  We present SemEval-2024 Task 10, a shared task centred on identifying
emotions and finding the rationale behind their flips within monolingual
English and Hindi-English code-mixed dialogues. This task comprises three
distinct subtasks - emotion recognition in conversation for code-mixed
dialogues, emotion flip reasoning for code-mixed dialogues, and emotion flip
reasoning for English dialogues. Participating systems were tasked to
automatically execute one or more of these subtasks. The datasets for these
tasks comprise manually annotated conversations focusing on emotions and
triggers for emotion shifts (The task data is available at
https://github.com/LCS2-IIITD/EDiReF-SemEval2024.git). A total of 84
participants engaged in this task, with the most adept systems attaining
F1-scores of 0.70, 0.79, and 0.76 for the respective subtasks. This paper
summarises the results and findings from 24 teams alongside their system
descriptions.
