Moving from limited-domain dialogue systems to open domain dialogue systems raises a number of challenges. One of them is the ability of the system to utilise small amounts of data from disparate domains to build a dialogue manager policy. Previous work has focused on using data from different domains to adapt a generic policy to a specific domain. Inspired by Bayesian committee machines, this paper proposes the use of a committee of dialogue policies. The results show that such a model is particularly beneficial for adaptation in multi-domain dialogue systems. The use of this model significantly improves performance compared to a single policy baseline, as confirmed by the performed real-user trial. This is the first time a dialogue policy has been trained on multiple domains on-line in interaction with real users.
REF presented policy committee for adaptation in multi-domain dialogue User Dialogue Action Inform(depart_city=Seattle) User Goal system based on Bayesian committee machine.
Policy committee for adaptation in multi-domain spoken dialogue systems
2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)
2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)
Computer Science