@inproceedings{kent-nindel-2025-itf,
    title = "{ITF}-{NLP} at {S}em{E}val-2025 Task 11 An Exploration of {E}nglish and {G}erman Multi-label Emotion Detection using Fine-tuned Transformer Models",
    author = "Kent, Samantha  and
      Nindel, Theresa",
    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/corrections-2025-08/2025.semeval-1.193/",
    pages = "1465--1472",
    ISBN = "979-8-89176-273-2",
    abstract = "We present our submission to Task 11, Bridging the Gap in Text-Based Emotion Detection, of the 19th International Workshop on Semantic Evaluation (SemEval) 2025. We participated in track A, multi-label emotion detection, in both German and English. Our approach is based on fine-tuning transformer models for each language, and our models achieve a Macro F1 of 0.75 and 0.62 for English and German respectively. Furthermore, we analyze the data available for training to gain insight into the model predictions."
}