Example-based training of dialogue planning incorporating user and situation models

To provide a high level of usability, spoken dialogue systems must generate cooperative responses for a wide variety of users and situations. We introduce a dialogue planning scheme incorporating user and situation models making such dialogue adaptation possible. Manually developing a set of dialogue rules to account for all possible model combinations, would be very difficult and obstruct system portability. To overcome this problem, we propose a novel example-based training scheme for dialogue planning, where example dialogues from a role-playing simulation are collected and a machine learning approach is used to train the dialogue planner. The proposed scheme is evaluated on the Kyoto city voice portal, a multi-domain spoken dialogue system. Subjects participated in a role-playing simulation where they selected appropriate system responses at each dialogue turn based on a given scenario. Experimental results show that the system successfully trains the dialogue planner and provides reasonable system performance.