# Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation

arXiv: https://arxiv.org/abs/1911.03531 · HTML: https://mlsanity.com/paper/1911.03531/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [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)
- [NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2509.02038/) (2025)
- [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)
- [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)
- [Are LLMs Good Text Diacritizers? An Arabic and Yoruba Case Study](https://mlsanity.com/paper/2506.11602/) (2025)
- [A Context-Contrastive Inference Approach To Partial Diacritization](https://mlsanity.com/paper/2401.08919/) (2024)
- [Sadeed: Advancing Arabic Diacritization Through Small Language Model](https://mlsanity.com/paper/2504.21635/) (2025)
- [SHAMS: An Audio-Grounded Pronunciation Benchmark for Levantine Arabic](https://mlsanity.com/paper/2610.01427/) (2026)

## Abstract (arXiv)

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. Moreover, we show that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.