@inproceedings{blombach-etal-2025-narrlangen,
    title = "Narrlangen at {S}em{E}val-2025 Task 10: Comparing (mostly) simple multilingual approaches to narrative classification",
    author = "Blombach, Andreas  and
      Doan Dang, Bao Minh  and
      Evert, Stephanie  and
      Fuchs, Tamara  and
      Heinrich, Philipp  and
      Kalashnikova, Olena  and
      Unjum, Naveed",
    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-1.291/",
    pages = "2240--2248",
    ISBN = "979-8-89176-273-2",
    abstract = "Our team focused on Subtask 2 (narrative classification) and tried several conceptually straightforward approaches: (1) prompt engineering of LLMs, (2) a zero-shot approach based on sentence similarities, (3) direct classification of fine-grained labels using SetFit, (4) fine-tuning encoder models on fine-grained labels, and (5) hierarchical classification using encoder models with two different classification heads. We list results for all systems on the development set, which show that the best approach was to fine-tune a pre-trained multilingual model, XLM-RoBERTa, with two additional linear layers and a softmax as classification head."
}