Kavli Affiliate: Yi Zhou
| First 5 Authors: Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Seid Muhie Yimam, Jan Philip Wahle
| Summary:
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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