# Arabic Text Diacritization Using Deep Neural Networks

arXiv: https://arxiv.org/abs/1905.01965 · HTML: https://mlsanity.com/paper/1905.01965/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 18 of 18)

- [GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP](https://mlsanity.com/paper/2305.14976/) (2023)
- [Dolphin: A Challenging and Diverse Benchmark for Arabic NLG](https://mlsanity.com/paper/2305.14989/) (2023)
- [Automatic Restoration of Diacritics for Speech Data Sets](https://mlsanity.com/paper/2311.10771/) (2023)
- [Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization](https://mlsanity.com/paper/2406.05760/) (2024)
- [Improving Arabic Diacritization by Learning to Diacritize and Translate](https://mlsanity.com/paper/2109.14150/) (2021)
- [Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation](https://mlsanity.com/paper/2310.16127/) (2023)
- [Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need](https://mlsanity.com/paper/2401.04848/) (2024)
- [CATT: Character-based Arabic Tashkeel Transformer](https://mlsanity.com/paper/2407.03236/) (2024)
- [Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning](https://mlsanity.com/paper/2108.01141/) (2021)
- [Diacritic Recognition Performance in Arabic ASR](https://mlsanity.com/paper/2302.14022/) (2023)
- [Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text](https://mlsanity.com/paper/2306.03557/) (2023)
- [Multilingual context-based pronunciation learning for Text-to-Speech](https://mlsanity.com/paper/2307.16709/) (2023)
- [Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization](https://mlsanity.com/paper/2303.14588/) (2023)
- [Ashaar: Automatic Analysis and Generation of Arabic Poetry Using Deep Learning Approaches](https://mlsanity.com/paper/2307.06218/) (2023)
- [Interplay of Machine Translation, Diacritics, and Diacritization](https://mlsanity.com/paper/2404.05943/) (2024)
- [Koshur Diacritizer: A Byte-Level Sequence-to-Sequence Model for Kashmiri Diacritic Restoration](https://mlsanity.com/paper/2606.15883/) (2026)
- [Inaccessible Neural Language Models Could Reinvigorate Linguistic Nativism](https://mlsanity.com/paper/2301.05272/) (2023)
- [Yo-ByT5: Efficient and High-Fidelity Diacritic Restoration for Yor`ub'a](https://mlsanity.com/paper/2610.01634/) (2026)

## Abstract (arXiv)

Diacritization of Arabic text is both an interesting and a challenging problem at the same time with various applications ranging from speech synthesis to helping students learning the Arabic language. Like many other tasks or problems in Arabic language processing, the weak efforts invested into this problem and the lack of available (open-source) resources hinder the progress towards solving this problem. This work provides a critical review for the currently existing systems, measures and resources for Arabic text diacritization. Moreover, it introduces a much-needed free-for-all cleaned dataset that can be easily used to benchmark any work on Arabic diacritization. Extracted from the Tashkeela Corpus, the dataset consists of 55K lines containing about 2.3M words. After constructing the dataset, existing tools and systems are tested on it. The results of the experiments show that the neural Shakkala system significantly outperforms traditional rule-based approaches and other closed-source tools with a Diacritic Error Rate (DER) of 2.88% compared with 13.78%, which the best DER for the non-neural approach (obtained by the Mishkal tool).