Tetreault, JR and Litman, DJ
(2008)
A Reinforcement Learning approach to evaluating state representations in spoken dialogue systems.
Speech Communication, 50 (8-9).
683 - 696.
ISSN 0167-6393
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Abstract
Although dialogue systems have been an area of research for decades, finding accurate ways of evaluating different systems is still a very active subfield since many leading methods, such as task completion rate or user satisfaction, capture different aspects of the end-to-end human-computer dialogue interaction. In this work, we step back the focus from the complete evaluation of a dialogue system to presenting metrics for evaluating one internal component of a dialogue system: its dialogue manager. Specifically, we investigate how to create and evaluate the best state space representations for a Reinforcement Learning model to learn an optimal dialogue control strategy. We present three metrics for evaluating the impact of different state models and demonstrate their use on the domain of a spoken dialogue tutoring system by comparing the relative utility of adding three features to a model of user, or student, state. The motivation for this work is that if one knows which features are best to use, one can construct a better dialogue manager, and thus better performing dialogue systems. © 2008 Elsevier B.V. All rights reserved.
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