SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
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2025
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| author | Muhammad, Shamsuddeen Hassan Ousidhoum, Nedjma Abdulmumin, Idris Yimam, Seid Muhie Wahle, Jan Philip Ruas, Terry Beloucif, Meriem De Kock, Christine Belay, Tadesse Destaw Ahmad, Ibrahim Said Surange, Nirmal Teodorescu, Daniela Adelani, David Ifeoluwa Aji, Alham Fikri Ali, Felermino Araujo, Vladimir Ayele, Abinew Ali Ignat, Oana Panchenko, Alexander Zhou, Yi Mohammad, Saif M. |
| author_facet | Muhammad, Shamsuddeen Hassan Ousidhoum, Nedjma Abdulmumin, Idris Yimam, Seid Muhie Wahle, Jan Philip Ruas, Terry Beloucif, Meriem De Kock, Christine Belay, Tadesse Destaw Ahmad, Ibrahim Said Surange, Nirmal Teodorescu, Daniela Adelani, David Ifeoluwa Aji, Alham Fikri Ali, Felermino Araujo, Vladimir Ayele, Abinew Ali Ignat, Oana Panchenko, Alexander Zhou, Yi Mohammad, Saif M. |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_07269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection Muhammad, Shamsuddeen Hassan Ousidhoum, Nedjma Abdulmumin, Idris Yimam, Seid Muhie Wahle, Jan Philip Ruas, Terry Beloucif, Meriem De Kock, Christine Belay, Tadesse Destaw Ahmad, Ibrahim Said Surange, Nirmal Teodorescu, Daniela Adelani, David Ifeoluwa Aji, Alham Fikri Ali, Felermino Araujo, Vladimir Ayele, Abinew Ali Ignat, Oana Panchenko, Alexander Zhou, Yi Mohammad, Saif M. Computation and Language 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 |
| title | SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.07269 |