@inproceedings{akrap-etal-2026-fer,
    title = "{FER} at {S}em{E}val-2026 Task 6: Analysis of Different Approaches to Unmasking Political Question Evasions",
    author = "Akrap, Matija  and
      Bili{\'c}, Andrija  and
      {\v{S}}impraga, Roko  and
      Ra{\v{c}}i{\'c}, Fran  and
      {\v{C}}uturilo, Luka",
    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-1.5/",
    pages = "30--36",
    ISBN = "979-8-89176-414-9",
    abstract = "We tackle classifying evasive political answerswithin the context of SemEval-2026 Task 6 andcompare three modeling strategies: a flat base-line, a hierarchical cascade, and a multitasklearning approach. Our experiments demon-strate that a hierarchical RoBERTa-base modelachieves the best performance, particularly byleveraging the distinctiveness of the class ClearNon-Reply. Conversely, we find that stan-dard multitask learning frequently producesstructurally invalid label combinations in a sig-nificant fraction of predictions. Our demon-strations show that applying a constrained in-ference mask eliminates these errors entirelywhile improving F1 performance, whereas afully joint training approach underperforms dueto data sparsity. Finally, we employ datasetcartography to compare training dynamics be-tween the hierarchical and multitask approach."
}