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Statistical user simulation is a promising
methodology to train and evaluate the
performance of (spoken) dialog systems.
We work with a modular architecture for
data-driven simulation where the "intentional" component of user simulation includes a User Model representing userspecific features. We train a dialog simulator that combines traits of human behavior such as cooperativeness and context
with domain-related aspects via the
Expectation-Maximization algorithm. We
show that cooperativeness provides a finer
representation of the dialog context which
directly affects task completion rate.
CitationGonzález, M. [et al.]. Cooperative user models in statistical dialog simulators. A: Annual Meeting of the Special Interest Group on Discourse and Dialogue. "11th Annual Meeting of the Special Interest Group on Discourse and Dialogue". Tokyo: 2010, p. 217-220.
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