MedArabs at AbjadMed: Arabic Medical Text Classification via Data- and Algorithm-Level Fusion

AbstractIn this work, we address the challenges of Arabic medical text classification, focusing on class imbalance and the complexity of the language’s morphology. We propose a multiclass classification pipeline based on Data- and Algorithm-Level fusion, which integrates the optimal Back Translation technique for data augmentation with the Class Balanced (CB) loss function to enhance performance. The domain-specific AraBERT model is fine-tuned using this approach, achieving competitive results. On the official test set of the AbjadMed task, our pipeline achieves a Macro-F1 score of 0.4219, and it achieves 0.4068 on the development set.