ReDASPersuasion at ArAIEval Shared Task: Multilingual and Monolingual Models For Arabic Persuasion Detection

AbstractTo enhance persuasion detection, we investigate the use of multilingual systems on Arabic data by conducting a total of 22 experiments using baselines, multilingual, and monolingual language transformers. Our aim is to provide a comprehensive evaluation of the various systems employed throughout this task, with the ultimate goal of comparing their performance and identifying the most effective approach. Our empirical analysis shows that *ReDASPersuasion* system performs best when combined with multilingual “XLM-RoBERTa” and monolingual pre-trained transformers on Arabic dialects like “CAMeLBERT-DA SA” depending on the NLP classification task.