@inproceedings{muhammad-etal-2025-semeval,
    title = "{S}em{E}val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection",
    author = "Muhammad, Shamsuddeen Hassan  and
      Ousidhoum, Nedjma  and
      Abdulmumin, Idris  and
      Yimam, Seid Muhie  and
      Wahle, Jan Philip  and
      Lima Ruas, Terry  and
      Beloucif, Meriem  and
      De Kock, Christine  and
      Belay, Tadesse Destaw  and
      Ahmad, Ibrahim Said  and
      Surange, Nirmal  and
      Teodorescu, Daniela  and
      Adelani, David Ifeoluwa  and
      Aji, Alham Fikri  and
      Ali, Felermino Dario Mario  and
      Araujo, Vladimir  and
      Ayele, Abinew Ali  and
      Ignat, Oana  and
      Panchenko, Alexander  and
      Zhou, Yi  and
      Mohammad, Saif",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.semeval-1.327/",
    pages = "2558--2569",
    ISBN = "979-8-89176-273-2",
    abstract = "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."
}