In recent decades, the field of affective computing has made substantial progress in advancing the ability of AI systems to recognize and express affective phenomena, such as affect and emotions, during human-human and human-machine interactions. This paper describes our examination of research at the intersection of multimodal interaction and affective computing, with the objective of observing trends and identifying understudied areas. We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing: ACM International Conference on Multimodal Interaction, AAAC International Conference on Affective Computing and Intelligent Interaction, Annual Meeting of the Association for Computational Linguistics, and Conference on Empirical Methods in Natural Language Processing. We identified 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find that this body of research has primarily focused on enabling machines to recognize and express affect and emotion. However, we find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states. Based on our analysis, we discuss directions to expand the role of affective phenomena in multimodal interaction research.

翻译：近几十年来，情感计算领域在提升人工智能系统识别与表达情感现象（如情感和情绪）方面取得了显著进展，这些现象涉及人机交互及人际交互场景。本文通过审视多模态交互与情感计算交叉领域的研究，旨在观察发展趋势并识别研究不足的领域。我们分析了来自多模态交互、情感计算及自然语言处理领域相关会议的逾16，000篇论文，具体包括：ACM国际多模态交互会议、AAAC国际情感计算与智能交互会议、计算语言学协会年会及自然语言处理经验方法会议。从中筛选出910篇与情感相关的论文，并针对其中情感现象的作用展开分析。研究发现，该领域研究主要聚焦于让机器能够识别和表达情感与情绪，但关于AI系统如何利用情感与情绪预测来增强对人类社交行为及认知状态的理解方面，现有研究十分有限。基于此分析，我们探讨了拓展情感现象在多模态交互研究中作用的未来方向。