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Maintaining consistent personas is essential for dialogue agents. Althoughtremendous advancements have been brought, the limited-scale of annotatedpersona-dense data are still barriers towards training robust and consistentpersona-based dialogue models. In this work, we show how the challenges can beaddressed by disentangling persona-based dialogue generation into two sub-taskswith a novel BERT-over-BERT (BoB) model. Specifically, the model consists of aBERT-based encoder and two BERT-based decoders, where one decoder is forresponse generation, and another is for consistency understanding. Inparticular, to learn the ability of consistency understanding from large-scalenon-dialogue inference data, we train the second decoder in an unlikelihoodmanner. Under different limited data settings, both automatic and humanevaluations demonstrate that the proposed model outperforms strong baselines inresponse quality and persona consistency.

BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data