We propose Bernard: a framework for an engaging open-domain socialbot. While the task of open-domain dialog generation remains a difficult one, we explore various strategies to generate coherent dialog given an arbitrary dialog history. We incorporate a stateful autonomous dialog manager using non-deterministic finite automata to control multi-turn conversations. We show that powerful pretrained language models are capable of generating coherent and topical responses in the presence of grounding facts. Finally, we implement Acknowledge-Retrieve- Reply strategy to combine template-based and neural dialog generation for greater diversity and increased naturalness. Extensive human evaluation shows that the combination of generative models and retrieval models in a stateful dialog machine can achieve desired user experiences in terms of topic diversity and engagingness, as showed in extensive human evaluation.
