Dialogue Intent Classification with Long Short-Term Memory Networks

被引：7

作者：

Meng, Lian ^{[1
]}

Huang, Minlie ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China

来源：

NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2017 | 2018年 / 10619卷

关键词：

D O I：

10.1007/978-3-319-73618-1_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dialogue intent analysis plays an important role for dialogue systems. In this paper, we present a deep hierarchical LSTM model to classify the intent of a dialogue utterance. The model is able to recognize and classify user's dialogue intent in an efficient way. Moreover, we introduce a memory module to the hierarchical LSTM model, so that our model can utilize more context information to perform classification. We evaluate the two proposed models on a real-world conversational dataset from a Chinese famous e-commerce service. The experimental results show that our proposed model outperforms the baselines.

引用

页码：42 / 50

页数：9

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