@inproceedings{ksiezniak-etal-2025-robust,
    title = "Robust Detection of Persuasion Techniques in {S}lavic Languages via Multitask Debiasing and Walking Embeddings",
    author = "Ksiezniak, Ewelina  and
      Wecel, Krzysztof  and
      Sawinski, Marcin",
    editor = "Piskorski, Jakub  and
      P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Nakov, Preslav  and
      Yangarber, Roman  and
      Marcinczuk, Michal",
    booktitle = "Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.bsnlp-1.25/",
    doi = "10.18653/v1/2025.bsnlp-1.25",
    pages = "224--230",
    ISBN = "978-1-959429-57-9",
    abstract = "We present our solution to Subtask 1 of the Shared Task on the Detection and Classification of Persuasion Techniques in Texts for Slavic Languages. Our approach integrates fine-tuned multilingual transformer models with two complementary robustness-oriented strategies: Walking Embeddings and Content-Debiasing. With the first, we tried to understand the change in embeddings when various manipulation techniques were applied. The latter leverages a supervised contrastive objective over semantically equivalent yet stylistically divergent text pairs, generated via GPT-4. We conduct extensive experiments, including 5-fold cross-validation and out-of-domain evaluation, and explore the impact of contrastive loss weighting."
}