# Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization

arXiv: https://arxiv.org/abs/2406.05760 · HTML: https://mlsanity.com/paper/2406.05760/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://mlsanity.com/paper/2305.14734/) (2023)
- [Arabic Text Diacritization Using Deep Neural Networks](https://mlsanity.com/paper/1905.01965/) (2019)
- [Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects](https://mlsanity.com/paper/2110.06852/) (2021)
- [Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis](https://mlsanity.com/paper/1905.05700/) (2019)
- [Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging](https://mlsanity.com/paper/1910.02267/) (2019)
- [A Multitask Learning Approach for Diacritic Restoration](https://mlsanity.com/paper/2006.04016/) (2020)
- [Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation](https://mlsanity.com/paper/1911.03531/) (2019)
- [Deep Diacritization: Efficient Hierarchical Recurrence for Improved Arabic Diacritization](https://mlsanity.com/paper/2011.00538/) (2020)
- [Camelira: An Arabic Multi-Dialect Morphological Disambiguator](https://mlsanity.com/paper/2211.16807/) (2022)
- [Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling](https://mlsanity.com/paper/1910.12702/) (2019)
- [Arabic Diacritic Recovery Using a Feature-Rich biLSTM Model](https://mlsanity.com/paper/2002.01207/) (2020)
- [NatiQ: An End-to-end Text-to-Speech System for Arabic](https://mlsanity.com/paper/2206.07373/) (2022)
- [Improving Arabic Diacritization by Learning to Diacritize and Translate](https://mlsanity.com/paper/2109.14150/) (2021)
- [Homograph Disambiguation Through Selective Diacritic Restoration](https://mlsanity.com/paper/1912.04479/) (2019)
- [Automatic Romanization of Arabic Bibliographic Records](https://mlsanity.com/paper/2103.07199/) (2021)
- [Diacritization of Maghrebi Arabic Sub-Dialects](https://mlsanity.com/paper/1810.06619/) (2018)
- [Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text](https://mlsanity.com/paper/2306.03557/) (2023)
- [A Context-Contrastive Inference Approach To Partial Diacritization](https://mlsanity.com/paper/2401.08919/) (2024)

## Built on by (most cited first, 8 of 8)

- [NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2509.02038/) (2025)
- [Guidelines for Fine-grained Sentence-level Arabic Readability Annotation](https://mlsanity.com/paper/2410.08674/) (2024)
- [Abjad AI at NADI 2025: CATT-Whisper: Multimodal Diacritic Restoration Using Text and Speech Representations](https://mlsanity.com/paper/2510.24247/) (2025)
- [Proper Noun Diacritization for Arabic Wikipedia: A Benchmark Dataset](https://mlsanity.com/paper/2505.02656/) (2025)
- [A Tale of Two Scripts: Transliteration and Post-Correction for Judeo-Arabic](https://mlsanity.com/paper/2507.04746/) (2025)
- [Lemmatization as a Classification Task: Results from Arabic across Multiple Genres](https://mlsanity.com/paper/2506.18399/) (2025)
- [Hebrew Diacritics Restoration using Visual Representation](https://mlsanity.com/paper/2510.26521/) (2025)
- [SHAMS: An Audio-Grounded Pronunciation Benchmark for Levantine Arabic](https://mlsanity.com/paper/2610.01427/) (2026)

## Abstract (arXiv)

The widespread absence of diacritical marks in Arabic text poses a significant challenge for Arabic natural language processing (NLP). This paper explores instances of naturally occurring diacritics, referred to as "diacritics in the wild," to unveil patterns and latent information across six diverse genres: news articles, novels, children's books, poetry, political documents, and ChatGPT outputs. We present a new annotated dataset that maps real-world partially diacritized words to their maximal full diacritization in context. Additionally, we propose extensions to the analyze-and-disambiguate approach in Arabic NLP to leverage these diacritics, resulting in notable improvements. Our contributions encompass a thorough analysis, valuable datasets, and an extended diacritization algorithm. We release our code and datasets as open source.