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Most of previous work on learning diacritization of the Arabic languagerelied on training models from scratch. In this paper, we investigate how toleverage pre-trained language models to learn diacritization. We finetunetoken-free pre-trained multilingual models (ByT5) to learn to predict andinsert missing diacritics in Arabic text, a complex task that requiresunderstanding the sentence semantics and the morphological structure of thetokens. We show that we can achieve state-of-the-art on the diacritization taskwith minimal amount of training and no feature engineering, reducing WER by40%. We release our finetuned models for the greater benefit of the researchersin the community.

Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization