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In this work, we present an approach to take advantage of confidence measures obtained during the recognition and understanding processes of a dialog system, in order to guide the behavior of the dialog manager. Our approach allows the system to ask the user for confirmation about the data which have low confidence values associated to them, after the(More)
In this paper, we present a statistical approach for the development of a dialog manager and for learning optimal dialog strategies. This methodology is based on a classification procedure that considers all of the previous history of the dialog to select the next system answer. To evaluate the performance of the dialog system, the statistical approach for(More)
In this article, we present an approach for the construction of a stochastic dialog manager, in which the system answer is selected by means of a classification procedure. In particular, we use neural networks for the implementation of this classification process, which takes into account the data supplied by the user and the last system turn. The(More)
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(More)