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Recently, the practical deployment of open-domain dialogue systems has beenplagued by the knowledge issue of information deficiency and factualinaccuracy. To this end, we introduce PLATO-K based on two-stage dialogiclearning to strengthen internal knowledge memorization and external knowledgeexploitation. In the first stage, PLATO-K learns through massive dialoguecorpora and memorizes essential knowledge into model parameters. In the secondstage, PLATO-K mimics human beings to search for external information and toleverage the knowledge in response generation. Extensive experiments revealthat the knowledge issue is alleviated significantly in PLATO-K with suchcomprehensive internal and external knowledge enhancement. Compared to theexisting state-of-the-art Chinese dialogue model, the overall engagingness ofPLATO-K is improved remarkably by 36.2% and 49.2% on chit-chat andknowledge-intensive conversations.

PLATO-K: Internal and External Knowledge Enhanced Dialogue Generation