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 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.
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