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This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multidomain Spoken Dialogue System built on a distributed architecture. In the proposed framework, the domain selection problem is treated as sequential planning instead of classification, such that confirmation and clarification interaction mechanisms are supported. In addition, it is shown that by using a model parameter tying trick, the extensibility of the system can be preserved, where dialogue components in new domains can be easily plugged in, without re-training the domain selection policy. The experimental results based on human subjects suggest that the proposed model marginally outperforms a non-trivial baseline.

Policy Learning for Domain Selection in an Extensible Multi-domain Spoken Dialogue System