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

**Authors:** Bashar Al-Rfooh, Gheith Abandah, Rami Al-Rfou  
**arXiv:** [2303.14588](https://arxiv.org/abs/2303.14588)  
**Date:** 2023-03-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.14588v1/

## Key points

- Diacritization of Arabic text is a challenging task that requires understanding sentence semantics and morphological structure of tokens.
- Previous approaches relied on training models from scratch, but this paper investigates leveraging pre-trained language models for diacritization.
- The authors finetune token-free pre-trained multilingual models (ByT5) to predict and insert missing diacritics in Arabic text.
- State-of-the-art results are achieved with minimal training and no feature engineering, reducing Word Error Rate (WER) by 40%.
- A curriculum utilizing both quality and size of training data is devised to study the effect of data quality and size on the finetuning process. Sequential finetuning reduces Diacritic Error Rate (DER) from 1.33% to 1.16%.
- Scale matters as consistent improvements are shown on downstream tasks as the pretrained model scales up.
- This paper presents a novel approach for accurate Arabic text diacritization using pre-trained language models without requiring extensive training or feature engineering.
- The authors release their finetuned models for use by researchers in the community.

## Summary

In the field of natural language processing, diacritization of Arabic text has been a challenging task that requires understanding the sentence semantics and the morphological structure of tokens. Previous approaches to learning a statistical model for automatic diacritization relied on training models from scratch. However, in this paper, the authors investigate how to leverage pre-trained language models to learn diacritization. They finetune token-free pre-trained multilingual models (ByT5) to predict and insert missing diacritics in Arabic text. The authors show that they can achieve state-of-the-art results on the diacritization task with minimal training and no feature engineering, reducing Word Error Rate (WER) by 40%. The authors also study the effect of data quality and size on the finetuning process, devising a curriculum that utilizes both quality and size of training data. They demonstrate that sequential finetuning is able to reduce Diacritic Error Rate (DER) from 1.33% to 1.16%. Additionally, they analyze whether scale matters by comparing Base ByT5 model with Small ByT5 model and show consistent improvements on downstream tasks as the pretrained model scales up. Overall, this paper presents a novel approach for accurate Arabic text diacritization using pre-trained language models without requiring extensive training or feature engineering. The authors release their finetuned models for use by researchers in the community.