Authors:
Hamid R. Chinaei
;
Brahim Chaib-draa
and
Luc Lamontagne
Affiliation:
Laval University, Canada
Keyword(s):
Learning, Spoken Dialogue Systems.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Applications
;
Artificial Intelligence
;
Cognitive Robotics
;
Conversational Agents
;
Industrial Applications of AI
;
Informatics in Control, Automation and Robotics
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Robotics and Automation
;
Soft Computing
;
Symbolic Systems
;
Uncertainty in AI
Abstract:
A common problem in spoken dialogue systems is finding the intention of the user. This problem deals with obtaining one or several topics for each transcribed, possibly noisy, sentence of the user. In this work, we apply the recent unsupervised learning method, Hidden Topic Markov Models (HTMM), for finding the intention of the user in dialogues. This technique combines two methods of Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM) in order to learn topics of documents. We show that HTMM can be also used for obtaining intentions for the noisy transcribed sentences of the user in spoken dialogue systems. We argue that in this way we can learn possible states in a speech domain which can be used in the design stage of its spoken dialogue system. Furthermore, we discuss that the learned model can be augmented and used in a POMDP (Partially Observable Markov Decision Process) dialogue manager of the spoken dialogue system.