  Recently, the practical deployment of open-domain dialogue systems has been
plagued by the knowledge issue of information deficiency and factual
inaccuracy. To this end, we introduce PLATO-K based on two-stage dialogic
learning to strengthen internal knowledge memorization and external knowledge
exploitation. In the first stage, PLATO-K learns through massive dialogue
corpora and memorizes essential knowledge into model parameters. In the second
stage, PLATO-K mimics human beings to search for external information and to
leverage the knowledge in response generation. Extensive experiments reveal
that the knowledge issue is alleviated significantly in PLATO-K with such
comprehensive internal and external knowledge enhancement. Compared to the
existing state-of-the-art Chinese dialogue model, the overall engagingness of
PLATO-K is improved remarkably by 36.2% and 49.2% on chit-chat and
knowledge-intensive conversations.
