@inproceedings{han-etal-2025-tue,
    title = "Tue-{JMS} at {S}em{E}val-2025 Task 11: {KR}e{L}ax: An Ensemble-Based Approach for Multilingual Emotion Detection and Addressing Data Imbalance",
    author = "Han, Jingyu  and
      Horikawa, Megan  and
      Lehtosalo, Suvi",
    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://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.113/",
    pages = "823--833",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion detection research has primarily focused on English, leaving a gap for low-resource languages. To address this, we present KReLaX, a multilingual ensemble model for multi-label emotion detection, combining three BERT-based encoders with a weighted voting layer. Within the shared task, our system performed well in multi-label classification, ranking 3rd in Tatar and achieving strong results in Hindi, Russian, Marathi, and Spanish. In emotion intensity classification, we achieved 6th place in Amharic and Hausa. While our system struggled in the zero-shot track, it achieved 7th place in Indonesian. These results highlight both the potential and the challenges of multilingual emotion detection, emphasizing the need for improved generalization in low-resource settings."
}