论文标题
情感生成模型的评论
A Review of Affective Generation Models
论文作者
论文摘要
情感计算是一个新兴的跨学科领域，开发了计算系统来分析，识别和影响人类的情感状态。通常可以将其分为两个子问题：情感识别和情感产生。在过去的十年中，情感识别已被多次进行了多次审查。然而，情感产生缺乏批判性审查。因此，我们建议对情感产生模型进行全面的审查，因为最常利用模型来影响他人的情绪状态。由于机器学习的飞跃，尤其是自2015年以来的深度学习，情感计算在各个领域和应用程序中都取得了动力。随着介绍的关键模型，这项工作被认为会受益于未来关于情感生成的研究。我们通过简要讨论现有挑战来结束这项工作。
Affective computing is an emerging interdisciplinary field where computational systems are developed to analyze, recognize, and influence the affective states of a human. It can generally be divided into two subproblems: affective recognition and affective generation. Affective recognition has been extensively reviewed multiple times in the past decade. Affective generation, however, lacks a critical review. Therefore, we propose to provide a comprehensive review of affective generation models, as models are most commonly leveraged to affect others' emotional states. Affective computing has gained momentum in various fields and applications, thanks to the leap of machine learning, especially deep learning since 2015. With critical models introduced, this work is believed to benefit future research on affective generation. We conclude this work with a brief discussion on existing challenges.
