---
title: "Unified Guidelines and Resources for Arabic — Habash 2018"
description: "Modern Standard Arabic has a fixed spelling standard, but spoken Arabic dialects have none."
authors: ["Nizar Habash", "Fadhl Eryani", "Salam Khalifa", "Owen Rambow", "Dana Abdulrahim", "Alexander Erdmann", "Reem Faraj", "Wajdi Zaghouani", "Houda Bouamor", "Nasser Zalmout", "Sara Hassan", "Faisal Al-Shargi", "Sakhar Alkhereyf", "Basma Abdulkareem", "Ramy Eskander", "Mohammad Salameh", "Hind Saddiki"]
year: 2018
venue: "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)"
source: "https://aclanthology.org/L18-1574/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/habash-2018-unified-guidelines-resources-arabic-dialect/"
---
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# Unified Guidelines and Resources for Arabic Dialect Orthography

**Authors:** [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/), [Fadhl Eryani](https://md.research.elamur.ai/free/en/authors/fadhl-eryani/), [Salam Khalifa](https://md.research.elamur.ai/free/en/authors/salam-khalifa/), [Owen Rambow](https://md.research.elamur.ai/free/en/authors/owen-rambow/), Dana Abdulrahim, [Alexander Erdmann](https://md.research.elamur.ai/free/en/authors/alexander-erdmann/), [Reem Faraj](https://md.research.elamur.ai/free/en/authors/reem-faraj/), [Wajdi Zaghouani](https://md.research.elamur.ai/free/en/authors/wajdi-zaghouani/), [Houda Bouamor](https://md.research.elamur.ai/free/en/authors/houda-bouamor/), [Nasser Zalmout](https://md.research.elamur.ai/free/en/authors/nasser-zalmout/), Sara Hassan, Faisal Al-Shargi et al. (17 authors) · **Year:** 2018 · **Venue:** Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/L18-1574/) · [PDF](https://aclanthology.org/L18-1574.pdf) · [OpenAlex](https://openalex.org/W2807107081)

**Citations:** 54 citations (openalex-snapshot · 2026-10-14) · [63](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Modern Standard Arabic has a fixed spelling standard, but spoken Arabic dialects have none. As a result, one dialectal word can appear online in dozens of forms, which fragments data and hurts NLP systems. Earlier Conventional Orthography for Dialectal Arabic (CODA) work covered one dialect at a time. Its rules were often vague or ad hoc, and it only considered dialect text as input to systems, ignoring readable output from ASR, MT or chatbots. The authors propose CODA*, a dialect-independent version with four parts:

- general rules kept apart from dialect-specific specifications,
- a phonological notation called CAPHI, modeled on Arpabet,
- a multidialectal Seed Lexicon recording earlier spelling decisions,
- public web pages exposing these resources.

They apply it to 28 city dialects across the Arab world, from Rabat to Muscat. Three large dialect-processing projects at three universities already use the framework. This makes it a practical shared basis for consistent annotation and for generating dialect text that people can read.

## Key points
- The motivating example is the Egyptian word /mabiPulha:S/ ('he does not say it'). It has 27 attested spellings on Google Search. Variation comes from separate choices, such as attaching or detaching the negation clitic, and writing the stem by sound versus by etymology.
- Coverage is 28 city dialects in four macro-regions: Maghreb, Nile Basin, Fertile Crescent and Arabian Peninsula. Examples include Fes, Sfax, Aswan, Salt, Mosul, Taiz and Doha.
- CODA* builds on earlier single-dialect versions: CODA-Egyptian, Tunisian, Palestinian, Algerian, Gulf, Moroccan and Yemeni.
- The noise problem is large:
  - about 32% of words in online MSA comments contain spelling errors;
  - close to 24% of Egyptian Arabic words do not follow CODA;
  - in MT evaluation, 10% of output tokens judged correct but missing from the reference were orthographic variants of reference tokens.
- Current users are MADAR (CMU Qatar & NYUAD), the Gulf Arabic Annotated Corpus (NYUAD) and the Columbia Arabic Dialect Annotation project.

## Interesting ideas
- **Work at the city level instead of the region level.** Picking concrete cities avoids fuzzy labels like "Levantine", which blur a continuum. This is useful for any dialect continuum where regional groupings are contested.
- **Design spelling conventions for output as well as input.** Normalization rules that suit a parser may look odd to readers. If a system will generate text (ASR transcripts, MT, chatbots), the convention must stay familiar to native readers.
- **Separate universal rules from per-variety specifications.** Exceptions are kept out of the core rules and managed in their own layer. This makes it much easier to extend the scheme to a new variety without rewriting it.
- **Keep a shared seed lexicon of past decisions.** Annotators working on a new dialect can check how cognate words were spelled elsewhere. This promotes consistency across varieties and reduces repeated debates, which helps any multi-team annotation effort.
- **Measure spelling chaos with web frequency counts.** Counting search-engine hits for each variant of a single word cheaply shows how serious the inconsistency problem is. The same counts also show which variants are most common.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cited by

Cited by

63 papers of our library cite it (of the 140673 whose bibliographies we read); 11 of them have a page here, 52 not yet.

- [Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling](https://md.research.elamur.ai/free/en/papers/acl-2019-zalmout-adversarial-multitask-learning-joint-multi/)
- [Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging](https://md.research.elamur.ai/free/en/papers/acl-2020-zalmout-joint-diacritization-lemmatization-normalization-fine/)
- [An Arabic Morphological Analyzer and Generator with Copious Features](https://md.research.elamur.ai/free/en/papers/aclbulk-2018-taji-arabic-morphological-analyzer-generator-copious/)
- [Morphologically Annotated Corpora for Seven Arabic Dialects: Taizi, Sanaani, Najdi, Jordanian, Syrian, Iraqi and Moroccan](https://md.research.elamur.ai/free/en/papers/aclbulk-2019-alshargi-morphologically-annotated-corpora-seven-arabic/)
- [Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic](https://md.research.elamur.ai/free/en/papers/coling-2024-khairallah-camel-morph-msa-large-scale/)
- [Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods](https://md.research.elamur.ai/free/en/papers/lingbulk-2020-khalifa-morphological-analysis-disambiguation-gulf-arabic/)
- [Morphotactic Modeling in an Open-source Multi-dialectal Arabic Morphological Analyzer and Generator](https://md.research.elamur.ai/free/en/papers/lingbulk-2022-habash-morphotactic-modeling-open-source-multi/)
- [A Comprehensive Full-Form Lexicon for Arabic NLP and Speech Technology](https://md.research.elamur.ai/free/en/papers/lingbulk-2026-haralambous-comprehensive-full-form-lexicon-arabic/)
- [Noise-Robust Morphological Disambiguation for Dialectal Arabic](https://md.research.elamur.ai/free/en/papers/naacl-2018-zalmout-noise-robust-morphological-disambiguation-dialectal/)
- [CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing](https://md.research.elamur.ai/free/en/papers/obeid-2020-camel-tools-open-source-python/)
- [Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS](https://md.research.elamur.ai/free/en/papers/sabbagh-2026-ramsa-large-sociolinguistically-rich-emirati/)

## Cite

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

```bibtex
@inproceedings{habash2018unified,
  title = {{Unified Guidelines and Resources for Arabic Dialect Orthography}},
  author = {Habash, Nizar and Eryani, Fadhl and Khalifa, Salam and Rambow, Owen and Abdulrahim, Dana and Erdmann, Alexander and Faraj, Reem and Zaghouani, Wajdi and Bouamor, Houda and Zalmout, Nasser and Hassan, Sara and Al-Shargi, Faisal and Alkhereyf, Sakhar and Abdulkareem, Basma and Eskander, Ramy and Salameh, Mohammad and Saddiki, Hind},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  doi = {10.63317/2k835qnyjdtw},
  url = {https://doi.org/10.63317/2k835qnyjdtw}
}
```

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## References

32 references, 5 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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