Emotions are essential for constructing social relationships between humans and interactive systems. Although emotional and empathetic dialogue generation methods have been proposed for dialogue systems, appropriate dialogue involves not only mirroring emotions and always being empathetic but also complex factors such as context. This paper proposes Emotion Regulation Chat (ER-Chat) as an end-to-end dialogue framework for emotion regulation. Emotion regulation is concerned with actions to approach appropriate emotional states. Learning appropriate emotion and intent when responding on the basis of the context of the dialogue enables the generation of more human-like dialogue. We conducted automatic and human evaluations to demonstrate the superiority of ER-Chat over the baseline system. The results show that inclusion of emotion and intent prediction mechanisms enable generation of dialogues with greater fluency, diversity, emotion awareness, and emotion appropriateness, which are greatly preferred by humans.

中文翻译：

---

ER-Chat：用于情绪调节的文本到文本开放域对话框架

情感对于构建人类与交互系统之间的社会关系至关重要。虽然已经为对话系统提出了情感和移情对话生成方法，但适当的对话不仅涉及镜像情感和始终移情，还涉及上下文等复杂因素。本文提出情绪调节聊天（ER-Chat）作为情绪调节的端到端对话框架。情绪调节与接近适当情绪状态的行为有关。在根据对话上下文做出响应时，学习适当的情绪和意图可以生成更像人类的对话。我们进行了自动和人工评估，以证明 ER-Chat 优于基线系统。