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.

在低资源环境下，通过设计一种解耦响应解码器使模型可以仅从大量未接地对话和非结构化文档中学习，而只使用有限的训练示例就能很好地拟合剩余的小参数。在两个基准测试上的评估结果表明，我们的模型仅使用 1/8 的训练数据就可以实现最先进的性能，而且对领域外知识有很好的概括能力。