  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.
