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.

本文提出一种通过领域专家教师和一个通用学生模型共同编制一个多领域对话管理器的新方法，解决了多领域对话系统中复杂的对话状态空间和策略生成质量受限的问题。实验结果表明，该方法在多领域和单领域设置中的结果与现有技术水平竞争力相当。