SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
This work addresses the problem of emotion detection for low-resource languages, providing datasets and benchmarks, but it is incremental as it builds on existing shared task frameworks.
The paper tackled the challenge of text-based emotion detection across over 30 low-resource languages by organizing a shared task with three tracks, attracting over 700 participants and resulting in baseline results and findings on best-performing systems.
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