NDLI: Unsupervised state clustering for stochastic dialog management

Content Provider	IEEE Xplore Digital Library
Author	Lefevre, F. de More, R.
Copyright Year	2007
Description	Author affiliation: Avignon Univ., Avignon (Lefevre, F.; de More, R.)
Abstract	Following recent studies in stochastic dialog management, this paper introduces an unsupervised approach aiming at reducing the cost and complexity for the setup of a probabilistic POMDP-based dialog manager. The proposed method is based on a first decoding step deriving semantic basic constituents from user utterances. These isolated units and some relevant context features (as previous system actions, previous user utterances...) are combined to form vectors representing the on-going dialog states. After a clustering step, each partition of this space is intented to represent a particular dialog state. Then any new utterance can be classified according to these automatic states and the belief state can be updated before the POMDP-based dialog manager can take a decision on the best next action to perform. The proposed approach is applied to the French media task (tourist information and hotel booking). The media 10k-utterance training corpus is semantically rich (over 80 basic concepts) and is segmentally annotated in terms of basic concepts. Before user trials can be carried out, some insights on the method effectiveness are obtained by analysis of the convergence of the POMDP models.
Starting Page	550
Ending Page	555
File Size	191975
Page Count	6
File Format	PDF
ISBN	9781424417452
DOI	10.1109/ASRU.2007.4430171
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-09
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Speech Principal component analysis Costs Decoding Management training Convergence Natural languages Handicapped aids Computational modeling
Content Type	Text
Resource Type	Article

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