ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework
Empathetic response generation necessitates the integration of emotional and intentional dynamics to foster meaningful interactions. Existing research either neglects the intricate interplay between emotion and intent, leading to suboptimal controllability of empathy, or resorts to large language models (LLMs), which incur significant computational overhead. In this paper, we introduce ReflectDiffu, a lightweight and comprehensive framework for empathetic response generation. This framework incorporates emotion contagion to augment emotional expressiveness and employs an emotion-reasoning mask to pinpoint critical emotional elements. Additionally, it integrates intent mimicry within reinforcement learning for refinement during diffusion. By harnessing an intent twice reflect the mechanism of Exploring-Sampling-Correcting, ReflectDiffu adeptly translates emotional decision-making into precise intent actions, thereby addressing empathetic response misalignments stemming from emotional misrecognition. Through reflection, the framework maps emotional states to intents, markedly enhancing both response empathy and flexibility. Comprehensive experiments reveal that ReflectDiffu outperforms existing models regarding relevance, controllability, and informativeness, achieving state-of-the-art results in both automatic and human evaluations.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingDialogue GenerationEmotion RecognitionEmpathetic Response GenerationResponse GenerationSentiment AnalysisText GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Studying The Effect of Emotional and Moral Language on Information Contagion during the Charlottesville Event
We highlight the contribution of emotional and moral language towards information contagion online. We find that retweet count on Twitter is significantly predicted by the use of negative emotions with negative moral lan…
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors
This study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative proble…
Contrastive LearningEEGEeg DecodingElectroencephalogram (EEG)+1Measuring Emotional Contagion in Social Media
Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the…
Uncertain Multimodal Intention and Emotion Understanding in the Wild
Understanding intention and emotion from social media poses unique challenges due to the inherent uncertainty in multimodal data, where posts often contain incomplete or missing modalities. While this uncertainty ref…
Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation
The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, t…
Decision MakingDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1