  Despite the continuing efforts to improve the engagingness and consistency of
chit-chat dialogue systems, the majority of current work simply focus on
mimicking human-like responses, leaving understudied the aspects of modeling
understanding between interlocutors. The research in cognitive science,
instead, suggests that understanding is an essential signal for a high-quality
chit-chat conversation. Motivated by this, we propose P^2 Bot, a
transmitter-receiver based framework with the aim of explicitly modeling
understanding. Specifically, P^2 Bot incorporates mutual persona perception to
enhance the quality of personalized dialogue generation. Experiments on a large
public dataset, Persona-Chat, demonstrate the effectiveness of our approach,
with a considerable boost over the state-of-the-art baselines across both
automatic metrics and human evaluations.
