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

This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multi-domain Spoken Dialogue System built on a distributed architecture. In the proposed framework, the domain selection prob-lem is treated as sequential planning in-stead of classification, such that confir-mation and clarification interaction mech-anisms are supported. In addition, it is shown that by using a model parameter ty-ing trick, the extensibility of the system can be preserved, where dialogue com-ponents 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.