CUED Publications database

Distributed dialogue policies for multi-domain statistical dialogue management

Gasic, M and Kim, D and Tsiakoulis, P and Young, S (2015) Distributed dialogue policies for multi-domain statistical dialogue management. In: UNSPECIFIED pp. 5371-5375..

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© 2015 IEEE. Statistical dialogue systems offer the potential to reduce costs by learning policies automatically on-line, but are not designed to scale to large open-domains. This paper proposes a hierarchical distributed dialogue architecture in which policies are organised in a class hierarchy aligned to an underlying knowledge graph. This allows a system to be deployed using a modest amount of data to train a small set of generic policies. As further data is collected, generic policies can be adapted to give in-domain performance. Using Gaussian process-based reinforcement learning, it is shown that within this framework generic policies can be constructed which provide acceptable user performance, and better performance than can be obtained using under-trained domain specific policies. It is also shown that as sufficient in-domain data becomes available, it is possible to seamlessly improve performance, without subjecting users to unacceptable behaviour during the adaptation period and without limiting the final performance compared to policies trained from scratch.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:32
Last Modified: 22 May 2018 07:18