  Generating personalized responses is one of the major challenges in natural
human-robot interaction. Current researches in this field mainly focus on
generating responses consistent with the robot's pre-assigned persona, while
ignoring the user's persona. Such responses may be inappropriate or even
offensive, which may lead to the bad user experience. Therefore, we propose a
Bilateral Personalized Dialogue Generation (BPDG) method for dyadic
conversation, which integrates user and robot personas into dialogue generation
via designing a dynamic persona-aware fusion method. To bridge the gap between
the learning objective function and evaluation metrics, the Conditional Mutual
Information Maximum (CMIM) criterion is adopted with contrastive learning to
select the proper response from the generated candidates. Moreover, a bilateral
persona accuracy metric is designed to measure the degree of bilateral
personalization. Experimental results demonstrate that, compared with several
state-of-the-art methods, the final results of the proposed method are more
personalized and consistent with bilateral personas in terms of both automatic
and manual evaluations.
