  Knowledge graph-based dialogue systems are capable of generating more
informative responses and can implement sophisticated reasoning mechanisms.
However, these models do not take into account the sparseness and
incompleteness of knowledge graph (KG)and current dialogue models cannot be
applied to dynamic KG. This paper proposes a dynamic Knowledge graph-based
dialogue generation method with improved adversarial Meta-Learning (KDAD). KDAD
formulates dynamic knowledge triples as a problem of adversarial attack and
incorporates the objective of quickly adapting to dynamic knowledge-aware
dialogue generation. We train a knowledge graph-based dialog model with
improved ADML using minimal training samples. The model can initialize the
parameters and adapt to previous unseen knowledge so that training can be
quickly completed based on only a few knowledge triples. We show that our model
significantly outperforms other baselines. We evaluate and demonstrate that our
method adapts extremely fast and well to dynamic knowledge graph-based dialogue
generation.
