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A Spelling Correction Corpus for Multiple Arabic Dialects

Summary

Arabic dialects are widely written online but have no official spelling, so a single word can be spelled in many ways. One Egyptian phrase was earlier documented with 27 variants. This makes data sparse, and it is especially harmful when dialect text is the output of a system, such as speech recognition or MT into a dialect, because evaluation becomes unreliable. The authors present the MADAR CODA Corpus: 10,000 sentences (2,000 per city) for Beirut, Cairo, Doha, Rabat and Tunis. Each sentence appears in its original Raw spelling and also rewritten in CODA, the Conventional Orthography for Dialectal Arabic, following the unified CODA* guidelines. The sentences come from the MADAR parallel corpus, so the same content is aligned across all five dialects. The authors describe a bootstrapping procedure that speeds up CODA annotation and report how it performs with different amounts of pre-existing resources. They also compare the dialects with each other in Raw and in CODA form. As expected, CODA shrinks each dialect's vocabulary and increases overlap between dialects. The corpus is public and supports research on dialectal spelling correction and normalization.

Key points

  • The MADAR CODA Corpus has 10,000 sentences: 2,000 for each of Beirut, Cairo, Doha, Rabat and Tunis. All sentences are parallel across the cities and pair the Raw spelling with a CODA version.
  • The source is the MADAR Corpus (Bouamor et al., 2018). It contains BTEC travel sentences translated into city dialects by local translators, who received no guidance on spelling.
  • Annotation follows CODA* (Habash et al., 2018), which merges earlier dialect-specific CODAs and has been applied to more than two dozen dialects. CODA* uses shared principles for open-class words and specifies closed-class spellings in detail.
  • The paper treats CODA annotation as the main task in its own right, not as a side step of morphological tagging or lemmatization. The authors call it spelling correction into CODA.
  • CODA lowers vocabulary size within each dialect and raises lexical overlap across dialects. The resource is distributed via resources.camel-lab.com.

Interesting ideas

  • I1. Variant spelling matters most when it appears on the output side, because the evaluation itself becomes noisy; a shared convention mapping the system output and the reference into one form makes scores meaningful. — For Mova (back): generated English could be normalized (contractions, British/American spelling, hyphenation) before comparison in text → graph → text round-trip tests, so the compressor is not penalized for harmless surface variation.
  • I2. Annotation is bootstrapped: existing resources produce a first-pass normalization, humans correct it, and speed and accuracy are tracked for each resource setting. — For Mova (learn): silver data could be produced the same way, with each LLM teacher tier's pre-annotation logged as a separate condition, so we can measure how much each tier actually saves human review.
  • I3. The value of a normalization is quantified by comparing varieties before and after it, measuring vocabulary size within each variety and overlap between them. — For Mova (tree): when converting treebanks between UD dialects, measuring label and lemma overlap before and after conversion would show whether the conversion really brings the dialects closer.
  • I4. The orthography is split into general principles for open-class words plus curated, unified lists for closed-class words. — For Mova (tokens): normalizing informal English (gonna, wanna, y'all, lemme) could use general rules for content words plus a small, versioned, explainable exception list for function words.
  • I5. A corpus that is parallel across varieties allows a controlled comparison, because content is held fixed and only the variety changes. — For Mova (learn): parallel paraphrase sets of one story in different registers could serve as negative controls, checking that the extracted frames and world state stay identical when only surface form changes.

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

Cited by

16 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 10 not yet.

Cite

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

@inproceedings{eryani2020spelling,
  title = {{A Spelling Correction Corpus for Multiple Arabic Dialects}},
  author = {Eryani, Fadhl and Habash, Nizar and Bouamor, Houda and Khalifa, Salam},
  booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference},
  year = {2020},
  url = {https://aclanthology.org/2020.lrec-1.508/}
}

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