Detection of dialogue acts using perplexity-based word clustering

Authors:
Iosif Mporas;Dimitrios P. Lyras;Kyriakos N. Sgarbas;Nikos Fakotakis
Affiliations:
Wire Communications Laboratory, Electrical and Computer Engineering Department, University of Patras, Rion, Patras, Greece;Wire Communications Laboratory, Electrical and Computer Engineering Department, University of Patras, Rion, Patras, Greece;Wire Communications Laboratory, Electrical and Computer Engineering Department, University of Patras, Rion, Patras, Greece;Wire Communications Laboratory, Electrical and Computer Engineering Department, University of Patras, Rion, Patras, Greece
Venue:
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Year:
2007

Citing 4
Cited 0

First steps towards statistical modeling of dialogue to predict the speech act type of the next utterance

ISSD-93 Selected papers presented at the international symposium on Spoken dialogue
Algorithms for bigram and trigram word clustering

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Dialogue act modeling for automatic tagging and recognition of conversational speech

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Effective utterance classification with unsupervised phonotactic models

NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1

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Abstract

In the present work we used a word clustering algorithm based on the perplexity criterion, in a Dialogue Act detection framework in order to model the structure of the speech of a user at a dialogue system. Specifically, we constructed an n-gram based model for each target Dialogue Act, computed over the word classes. Then we evaluated the performance of our dialogue system on ten different types of dialogue acts, using an annotated database which contains 1,403,985 unique words. The results were very promising since we achieved about 70% of accuracy using trigram based models.