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.

本文提出了基于弱监督学习的三阶段学习框架和一种变体 Transformer 来构建知识驱动的对话系统。评估结果表明该方法在少量训练数据甚至零资源的情况下仍然表现出色。