@inproceedings{vazquez-cerrillo-etal-2026-late,
    title = "{LATE}-iimas at {S}em{E}val-2026 Task 10: Conspiracy Detection via {D}e{BERT}a-v3 Ensemble and Weighted Loss Optimization",
    author = "Vazquez-Cerrillo, Jose  and
      Gomez-Adorno, Helena  and
      Bel-Enguix, Gemma",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.307/",
    pages = "2432--2437",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes the system developed by the LATE-iimas team for Task 10 of SemEval-2026: Psycomark, specifically for Subtask 2, which involves conspiracy detection. Our approach was based on fine-tuning the popular pre-trained language model DeBERTa-v3-Large. To address the challenges inherent in the provided dataset, such as class imbalance and the linguistic ambiguity of the ``Can{'}t tell'' label, we implemented a 5-Fold Stratified Cross-Validation technique combined with a Weighted Cross-Entropy Loss function. The final system, which operates using an ensemble of the resulting models, achieved a Weighted F1-Score of 0.75, placing it in the top 10 of the ranking."
}