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DIA2 - a Comprehensive and Diverse Diacritized Arabic Corpus for NLP Research

Paper · 2026 · Fatima Dekmak, Shady Elbassuoni, Khaled Shaban et al.

Summary

Arabic NLP and Arabic LLMs lack large, clean, natively written training text. Existing big corpora often depend on noisy web crawls, include machine-translated material, skip systematic deduplication, or strip diacritics, even though diacritics carry grammatical and lexical distinctions that undiacritized script leaves ambiguous. The authors built DIA2, a mostly Modern Standard Arabic corpus with some Classical Arabic. It draws on 28 public native-Arabic sources covering general web and encyclopedic text, news, books and poetry, and contains no translated content. Their pipeline cleans text at the sentence level, removes HTML and JavaScript, filters by URL and quality metrics, deduplicates at both the URL and document level, and diacritizes everything automatically with the CATT model. A separate gold subset keeps the manually vocalized Tashkeela text. The release exceeds 140 GB, comes in diacritized and plain versions, and spans 41.9 billion tokens with a vocabulary of over 26 million distinct words. To test the pipeline, they ran continued pretraining of Llama3.1-8B on raw and processed slices. The processed version scored higher on several Arabic benchmarks, which supports the value of careful curation for low-resource LLM work.

Key points

  • The raw composition by source category is 72.93% general-purpose (e.g., CulturaX, ArWiki, Opus Wiki), 26.23% news (e.g., NADiA, AraNPCC, SANAD), 0.50% books (Sanadset, Hindawi Books) and 0.35% poetry (e.g., Ashaar, APCD).
  • Three Hugging Face datasets are released:
    • DIA2-Dataset-Diacritized;
    • DIA2-Dataset-Non-Diacritized;
    • DIA2-Tashkeela-Gold, the manually annotated gold diacritics subset.
  • Automatic vocalization uses the CATT model, and the preprocessing code is public.
  • The near-duplicate detector was tuned on 682,004 documents with 10 planted duplicates:
    • 20 and 32 permutations were tested, with Jaccard thresholds of 0.4, 0.5 and 0.8;
    • 32 permutations with threshold 0.5 gave recall 1, precision 1 and F1 1;
    • threshold 0.8 found only 1 of the 10 planted duplicates.
  • The dataset card names three limitations: no dialectal text, little clean gold diacritized data, and no deduplication at the sentence level.

Interesting ideas

  • I1. The authors measured the deduplicator's recall by planting known duplicates with tracked IDs, and estimated precision by manually checking a small sample of flagged pairs, so the threshold choice rests on evidence rather than guesswork.
  • I2. Gold-annotated diacritics are kept as a separate subset instead of being mixed into the automatically diacritized bulk, which keeps human labels and model labels distinguishable.
  • I3. The same text is released both with and without the disambiguating layer (diacritics), so users can train on whichever form suits the task.
  • I4. The pipeline is validated by training one fixed model on raw versus processed slices of the same source, which isolates the effect of preprocessing from everything else.
  • I5. The dataset card states what the pipeline does not do, such as dedupe at the sentence level or cover dialects, which makes the blind spots explicit.

For Mova

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

  • I1 (learn): Plant known-answer items (duplicates, deliberately corrupted parses) in silver-data batches, so that each filter or repair rule reports measurable recall against its own planted controls.
  • I2 (tokens): Tag every morphological and lemma annotation with its origin (gold treebank versus tagger output), so that tagger evaluation never counts machine-labelled text as ground truth.
  • I3 (tokens): Store tokens with rich morphological features alongside a plain surface form, so components can be tested on whether the extra disambiguation actually helps downstream.
  • I4 (learn): Run matched ablations in which the same parser is trained on unfiltered versus witness-agreed silver data, to prove the agreement filter adds value rather than assuming it.
  • I5 (world): Each compiled knowledge resource (verb classes, ontology slices) could ship a short card listing known coverage gaps, so failures in text-world construction can be traced to those gaps.

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

 Institutions

🎓American University of BeirutBeirut · Lebanon34 works · 2,561 citations🎓Qatar UniversityDoha · Qatar48 works · 1,026 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksEducation · 203 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 2,908 works

Cite

Cite as Fatima Dekmak, Shady Elbassuoni, Khaled Shaban, Hazem Hajj, Wassim El-Hajj, Yasmine Abu Adla and Buthaina Alabrash. 2026. DIA2 - a Comprehensive and Diverse Diacritized Arabic Corpus for NLP Research. The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/3k2m7vtzunuk

@inproceedings{dekmak2026dia2,
  title = {{DIA2 - a Comprehensive and Diverse Diacritized Arabic Corpus for NLP Research}},
  author = {Fatima Dekmak, Shady Elbassuoni, Khaled Shaban, Hazem Hajj, Wassim El-Hajj, Yasmine Abu Adla and Buthaina Alabrash},
  booktitle = {The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks},
  year = {2026},
  doi = {10.63317/3k2m7vtzunuk},
  url = {https://doi.org/10.63317/3k2m7vtzunuk}
}

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