Fine-Tuned Transformer-Based Weighted Soft Voting Ensemble for Persuasion Technique Classification in Slavic Languages

AbstractThis paper explores detecting persuasion techniques in Slavic languages using both single transformer models and weighted soft voting ensemble methods. We focused on identifying the presence of persuasion in Bulgarian, Polish, Slovene, and Russian text fragments. We have applied various preprocessing steps to improve model performance. Our experiments show that weighted soft voting ensembles consistently outperform single models in most languages, achieving F1-scores of 0.867 for Bulgarian, 0.902 for Polish, and 0.804 for Russian. For Slovene, the single SlovakBERT model performed best with an F1-score of 0.823, just ahead of the ensemble. These results demonstrate that combining monolingual and multilingual transformer models is effective for robust persuasion detection in low-resource Slavic languages.