Managing Unseen Situations in a Stochastic Dialog Model

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In this article, we present an approach for enriching a stochastic dialog manager to be able to manage unseen situations. As the model is estimated using a training corpus, the problem of augmenting the coverage of the model must be tackled. We modeled the problem of coverage as a classification problem, and we present several approaches for the definition of the classification function. This system has been developed in the DIHANA project, whose goal is the design and development of a dialog system to access a railway information system using spontaneous speech in Spanish. A corpus of 900 dialogs was acquired through the Wizard of Oz technique. An evaluation of these approaches is also presented.