@inproceedings{senichev-etal-2025-gradient,
    title = "Gradient Flush at {S}lavic {NLP} 2025 Task: Leveraging {S}lavic {BERT} and Translation for Persuasion Techniques Classification",
    author = "Senichev, Sergey  and
      Boriskin, Aleksandr  and
      Krayko, Nikita  and
      Galimzianova, Daria",
    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/acl25-workshop-ingestion/2025.bsnlp-1.19/",
    pages = "171--176",
    ISBN = "978-1-959429-57-9",
    abstract = "The task of persuasion techniques detection is limited by several challenges, such as insufficient training data and ambiguity in labels. In this paper, we describe a solution for the Slavic NLP 2025 Shared Task. It utilizes multilingual XLM-RoBERTa, that was trained on 100 various languages, and Slavic BERT, a model fine-tuned on four languages of the Slavic group. We suggest to augment the training dataset with related data from previous shared tasks, as well as some automatic translations from English and German. The resulting solutions are ranked among the top 3 for Russian in the Subtask 1 and for all languages in the Subtask 2. We release the code for our solution - https://github.com/ssenichev/ACL{\_}SlavicNLP2025."
}