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We present an evaluation of a spoken language dialogue system with a module for the management of userrelated information, stored as user preferences and privileges. The exibility of our dialogue management approach, based on Bayesian Networks (BN), together with a contextual information module, which performs different strategies for handling such information, allows us to include user information as a new level into the Context Manager hierarchy. We propose a set of objective and subjective metrics to measure the relevance of the different contextual information sources. The analysis of our evaluation scenarios shows that the relevance of the short-term information (i.e. the system status) remains pretty stable throughout the dialogue, whereas the dialogue history and the user prole (i.e. the middle-term and the long-term information, respectively) play a complementary role, evolving their usefulness as the dialogue evolves.

EVALUATION OF A USER-ADAPTED SPOKEN LANGUAGE DIALOGUE SYSTEM - Measuring the Relevance of the Contextual Information Sources