  Personalizing dialogue agents is important for dialogue systems to generate
more specific, consistent, and engaging responses. However, most current
dialogue personalization approaches rely on explicit persona descriptions
during inference, which severely restricts its application. In this paper, we
propose a novel approach that learns to predict persona information based on
the dialogue history to personalize the dialogue agent without relying on any
explicit persona descriptions during inference. Experimental results on the
PersonaChat dataset show that the proposed method can improve the consistency
of generated responses when conditioning on the predicted profile of the
dialogue agent (i.e. "self persona"), and improve the engagingness of the
generated responses when conditioning on the predicted persona of the dialogue
partner (i.e. "their persona"). We also find that a trained persona prediction
model can be successfully transferred to other datasets and help generate more
relevant responses.
