@inproceedings{wang-etal-2025-team,
    title = "Team {INSA}ntive at {S}lavic{NLP}-2025 Shared Task: Data Augmentation and Enhancement via Explanations for Persuasion Technique Classification",
    author = "Wang, Yutong  and
      Nurbakova, Diana  and
      Calabretto, Sylvie",
    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-1.22/",
    pages = "190--201",
    ISBN = "978-1-959429-57-9",
    abstract = "This study investigates the automatic detection and classification of persuasion techniques across five Slavic languages (Bulgarian, Croatian, Polish, Russian, and Slovenian), addressing two subtasks: binary detection of persuasion techniques in text fragments (Subtask 1) and multi-label classification of specific technique types (Subtask 2). To overcome limited training resources, we implemented a multi-level cross-lingual augmentation strategy utilizing GPT-4o for non-Slavic to Slavic conversion and intra-Slavic language migration. We employ XLM-RoBERTa architecture with two LLM-enhanced variants that use explanations to improve classification performance. The experimental results demonstrate varied performance across languages and tasks, with our approach achieving first place in the Russian subtask 1 and second place in Bulgarian subtask 2, confirming that larger parameter models excel in complex classification tasks. These findings highlight the significant potential of LLMs for enhancing multilingual classification and the persistent difficulties in ensuring consistent cross-linguistic performance."
}