Using Information State to Improve Dialogue Move Identification in a Spoken Dialogue System (bibtex)
by Ai, Hua, Roque, Antonio, Leuski, Anton and Traum, David
Abstract:
In this paper we investigate how to improve the performance of a dialogue move and parameter tagger for a taskoriented dialogue system using the information-state approach. We use a corpus of utterances and information states from an implemented system to train and evaluate a tagger, and then evaluate the tagger in an on-line system. Use of information state context is shown to improve performance of the system.
Reference:
Using Information State to Improve Dialogue Move Identification in a Spoken Dialogue System (Ai, Hua, Roque, Antonio, Leuski, Anton and Traum, David), In Proceedings of the 10th Interspeech Conference, 2007.
Bibtex Entry:
@inproceedings{ai_using_2007,
	address = {Antwerp, Belgium},
	title = {Using {Information} {State} to {Improve} {Dialogue} {Move} {Identification} in a {Spoken} {Dialogue} {System}},
	url = {http://ict.usc.edu/pubs/Using%20Information%20State%20to%20Improve%20Dialogue%20Move%20Identification%20in%20a%20Spoken%20Dialogue%20System.pdf},
	abstract = {In this paper we investigate how to improve the performance of a dialogue move and parameter tagger for a taskoriented dialogue system using the information-state approach. We use a corpus of utterances and information states from an implemented system to train and evaluate a tagger, and then evaluate the tagger in an on-line system. Use of information state context is shown to improve performance of the system.},
	booktitle = {Proceedings of the 10th {Interspeech} {Conference}},
	author = {Ai, Hua and Roque, Antonio and Leuski, Anton and Traum, David},
	month = aug,
	year = {2007},
	keywords = {Virtual Humans}
}
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