---
title: "A System for Diacritizing Four Varieties of Arabic — Mubarak 2019"
description: "This demo paper presents a deployed system that adds diacritics back for four varieties: Classical… 24 references, cited by 7 papers here."
authors: ["Hamdy Mubarak", "Ahmed Abdelali", "Kareem Darwish", "Mohamed Eldesouki", "Younes Samih", "Hassan Sajjad"]
year: 2019
venue: "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations"
source: "https://aclanthology.org/D19-3037/"
doi: "10.18653/v1/d19-3037"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mubarak-2019-system-diacritizing-four-varieties-arabic/"
---
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# A System for Diacritizing Four Varieties of Arabic

**Authors:** [Hamdy Mubarak](https://md.research.elamur.ai/free/en/authors/hamdy-mubarak/), [Ahmed Abdelali](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/), [Kareem Darwish](https://md.research.elamur.ai/free/en/authors/kareem-darwish/), [Mohamed Eldesouki](https://md.research.elamur.ai/free/en/authors/mohamed-eldesouki/), [Younes Samih](https://md.research.elamur.ai/free/en/authors/younes-samih/) and [Hassan Sajjad](https://md.research.elamur.ai/free/en/authors/hassan-sajjad/) · **Year:** 2019 · **Venue:** [Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2019](https://md.research.elamur.ai/free/en/venues/emnlp/2019/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D19-3037/) · [PDF](https://aclanthology.org/D19-3037.pdf) · [DOI 10.18653/v1/d19-3037](https://doi.org/10.18653/v1/d19-3037) · [OpenAlex](https://openalex.org/W2970515934)

**Citations:** 10 citations (OpenAlex, 2026-10-14) · [7](https://md.research.elamur.ai/free/en/papers/mubarak-2019-system-diacritizing-four-varieties-arabic/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Arabic is usually written without short vowels (diacritics). Readers must infer them to pronounce words, so automatic diacritic restoration matters for text-to-speech and language learning. This demo paper presents a deployed system that adds diacritics back for four varieties: Classical Arabic (CA), Modern Standard Arabic (MSA), and two dialects, Moroccan (DA-MA) and Tunisian (DA-TN).

The approach treats diacritization as character-level translation. An OpenNMT seq2seq model maps undiacritized character sequences to diacritized ones and needs no hand-built features, segmenters or POS taggers. To keep word order fixed, the model works on overlapping 7-word windows, and votes across windows pick each word's final form. Around the model sit:
- Farasa-based cleaning,
- a fastText character-n-gram classifier that guesses the variety,
- a Flask/Gunicorn/NGINX web service with a REST API.

The authors also built a large CA corpus and release a CA test set. Word error rates drop substantially below every compared system for all four varieties, showing that one feature-free architecture transfers across standard and dialectal Arabic.

## Key points
- WER: MSA 4.5% (best prior, Microsoft ATKS, 12.3%); CA 3.7% (Farasa 12.8%); DA-MA 1.4% (Bi-LSTM 2.7%); DA-TN 2.5% (Bi-LSTM 3.6%). This is a 25–60% error reduction over earlier neural systems.
- Data:
  - MSA: 4.5m-word training corpus, tested on WikiNews (18.3k words).
  - CA: a publisher's 65m-word corpus, with a released test set of 5,000 sentences (~400k words).
  - DA: diacritized New Testament translations (166k Moroccan and 157k Tunisian words), split 70/10/20.
- Model: OpenNMT-tf with two RNN layers of 500 units and 300-dimensional embeddings. Inputs are 7-word sliding windows, combined by majority voting; ties go to the window where the word sits in the centre.
- Variety identification: fastText on character 3–6-grams. Accuracy rises with input length, from 93.8% at 5 words to 99.1% at 20 words.
- Deployment: a 4-core, 16 GB Azure VM with no GPU handles about 672 requests per second, averaging 149 ms per request.

## Interesting ideas
- Overlapping fixed-size windows let one token get several predictions, which are then voted on, with the most central context breaking ties; this bounds input length while still giving each token full context.
- Manual error analysis found that in 7% of CA "errors" both the gold and the system output were valid alternatives, so raw WER overstates the error rate.
- The seq2seq model sometimes produced output unrelated to the input, and the authors propose forcing output letters to match input letters exactly.
- A cheap character-level classifier first detects the language variety and routes the text to a dedicated model, with its accuracy reported against input length.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tree): Very long sentences could be parsed as overlapping chunks, voting on each token's head and relation and trusting the chunk where the token is most central.
- **I2** (learn): Evaluation could add an explicit "both acceptable" category when sampling disagreements, so models are not penalised for legitimate variant analyses.
- **I3** (back): Generation and the text → graph → text compressor could include a hard check that required content tokens survive the round trip, flagging hallucinated material.
- **I4** (tokens): A similar router could detect register or genre (e.g. informal web text vs. edited prose) before tagging, and log confidence by input length to know when routing is unreliable.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Hamdy Mubarak, Ahmed Abdelali, Kareem Darwish, Mohamed Eldesouki, Younes Samih and Hassan Sajjad. 2019. A System for Diacritizing Four Varieties of Arabic. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{mubarak2019system,
  title = {{A System for Diacritizing Four Varieties of Arabic}},
  author = {Hamdy Mubarak, Ahmed Abdelali, Kareem Darwish, Mohamed Eldesouki, Younes Samih and Hassan Sajjad},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations},
  year = {2019},
  doi = {10.18653/v1/d19-3037},
  url = {https://doi.org/10.18653/v1/d19-3037}
}
```

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## References

24 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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4. Mustafa Jarrar, Nizar Habash, Faeq Alrimawi, Diyam Akra, and Nasser Zalmout. 2017. Curras: an annotated corpus for the palestinian arabic dialect. Language Resources and Evaluation, 51(3):745–775. [↗](https://doi.org/10.1007/s10579-016-9370-7)
5. Ahmed Abdelali, Mohammed Attia, Younes Samih, Kareem Darwish, and Hamdy Mubarak. 2018. [Diacritization of maghrebi arabic sub-dialects](https://md.research.elamur.ai/free/en/papers/abdelali-2018-diacritization-maghrebi-arabic-sub-dialects/). arXiv preprint arXiv:1810.06619. [↗](https://arxiv.org/abs/1810.06619)
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18. Mohsen Rashwan, Ahmad Al Sallab, M. Raafat, and Ahmed Rafea. 2015. Deep learning framework with confused sub-set resolution architecture for automatic arabic diacritization. In IEEE Transactions on Audio, Speech, and Language Processing, pages 505–516. [↗](https://doi.org/10.1109/taslp.2015.2395255)
19. Kareem Darwish, Hamdy Mubarak, and Ahmed Abdelali. 2017. [Arabic diacritization: Stats, rules, and hacks](https://md.research.elamur.ai/free/en/papers/darwish-2017-arabic-diacritization-stats-rules-hacks/). In Proceedings of the Third Arabic Natural Language Processing Workshop, pages 9–17.
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*Updated 1 November 2026*