REIGNITE at AbjadMed: Imbalance-Aware Fine-Tuning of Pretrained Arabic Transformers for Arabic Medical Text Classification Task

AbstractThis paper presents our system developed for the AbjadNLP Shared Task 4 on Medical Text Classification in Arabic, which aims to assign Arabic medical question-answer pairs to a predefined set of medical categories. The task poses significant challenges due to severe class imbalance across 82 categories and the linguistic complexity of domain-specific Arabic medical text. To address these challenges, we propose an imbalance-aware training framework that combines targeted data augmentation for minority classes with class-weighted focal loss during fine-tuning. We evaluate multiple Arabic pretrained transformer models under a unified training configuration and further improve robustness through a majority-voting ensemble of the best-performing models. Our approach achieves competitive performance, ranking 15th on the private leaderboard with a macro F1 score of 0.4052, demonstrating the effectiveness of combining different data augmentation techniques, imbalance-aware training objectives, and ensemble learning for large-scale, highly imbalanced Arabic medical text classification. The code is available on GitHub.