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 spoken across various continents. The data instances are multi-labeled into six emotional classes, with additional datasets in 11 languages annotated for emotion intensity. Participants were asked to predict labels in three tracks: (a) emotion labels in monolingual settings, (b) emotion intensity scores, and (c) emotion labels in cross-lingual settings. The task attracted over 700 participants. We received final submissions from more than 200 teams and 93 system description papers. We report baseline results, as well as findings on the best-performing systems, the most common approaches, and the most effective methods across various tracks and languages. The datasets for this task are publicly available.

翻译：我们介绍了基于文本的情绪检测的共享任务，涵盖了来自七个不同语系的超过30种语言。这些语言主要为低资源语言，并在各大洲广泛使用。数据实例被多标签标注为六个情绪类别，另有11种语言的数据集标注了情绪强度。参与者被要求在三个赛道中进行预测：(a) 单语环境下的情绪标签，(b) 情绪强度分数，以及(c) 跨语言环境下的情绪标签。该任务吸引了超过700名参与者。我们收到了来自200多个团队的最终提交结果和93篇系统描述论文。我们报告了基线结果，以及关于各赛道和语言中表现最佳的系统、最常见的方法和最有效方法的发现。该任务的数据集已公开可用。