Semantic information and derivation rules for robust dialogue act detection in a spoken dialogue system

Wei Bin Liang; Chung-Hsien Wu; Chia Ping Chen

Semantic information and derivation rules for robust dialogue act detection in a spoken dialogue system

Wei Bin Liang, Chung-Hsien Wu, Chia Ping Chen

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

In this study, a novel approach to robust dialogue act detection for error-prone speech recognition in a spoken dialogue system is proposed. First, partial sentence trees are proposed to represent a speech recognition output sentence. Semantic information and the derivation rules of the partial sentence trees are extracted and used to model the relationship between the dialogue acts and the derivation rules. The constructed model is then used to generate a semantic score for dialogue act detection given an input speech utterance. The proposed approach is implemented and evaluated in aMandarin spoken dialogue system for tour-guiding service. Combined with scores derived from the ASR recognition probability and the dialogue history, the proposed approach achieves 84.3% detection accuracy, an absolute improvement of 34.7% over the baseline of the semantic slot-based method with 49.6% detection accuracy.

Original language	English
Title of host publication	ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publication	Human Language Technologies
Pages	603-608
Number of pages	6
Volume	2
Publication status	Published - 2011
Event	49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 - Portland, OR, United States Duration: 2011 Jun 19 → 2011 Jun 24

Other

Other	49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
Country/Territory	United States
City	Portland, OR
Period	11-06-19 → 11-06-24

All Science Journal Classification (ASJC) codes

Language and Linguistics
Linguistics and Language

Cite this

@inproceedings{de02062efff5431c8a841706ed2ef8d2,

title = "Semantic information and derivation rules for robust dialogue act detection in a spoken dialogue system",

abstract = "In this study, a novel approach to robust dialogue act detection for error-prone speech recognition in a spoken dialogue system is proposed. First, partial sentence trees are proposed to represent a speech recognition output sentence. Semantic information and the derivation rules of the partial sentence trees are extracted and used to model the relationship between the dialogue acts and the derivation rules. The constructed model is then used to generate a semantic score for dialogue act detection given an input speech utterance. The proposed approach is implemented and evaluated in aMandarin spoken dialogue system for tour-guiding service. Combined with scores derived from the ASR recognition probability and the dialogue history, the proposed approach achieves 84.3% detection accuracy, an absolute improvement of 34.7% over the baseline of the semantic slot-based method with 49.6% detection accuracy.",

author = "Liang, {Wei Bin} and Chung-Hsien Wu and Chen, {Chia Ping}",

year = "2011",

language = "English",

isbn = "9781932432886",

volume = "2",

pages = "603--608",

booktitle = "ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics",

note = "49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 ; Conference date: 19-06-2011 Through 24-06-2011",

}

Liang, WB, Wu, C-H & Chen, CP 2011, Semantic information and derivation rules for robust dialogue act detection in a spoken dialogue system. in ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. vol. 2, pp. 603-608, 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011, Portland, OR, United States, 11-06-19.

Semantic information and derivation rules for robust dialogue act detection in a spoken dialogue system. / Liang, Wei Bin; Wu, Chung-Hsien; Chen, Chia Ping.

ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Vol. 2 2011. p. 603-608.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Semantic information and derivation rules for robust dialogue act detection in a spoken dialogue system

AU - Liang, Wei Bin

AU - Wu, Chung-Hsien

AU - Chen, Chia Ping

PY - 2011

Y1 - 2011

N2 - In this study, a novel approach to robust dialogue act detection for error-prone speech recognition in a spoken dialogue system is proposed. First, partial sentence trees are proposed to represent a speech recognition output sentence. Semantic information and the derivation rules of the partial sentence trees are extracted and used to model the relationship between the dialogue acts and the derivation rules. The constructed model is then used to generate a semantic score for dialogue act detection given an input speech utterance. The proposed approach is implemented and evaluated in aMandarin spoken dialogue system for tour-guiding service. Combined with scores derived from the ASR recognition probability and the dialogue history, the proposed approach achieves 84.3% detection accuracy, an absolute improvement of 34.7% over the baseline of the semantic slot-based method with 49.6% detection accuracy.

AB - In this study, a novel approach to robust dialogue act detection for error-prone speech recognition in a spoken dialogue system is proposed. First, partial sentence trees are proposed to represent a speech recognition output sentence. Semantic information and the derivation rules of the partial sentence trees are extracted and used to model the relationship between the dialogue acts and the derivation rules. The constructed model is then used to generate a semantic score for dialogue act detection given an input speech utterance. The proposed approach is implemented and evaluated in aMandarin spoken dialogue system for tour-guiding service. Combined with scores derived from the ASR recognition probability and the dialogue history, the proposed approach achieves 84.3% detection accuracy, an absolute improvement of 34.7% over the baseline of the semantic slot-based method with 49.6% detection accuracy.

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M3 - Conference contribution

AN - SCOPUS:84859065910

SN - 9781932432886

VL - 2

SP - 603

EP - 608

BT - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics

T2 - 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011

Y2 - 19 June 2011 through 24 June 2011

ER -

Semantic information and derivation rules for robust dialogue act detection in a spoken dialogue system

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