络的多意图识别方法的研究。

关键词: 意图识别, 口语理解, 对话系统, 人工智能