  Responding with knowledge has been recognized as an important capability for
an intelligent conversational agent. Yet knowledge-grounded dialogues, as
training data for learning such a response generation model, are difficult to
obtain. Motivated by the challenge in practice, we consider knowledge-grounded
dialogue generation under a natural assumption that only limited training
examples are available. In such a low-resource setting, we devise a
disentangled response decoder in order to isolate parameters that depend on
knowledge-grounded dialogues from the entire generation model. By this means,
the major part of the model can be learned from a large number of ungrounded
dialogues and unstructured documents, while the remaining small parameters can
be well fitted using the limited training examples. Evaluation results on two
benchmarks indicate that with only 1/8 training data, our model can achieve the
state-of-the-art performance and generalize well on out-of-domain knowledge.
