Review of Intent Detection Methods in the Human-Machine Dialogue System

被引:30
作者
Liu, Jiao [1 ]
Li, Yanling [1 ]
Lin, Min [1 ]
机构
[1] Inner Mongolia Normal Univ, Coll Comp Sci & Technol, Hohhot, Peoples R China
来源
2019 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, AUTOMATION AND CONTROL TECHNOLOGIES (AIACT 2019) | 2019年 / 1267卷
基金
中国国家自然科学基金;
关键词
D O I
10.1088/1742-6596/1267/1/012059
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Spoken language understanding is an important part of the human-machine dialogue system, intent detection is a sub-task of spoken language understanding, and it is very important. 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 method 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.
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页数:10
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