@inproceedings{concas-etal-2025-ilostthecode,
    title = "i{L}ost{T}he{C}ode at {S}em{E}val-2025 Task 10: Bottom-up Multilevel Classification of Narrative Taxonomies",
    author = "Concas, Lorenzo  and
      Sanguinetti, Manuela  and
      Atzori, Maurizio",
    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-1.85/",
    pages = "607--616",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes the approach used to address the task of narrative classification, which has been proposed as a subtask of Task 10 on Multilingual Characterization and Extraction of Narratives from Online News at the SemEval 2025 campaign. The task consists precisely in assigning all relevant sub-narrative labels from a two-level taxonomy to a given news article in multiple languages (i.e., Bulgarian, English, Hindi, Portuguese and Russian). This involves performing both multi-label and multi-class classification. The model developed for this purpose uses multiple pretrained BERT-based models to create contextualized embeddings that are concatenated and then fed into a simple neural network to compute classification probabilities. Results on the official test set, evaluated using samples {\$}F{\_}1{\$}, range from {\$}0.15{\$} in Hindi (rank {\#}9) to {\$}0.41{\$} in Russian (rank {\#}3). Besides an overview of the system and the results obtained in the task, the paper also includes some additional experiments carried out after the evaluation phase along with a brief discussion of the observed errors."
}