Abstract: Emotional dialogue is crucial in endowing conversational agents with the ability to handle emotions, aiming to equip them with capabilities in emotion recognition, understanding, and generation. To address the deficiency in the generation of emotions within emotional dialogue technology, the task of emotion generation has been proposed and developed as a core task in artificial intelligence for emotional expression. Its objective is to generate contextually appropriate artificial emotion categories and provide emotional guidance for relevant downstream tasks. Since 2018, researchers in the field of emotional dialogue have devoted themselves to enabling conversational agents to generate controllable emotions, exhibit strong empathy, and assist users in alleviating emotional distress. All these efforts highlight a strong demand for the task of emotion generation. Firstly, the definition and basic framework of the emotion generation task are provided, along with a brief introduction to the definitions of downstream tasks within the field of emotional dialogue. Secondly, the research status of the emotion generation task and its downstream tasks are summarized, along with commonly used datasets and evaluation metrics for this task. Finally, the challenges and future directions of the emotion generation task are outlined.

Key words: emotion generation, artificial emotion, emotional dialogue, affective computing

摘要： 情感对话是赋予对话机器人情感处理能力的关键，旨在使对话机器人具备情绪识别、情绪理解和情绪生成的能力。为了弥补情感对话技术中情绪生成能力的缺失，情绪生成任务被提出且已经发展为人工智能情绪表达的核心任务，其旨在生成符合语境的人工情绪类别，并为相关下游任务提供情绪指导。2018年至今，情感对话领域的研究人员致力于使对话机器人生成情绪可控、同理心强且能够帮助用户改善情绪痛苦的情绪响应，所有努力都表明了对情绪生成任务的强烈需求。给出了情绪生成任务定义与基本框架，并简要介绍了该任务在情感对话领域内下游任务的定义；总结了情绪生成任务及其下游任务的研究现状，并介绍了该任务常用数据集与评价指标。最后，分析了情绪生成任务的优势、面临的挑战以及未来发展方向。

关键词: 情感生成, 人工情感, 情感对话, 情感计算