S ty E mp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement (2024)

Abstract: 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.

## introduction empathy and personality are pivotal factors in the development of human-like systems. empathy is the ability of humans to put themselves in another's position, which encompasses understanding another's experiences and feelings for responding appropriately. personality is the enduring patterns of thoughts, feelings, and behaviors that distinguish individuals from one another @xcite . empathy integrates cognition and emotion, involving understanding and responding emotionally to others' situations @xcite . consequently, prior research has focused on methods to generate empathetic responses by improving affective expression (lin et al., 2019; majumder et al., 2020; 1 we utilize nine empathetic intents from @xcite , which do not strictly adhere to the definition of empathetic, including sympathizing and agreeing. @xcite . in a given context, the user shows varying feelings to the system's responses, where the system encompasses empathetic expression and consistent personality traits, resulting in a more human-like interaction. @xcite , or exploring context understanding @xcite @xcite . however, as illustrated in figure 1 , individuals with different personalities can exhibit diverse empathy styles given identical contexts. previous methods for empathetic response generation did not consider the system's personalities, which leads to responses that may reflect empathy but lack personalization. systems that express a consistent personality are important for enhancing believability @xcite . as shown in figure 1 , when the system changes its personality in a conversation, it would make the interaction feel less human-like. moreover, an appropriate empathetic response may depend on the personality traits. richendoller and weaver @xcite examined the relationships between psychoticism, extraversion, and neuroticism and three styles of empathic intents: empathetic, perspective-taking, and sympathetic. their findings indicate that individuals with different personalities exhibit distinct preferences for empathetic intents, inspiring our motivation to consider the system's personality traits in empathetic response generation. however, the relationship between commonly-used big 5 @xcite ) / myers-briggs type indicator (mbti) @xcite personalities and empathetic intents has not been fully explored. to address this, we implicitly learn these connections through the prediction of both personality traits and empathetic signals in responses. empathetic signals include empathetic intents and empathetic communication mechanisms (ecm) @xcite -interpretations (ip), explorations (ex), and emotional reactions (er). further inspired by the prefix tuning method employed by @xcite and @xcite , we propose a multi-grained prefix encoder aimed at discerning personality traits alongside empathetic signals. because the empatheticdialogues dataset (ed) @xcite primarily targets expressing empathy rather than personality, it is hard to learn personality traits from a single response. to solve this problem, we utilize a pool of past utterances by the same listener to predict and encode personality traits. then, we propose a personality reinforcement (pr) module to calibrate the generation of empathetic responses by integrating explicitly personality traits. our main contributions are: ## preliminaries due to the lack of personality and empathetic signal annotations within the benchmark ed dataset, we train distinct models specialized for each aspect. ## proposed method figure 2 shows an overview of our proposed method which comprises two main components. firstly, a multi-grained prefix encoder is designed to implicitly learn the connections between personality traits and empathetic signals present in the system's response by multi-grained signals prediction and prefix encoding. secondly, we introduce a personality reinforcement mechanism aiming at integrating the generation of empathetic responses with explicit personality trait learning. ## conclusions and future work we have proposed styemp, which aims to stylize empathetic response generation with consistent personality. specifically, styemp 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 responses are both empathetic and reflective of the distinct personality. the experimental results demonstrate that our method outperforms other competitive methods on both automatic and human evaluations. the performance of our model is currently limited by the efficacy of the personality predictor. in future work, we plan to utilize ground-truth personality traits instead of predicted ones by annotating the dataset with personality labels.