  Endowing chatbots with a consistent personality plays a vital role for agents
to deliver human-like interactions. However, existing personalized approaches
commonly generate responses in light of static predefined personas depicted
with textual description, which may severely restrict the interactivity of
human and the chatbot, especially when the agent needs to answer the query
excluded in the predefined personas, which is so-called out-of-predefined
persona problem (named OOP for simplicity). To alleviate the problem, in this
paper we propose a novel retrieval-to-prediction paradigm consisting of two
subcomponents, namely, (1) Persona Retrieval Model (PRM), it retrieves a
persona from a global collection based on a Natural Language Inference (NLI)
model, the inferred persona is consistent with the predefined personas; and (2)
Posterior-scored Transformer (PS-Transformer), it adopts a persona posterior
distribution that further considers the actual personas used in the ground
response, maximally mitigating the gap between training and inferring.
Furthermore, we present a dataset called IT-ConvAI2 that first highlights the
OOP problem in personalized dialogue. Extensive experiments on both IT-ConvAI2
and ConvAI2 demonstrate that our proposed model yields considerable
improvements in both automatic metrics and human evaluations.
