基于上下文信息的口语意图检测方法

doi:10.11896/jsjkx.181202269

Abstract

Abstract: In recent years,with the development of artificial intelligence and the popularization of smart devices,human-computer intelligent dialogue technology has received extensive attention.Spoken language understanding is an important task dialogue system,and spoken language intention detection is a key technology in spoken language understanding.Due to complex language phenomena such as semantic missing,frame representation and intent conversion in multiple rounds of dialogue,the intent detection task for spoken language is very challenging.In order to solve the above problems,a gated mechanism based information sharing neural network method was proposed in this paper,which can take advantages of contextual information in multiple rounds of dialogue to improve detection performance.Specifically,first the current round text and context text initial representation are constructed in combination with the phonetic features to reduce the impact of speech recognition errors on semantic representation.Secondly,a semantic encoder based on hierarchical attention mechanism is used to obtain deep semantic representations of the current round and contextual text,including multi-level semantic information from word to sentence to multiple rounds of text.Finally,the gated mechaniam based information sharing neural network is constructed to use the context semantic information to help the intent detection of the current round of text.The experimental results show that the proposed method can effectively use context information to improve the detection of spoken language intentions,and achieves 88.1% accuracy and 88.0% F1 value in dataset of CCKS2018 shared task-2,which is significantly improved performance compared with the existing methods.

Key words: Context information, Gated neural network, Intent detection, Spoken language understanding

CLC Number:

TP391

XU Yang,WANG Jian-cheng,LIU Qi-yuan,LI Shou-shan. Intention Detection in Spoken Language Based on Context Information[J].Computer Science, 2020, 47(1): 205-211.

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