论文标题
hicem：人造情绪智力的高覆盖情绪模型
HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence
论文作者
论文摘要
当社会机器人和其他智能机器进入家中时，人工情感智力（AEI）正在焦点，以应对用户对更深入，更有意义的人类机器互动的渴望。为了完成这种有效的互动，下一代AEI需要全面的人类情感模型才能进行训练。与情感理论（一直是心理学的历史重点）不同，情感模型是一种描述性工具。在实践中，最强的模型需要强大的覆盖范围，这意味着定义最小的核心情感集可以从中得出所有其他情感。为了实现所需的覆盖范围，我们转向自然语言处理中的单词嵌入。我们的实验使用无监督的聚类技术表明，只有15个离散的情绪类别，我们可以在六种主要语言（阿拉伯语，中文，英语，法语，西班牙语和俄语）上提供最大的覆盖范围。为了支持我们的发现，我们还检查了两个大规模情感识别数据集的注释，以评估与人类观念的规模观念相比，评估现有情绪模型的有效性。由于强大的，全面的情感模型是开发现实世界情感计算应用的基础，因此这项工作对社会机器人技术，人机互动，心理保健和计算心理学具有广泛的影响。
As social robots and other intelligent machines enter the home, artificial emotional intelligence (AEI) is taking center stage to address users' desire for deeper, more meaningful human-machine interaction. To accomplish such efficacious interaction, the next-generation AEI need comprehensive human emotion models for training. Unlike theory of emotion, which has been the historical focus in psychology, emotion models are a descriptive tools. In practice, the strongest models need robust coverage, which means defining the smallest core set of emotions from which all others can be derived. To achieve the desired coverage, we turn to word embeddings from natural language processing. Using unsupervised clustering techniques, our experiments show that with as few as 15 discrete emotion categories, we can provide maximum coverage across six major languages--Arabic, Chinese, English, French, Spanish, and Russian. In support of our findings, we also examine annotations from two large-scale emotion recognition datasets to assess the validity of existing emotion models compared to human perception at scale. Because robust, comprehensive emotion models are foundational for developing real-world affective computing applications, this work has broad implications in social robotics, human-machine interaction, mental healthcare, and computational psychology.
