INTERSPEECH 2004 - ICSLP
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 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 used to train a dialogue planning scheme using a machine learning approach. 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.
Bibliographic reference. Lane, Ian Richard / Kawahara, Tatsuya / Ueno, Shinichi (2004): "Example-based training of dialogue planning incorporating user and situation models", In INTERSPEECH-2004, 2837-2840.