State-of-the-art neural dialogue systems excel at syntactic and semantic modelling of language, but often have a hard time establishing emotional alignment with the human interactant during a conversation. In this work, we bring Affect Control Theory (ACT), a socio-mathematical model of emotions for human-human interactions, to the neural dialogue generation setting. ACT makes predictions about how humans respond to emotional stimuli in social situations. Due to this property, ACT and its derivative probabilistic models have been successfully deployed in several applications of Human-Computer Interaction, including empathetic tutoring systems, assistive healthcare devices and two-person social dilemma games. We investigate how ACT can be used to develop affect-aware neural conversational agents, which produce emotionally aligned responses to prompts and take into consideration the affective identities of the interactants.

中文翻译：

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使用影响控制理论在对话中产生情绪一致的反应

最先进的神经对话系统擅长语言的句法和语义建模，但通常很难在对话期间与人类互动者建立情感一致。在这项工作中，我们将情感控制理论 (ACT)，一种用于人与人互动的情绪社会数学模型，引入神经对话生成设置。ACT 预测人类在社交场合对情绪刺激的反应。由于这一特性，ACT 及其衍生概率模型已成功部署在人机交互的多个应用中，包括移情辅导系统、辅助医疗设备和两人社交困境游戏。我们研究了 ACT 如何用于开发情感感知神经会话代理，