  How to build a high-quality multi-domain dialogue system is a challenging
work due to its complicated and entangled dialogue state space among each
domain, which seriously limits the quality of dialogue policy, and further
affects the generated response. In this paper, we propose a novel method to
acquire a satisfying policy and subtly circumvent the knotty dialogue state
representation problem in the multi-domain setting. Inspired by real school
teaching scenarios, our method is composed of multiple domain-specific teachers
and a universal student. Each individual teacher only focuses on one specific
domain and learns its corresponding domain knowledge and dialogue policy based
on a precisely extracted single domain dialogue state representation. Then,
these domain-specific teachers impart their domain knowledge and policies to a
universal student model and collectively make this student model a multi-domain
dialogue expert. Experiment results show that our method reaches competitive
results with SOTAs in both multi-domain and single domain setting.
