  Knowledge graph-based dialogue systems can narrow down knowledge candidates
for generating informative and diverse responses with the use of prior
information, e.g., triple attributes or graph paths. However, most current
knowledge graph (KG) cover incomplete domain-specific knowledge. To overcome
this drawback, we propose a knowledge graph based proactive dialogue generation
model (KgDg) with three components, improved model-agnostic meta-learning
algorithm (MAML), knowledge selection in knowledge triplets embedding, and
knowledge aware proactive response generator. For knowledge triplets embedding
and selection, we formulate it as a problem of sentence embedding to better
capture semantic information. Our improved MAML algorithm is capable of
learning general features from a limited number of knowledge graphs, which can
also quickly adapt to dialogue generation with unseen knowledge triplets.
Extensive experiments are conducted on a knowledge aware dialogue dataset
(DuConv). The results show that KgDg adapts both fast and well to knowledge
graph-based dialogue generation and outperforms state-of-the-art baseline.
