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The use of multilingual language models for tasks in low and high-resourcelanguages has been a success story in deep learning. In recent times, Arabichas been receiving widespread attention on account of its dialectal variance.While prior research studies have tried to adapt these multilingual models fordialectal variants of Arabic, it still remains a challenging problem owing tothe lack of sufficient monolingual dialectal data and parallel translation dataof such dialectal variants. It remains an open problem on whether the limiteddialectical data can be used to improve the models trained in Arabic on itsdialectal variants. First, we show that multilingual-BERT (mBERT) incrementallypretrained on Arabic monolingual data takes less training time and yieldscomparable accuracy when compared to our custom monolingual Arabic model andbeat existing models (by an avg metric of +$6.41$). We then explore twocontinual pre-training methods -- (1) using small amounts of dialectical datafor continual finetuning and (2) parallel Arabic to English data and aTranslation Language Modeling loss function. We show that both approaches helpimprove performance on dialectal classification tasks ($+4.64$ avg. gain) whenused on monolingual models.

Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic