Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor

• URL: http://arxiv.org/abs/2412.17572v1
• Date: Mon, 23 Dec 2024 13:44:51 GMT
• Title: Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor
• Authors: Yeonju Kim, Se Jin Park, Yong Man Ro,
• Abstract summary: Our study introduces a dual approach: firstly, we employ Emotional Preference Optimization (EPO) to train chatbots. This training enables the model to discern fine distinctions between correct and counter-emotional responses. Secondly, we introduce MambaCompressor to effectively compress and manage extensive conversation histories. Our comprehensive experiments across multiple datasets demonstrate that our model significantly outperforms existing models in generating empathetic responses and managing lengthy dialogues.
• Score: 44.499778745131046
• License:
• Abstract: Chatbot research is advancing with the growing importance of chatbots in fields that require human interactions, such as customer support and mental health care. Despite these advancements, chatbots still face significant challenges in understanding subtle nuances and managing long conversation histories. To address these issues, our study introduces a dual approach: firstly, we employ Emotional Preference Optimization (EPO) to train chatbots not only with correct responses but also with counter-emotional responses-those that are contextually similar but emotionally divergent. This training enables the model to discern fine nuance distinctions between correct and counter-emotional responses, thereby enhancing the quality of its responses. Secondly, we introduce MambaCompressor to effectively compress and manage extensive conversation histories, significantly reducing time and memory complexities while improving the chatbot's contextual understanding. Our comprehensive experiments across multiple datasets demonstrate that our model significantly outperforms existing models in generating empathetic responses and efficiently managing lengthy dialogues.

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