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.

翻译：我们提出SemEval-2024任务10，这是一项以识别单语英语和印地语-英语代码混合对话中的情感并探索其翻转原因为核心的共享任务。该任务包含三个不同的子任务——代码混合对话中的情感识别、代码混合对话中的情感翻转推理以及英语对话中的情感翻转推理。参与系统需自动执行其中一个或多个子任务。这些任务的数据集包含以情感及情感转变触发因素为重点的人工标注对话（任务数据可通过https://github.com/LCS2-IIITD/EDiReF-SemEval2024.git获取）。共有84名参与者参与此项任务，表现最佳的三个系统在相应子任务中分别取得了0.70、0.79和0.76的F1分数。本文总结了24个参赛队伍的结果与发现及其系统描述。