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. Our code is available at https://github.com/fuyahuii/StyEmp.

Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user’s experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user’s perspective, ignoring the system’s perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user’s perspective (user’s desires and reactions) and the system’s perspective (system’s intentions and reactions). We enhance ChatGPT’s ability to reason for the system’s perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations.

The author’s objective centers around developing a spoken dialogue system (SDS) that can emulate the cognitive and conversational qualities of a human friend. Key attributes such as empathy, knowledge/causality reasoning, and personality are integral components of human interaction. The proposed approach involves the creation of an Empathy-enriched SDS, capable of comprehending human emotions and circumstances, thus providing companionship and assistance akin to a trusted friend. Additionally, the Causality-reasoning for SDS aims to ground the system in commonsense knowledge and equip it with the ability to reason about causalities, such as predicting user desires/reactions and system intentions/reactions, thereby enhancing the system’s intelligence and human-like behavior. Finally, the concept of a Personality-conditioned SDS involves enabling systems to exhibit distinct personalities, further enhancing the naturalness of human-robot interaction.