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Zero-resource Multi-dialectal Arabic Natural Language Understanding
2021
International Journal of Advanced Computer Science and Applications
A reasonable amount of annotated data is required for fine-tuning pre-trained language models (PLM) on downstream tasks. However, obtaining labeled examples for different language varieties can be costly. In this paper, we investigate the zero-shot performance on Dialectal Arabic (DA) when fine-tuning a PLM on modern standard Arabic (MSA) data only – identifying a significant performance drop when evaluating such models on DA. To remedy such performance drop, we propose self-training with
doi:10.14569/ijacsa.2021.0120369
fatcat:kw57zdjmmbcjzg42gaa2372bnu