跨语言情绪检测挑战赛，覆盖30+低资源语言。
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
- 多语言多标签情绪识别，含11种语言的情绪强度标注
- 超过200支团队参与，最佳模型在跨语言任务中表现突出
- 公开数据集支持研究与应用，适合多语言情感分析方向
我们介绍了文本情绪检测的共享任务，涵盖来自七个语言家族的30多种语言，这些语言多为低资源语言，分布于各大洲。数据实例以六类情绪进行多标签标注，并在11种语言中额外标注了情绪强度。参赛者需完成三个赛道：(a) 多标签情绪检测，(b) 情绪强度评分检测，(c) 跨语言情绪检测。任务吸引了超过700名参与者，共收到200多个团队的最终提交及93篇系统描述论文。我们报告了基线结果，以及各赛道和语言中表现最佳系统的发现、主流方法与最有效策略。该任务的数据集已公开，获取地址：https://brighter-dataset.github.io
原文摘要 · Abstract (English)
We present our shared task on text-based emotion detection, covering more than 30 languages from seven distinct language families. These languages are predominantly low-resource and are spoken across various continents. The data instances are multi-labeled with six emotional classes, with additional datasets in 11 languages annotated for emotion intensity. Participants were asked to predict labels in three tracks: (a) multilabel emotion detection, (b) emotion intensity score detection, and (c) cross-lingual emotion detection. The task attracted over 700 participants. We received final submissions from more than 200 teams and 93 system description papers. We report baseline results, along with findings on the best-performing systems, the most common approaches, and the most effective methods across different tracks and languages. The datasets for this task are publicly available. The dataset is available at SemEval2025 Task 11 https://brighter-dataset.github.io
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