  Dialogue systems dealing with multi-domain tasks are highly required. How to
record the state remains a key problem in a task-oriented dialogue system.
Normally we use human-defined features as dialogue states and apply a state
tracker to extract these features. However, the performance of such a system is
limited by the error propagation of a state tracker. In this paper, we propose
a dialogue generation model that needs no external state trackers and still
benefits from human-labeled semantic data. By using a teacher-student
framework, several teacher models are firstly trained in their individual
domains, learn dialogue policies from labeled states. And then the learned
knowledge and experience are merged and transferred to a universal student
model, which takes raw utterance as its input. Experiments show that the
dialogue system trained under our framework outperforms the one uses a belief
tracker.
