From Active Learning to Semantic Data Augmentation: Exploring the Limits of Named Entity Recognition in Low-Resource Arabic Dialects

Named Entity Recognition (NER) for Arabic dialects faces persistent challenges due to the scarcity of annotated resources, severe class imbalance, and high linguistic variability. These factors hinder both model accuracy and cross-dialect generalization, creating a pressing need for more efficient annotation strategies and robust learning approaches. Motivated by these challenges, this work investigates the integration of active learning strategies with a semantic oversampling method to enhance performance on Algerian and Moroccan dialectal corpora. Three sampling strategies, Random, Uncertainty, and Diversity, are evaluated at incremental annotation levels (20%, 40%, 60%, and 80%), both with and without oversampling. The proposed semantic oversampling approach generates contextually coherent synthetic examples to alleviate underrepresented entity classes. Experiments conducted with three pre-trained language models, AraBERT, MARBERT, and Multi-dialect-BERT-Base-Arabic, demonstrate that semantic oversampling provides substantial early-stage improvements, particularly in recall, with consistent benefits observed across models. However, overall F1-scores remain modest (≤ 55%), and cross-dialect transfer performance is still limited. These findings indicate that while combining active learning with semantic oversampling improves annotation efficiency and model robustness, further progress in dialectal NER will require richer, more diverse datasets and dialect-aware modeling techniques.