  With the availability of massive general-domain dialogue data, pre-trained
dialogue generation appears to be super appealing to transfer knowledge from
the general domain to downstream applications. In most existing work, such
transferable ability is mainly obtained by fitting a large model with hundreds
of millions of parameters on massive data in an exhaustive way, leading to
inefficient running and poor interpretability. This paper proposes a novel
dialogue generation model with a latent structure that is easily transferable
from the general domain to downstream tasks in a lightweight and transparent
way. Experiments on two benchmarks validate the effectiveness of the proposed
model. Thanks to the transferable latent structure, our model is able to yield
better dialogue responses than four strong baselines in terms of both automatic
and human evaluations, and our model with about 22% parameters particularly
delivers a 5x speedup in running time compared with the strongest baseline.
Moreover, the proposed model is explainable by interpreting the discrete latent
variables.
