StyEmp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement
This work addresses the need for more trustworthy and human-like dialogue systems by adding personality to empathetic response generation, though it is incremental as it builds on existing emotional resonance methods.
The paper tackles the problem of generating empathetic responses with consistent personality in dialogue systems, proposing StyEmp which incorporates a multi-grained prefix encoder and personality reinforcement module. Results show StyEmp outperforms baselines on the EMPATHETICDIALOGUES benchmark in both empathy and personality metrics.
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.