StyEmp：通过多粒度前缀编码器和人格强化实现共情回复生成的风格化
StyEmp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement
AI总结：
本文提出 StyEmp，通过多粒度前缀机制和基于对比学习的人格强化模块，使共情回复兼具情感理解与一致人格，并在 EMPATHETICDIALOGUES 上验证其优于基线。
摘要：
近期的共情回复生成方法主要关注情感共鸣和用户理解，而未考虑系统的人格。一致的人格在真实人类表达中显而易见，并且对于构建值得信赖的系统十分重要。为解决这一问题，我们提出 StyEmp，旨在以一致的人格使共情回复生成风格化。具体而言，该方法引入多粒度前缀机制，用于捕捉系统人格与其共情表达之间的复杂关系。此外，我们提出人格强化模块，利用对比学习校准生成模型，确保回复既具有共情性，又能体现鲜明人格。在 EMPATHETICDIALOGUES 基准上的自动评估和人工评估表明，StyEmp 在共情和人格表达方面均优于具有竞争力的基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
