@inproceedings{gomez-navalon-etal-2026-umuteam,
    title = "{UMUT}eam at {S}em{E}val-2026 Task 10: Transformer Ensembles for Conspiratorial Span Extraction and Detection",
    author = "G{\'o}mez-Naval{\'o}n, Jorge  and
      Pan, Ronghao  and
      Bernal-Beltr{\'a}n, Tom{\'a}s  and
      Garc{\'i}a-D{\'i}az, Jos{\'e} Antonio  and
      Valencia-Garcia, Rafael",
    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-1.69/",
    pages = "483--489",
    ISBN = "979-8-89176-414-9",
    abstract = "Conspiracy theories pose significant societal risks and require reliable automated detection methods. In this paper, we present our system for SemEval 2026 Task 10, addressing both conspiracy detection and psycholinguistic marker extraction. We leverage multiple pretrained transformer architectures and ensemble strategies to model conspiratorial discourse at both document and token levels. For classification, our ensemble achieves a weighted F1-score of 0.7688, indicating effective performance in distinguishing conspiratorial statements. For marker extraction, we formulate the task as a BIOES sequence labeling problem and enhance predictions through ensemble and specialist models. Our results highlight both the effectiveness of transformer-based approaches and the challenges of fine-grained conspiracy marker extraction."
}