@inproceedings{eyasu-etal-2025-tewodros,
    title = "Tewodros at {S}em{E}val-2025 Task 11: Multilingual Emotion Intensity Detection using Small Language Models",
    author = "Eyasu, Mikiyas  and
      Abebaw, Wendmnew Sitot  and
      Hafeez, Nida  and
      Uroosa, Fatima  and
      Bizuneh, Tewodros Achamaleh  and
      Sidorov, Grigori  and
      Gelbukh, Alexander",
    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-1.196/",
    pages = "1485--1494",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotions play a fundamental role in the decision-making process, shaping human actions across diverse disciplines. The extensive usage of emotion intensity detection approaches has generated substantial research interest during the last few years. Efficient multi-label emotion intensity detection remains unsatisfactory even for high-resource languages, with a substantial performance gap among well-resourced and under-resourced languages. Team {\{}textbf{\{}Tewodros{\}}{\}} participated in SemEval-2025 Task 11, Track B, focusing on detecting text-based emotion intensity. Our work involved multi-label emotion intensity detection across three languages: Amharic, English, and Spanish, using the (afro-xlmr-large-76L), (DeBERTa-v3-base), and (BERT-base-Spanish-wwm-uncased) models. The models achieved an average F1 score of 0.6503 for Amharic, 0.5943 for English, and an accuracy score of 0.6228 for Spanish. These results demonstrate the effectiveness of our models in capturing emotion intensity across multiple languages."
}