  Recent approaches for empathetic response generation mainly focus on
emotional resonance and user understanding, without considering the system's
personality. Consistent personality is evident in real human expression and is
important for creating trustworthy systems. To address this problem, we propose
StyEmp, which aims to stylize the empathetic response generation with a
consistent personality. Specifically, it incorporates a multi-grained prefix
mechanism designed to capture the intricate relationship between a system's
personality and its empathetic expressions. Furthermore, we introduce a
personality reinforcement module that leverages contrastive learning to
calibrate the generation model, ensuring that responses are both empathetic and
reflective of a distinct personality. Automatic and human evaluations on the
EMPATHETICDIALOGUES benchmark show that StyEmp outperforms competitive
baselines in terms of both empathy and personality expressions.
