SemEval 2024——任务10：对话中的情绪发现及其翻转推理（EDiReF）
SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)
AI总结：
该研究提出 SemEval-2024 任务10（EDiReF），围绕英语及印地语-英语混合对话设置情绪识别与情绪翻转推理三个子任务，并基于人工标注数据评估24支团队的系统表现。
摘要：
我们介绍 SemEval-2024 任务10，这是一项共享任务，核心是在单语英语和印地语-英语代码混合对话中识别情绪，并找出情绪翻转背后的理据。该任务包含三个不同的子任务：代码混合对话中的对话情绪识别、代码混合对话的情绪翻转推理，以及英语对话的情绪翻转推理。参赛系统需要自动执行上述一个或多个子任务。这些任务的数据集由人工标注的对话组成，重点关注情绪以及情绪转变的触发因素（任务数据可在 https://github.com/LCS2-IIITD/EDiReF-SemEval2024.git 获取）。共有84名参与者参与了该任务，最熟练的系统在三个相应子任务上分别取得0.70、0.79和0.76的F1分数。本文总结了24支团队的结果、发现及其系统描述。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
