  Neural conversation models have shown great potentials towards generating
fluent and informative responses by introducing external background knowledge.
Nevertheless, it is laborious to construct such knowledge-grounded dialogues,
and existing models usually perform poorly when transfer to new domains with
limited training samples. Therefore, building a knowledge-grounded dialogue
system under the low-resource setting is a still crucial issue. In this paper,
we propose a novel three-stage learning framework based on weakly supervised
learning which benefits from large scale ungrounded dialogues and unstructured
knowledge base. To better cooperate with this framework, we devise a variant of
Transformer with decoupled decoder which facilitates the disentangled learning
of response generation and knowledge incorporation. Evaluation results on two
benchmarks indicate that our approach can outperform other state-of-the-art
methods with less training data, and even in zero-resource scenario, our
approach still performs well.
