  Grounding dialogue system with external knowledge is a promising way to
improve the quality of responses. Most existing works adopt knowledge graphs
(KGs) as the external resources, paying attention to the contribution of
entities in the last utterance of the dialogue for context understanding and
response generation. Nevertheless, the correlations between knowledge implied
in the multi-turn context and the transition regularities between relations in
KGs are under-explored. To this end, we propose a Relation Transition aware
Knowledge-Grounded Dialogue Generation model (RT-KGD). Specifically, inspired
by the latent logic of human conversation, our model integrates dialogue-level
relation transition regularities with turn-level entity semantic information.
In this manner, the interaction between knowledge is considered to produce
abundant clues for predicting the appropriate knowledge and generating coherent
responses. The experimental results on both automatic evaluation and manual
evaluation indicate that our model outperforms state-of-the-art baselines.
