The ability to understand emotions is an essential component of human-like artificial intelligence, as emotions greatly influence human cognition, decision making, and social interactions. In addition to emotion recognition in conversations, the task of identifying the potential causes behind an individual's emotional state in conversations, is of great importance in many application scenarios. We organize SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, which aims at extracting all pairs of emotions and their corresponding causes from conversations. Under different modality settings, it consists of two subtasks: Textual Emotion-Cause Pair Extraction in Conversations (TECPE) and Multimodal Emotion-Cause Pair Extraction in Conversations (MECPE). The shared task has attracted 143 registrations and 216 successful submissions. In this paper, we introduce the task, dataset and evaluation settings, summarize the systems of the top teams, and discuss the findings of the participants.

翻译：理解情绪的能力是类人人工智能的重要组成部分，因为情绪极大地影响着人类的认知、决策和社会互动。除了对话中的情绪识别之外，识别对话中个体情绪状态背后潜在原因的任务在许多应用场景中至关重要。我们组织了 SemEval-2024 任务 3，命名为“对话中的多模态情绪原因分析”，其目标是从对话中提取所有情绪及其对应原因的对。在不同的模态设置下，它包含两个子任务：对话中的文本情绪-原因对抽取（TECPE）和对话中的多模态情绪-原因对抽取（MECPE）。该共享任务吸引了 143 个注册和 216 份成功提交。在本文中，我们介绍了任务、数据集和评估设置，总结了顶尖团队的系统，并讨论了参与者的发现。