Cooperative Dialogue Planning with User and Situation Models via Example-based Training

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 which incorporates user and situation models, making such dialogue adaptation possible. Manually developing a set of dialogue rules to accommodate all possible model combinations is very difficult and obstructs system portability. To overcome this problem, we propose a novel example-based training scheme, where example dialogues from a roleplaying simulation are used to train the dialogue planner via machine learning. 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 given scenario information. Experimental results show that the system successfully trains the dialogue planner and provides reasonable system performance.