User modeling in spoken dialogue systems for flexible guidance generation

We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems. Unlike previous studies that focus on users' knowledge or typical kinds of users, the proposed user model is more comprehensive. Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and degree of hastiness. Moreover, the models are automatically derived by decision tree learning using real dialogue data. We obtained reasonable classification accuracy for all dimensions. Dialogue strategies based on the user modeling are implemented in Kyoto city bus information system that has been developed at our laboratory. Experimental evaluation shows that the cooperative responses adaptive to individual users serve as good guidance for novice users without increasing the dialogue duration for skilled users.