Fine-Tashkeel: Fine-Tuning Byte-Level Models for Accurate Arabic Text Diacritization
Bashar Al-Rfooh, Gheith A. Abandah, Rami Al‐Rfou · 2023
Most of previous work on learning diacritization of the Arabic language relied on training models from scratch. In this paper, we investigate how to leverage pre-trained language models to learn diacritization. We fine-tune token-free pre-trained multilingual models (ByT5) to learn to predict and insert missing diacritics in Arabic text, a complex task that requires understanding the sentence semantics and the morphological structure of the tokens. We achieve state-of-the-art accuracy on the dia-critization task with minimal amount of training and no feature engineering, reducing WER (word error rate) by 40%. We release our fine-tuned models for the greater benefit of the researchers in the community.