RECURRENT CONVOLUTIONAL NEURAL NETWORKS FOR STRUCTURED SPEECH ACT TAGGING

被引：0

作者：

Ushio, Takashi ^{[1
]}

Shi, Hongjie ^{[1
]}

Endo, Mitsuru ^{[1
]}

Yamagami, Katsuyoshi ^{[1
]}

Horii, Noriaki ^{[1
]}

机构：

[1] Panasonic Corp, Interact AI Res Grp, Osaka, Japan

来源：

2016 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2016) | 2016年

关键词：

spoken language understanding; speech act tagging; text classification; multi-task learning; neural networks;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Spoken language understanding (SLU) is one of the important problem in natural language processing, and especially in dialog system. Fifth Dialog State Tracking Challenge (DSTC5) introduced a SLU challenge task, which is automatic tagging to speech utterances by two speaker roles with speech acts tag and semantic slots tag. In this paper, we focus on speech acts tagging. We propose local coactivate multi-task learning model for capturing structured speech acts, based on sentence features by recurrent convolutional neural networks. An experiment result, shows that our model outperformed all other submitted entries, and were able to capture coactivated local features of category and attribute, which are parts of speech act.

引用

页码：518 / 524

页数：7

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