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A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions

Paper · 2025 · Injy Hamed, Caroline Sabty, Slim Abdennadher et al.

Summary

Code-switching (CSW) is common in the Arab world, yet language technology rarely handles it well. Speakers move between Modern Standard Arabic (MSA) and dialects, which the authors call diglossic CSW. They also mix Arabic with English, French and other languages, which is bilingual CSW. The authors collected papers from Google Scholar using code-switch, code-mix and Arabic as search terms. They tagged each paper by year, venue, language-pair category, NLP task, method family and, for resource papers, data domain, following a scheme adapted from Winata et al. (2023). They then report trends, best results per task and gaps. Interest in the field began around 2014, driven largely by the CSW workshop shared tasks, and has since averaged about a dozen papers a year. Arabic CSW work adopts new methods roughly 3–4 years after CSW research in general. Only half of the listed tasks have any empirical work at all. High-level, user-facing tasks remain almost untouched. The survey gives a map of the field and a research agenda for a variety that is underserved.

Key points

  • The taxonomy covers four switch types: inter-sentential, extra-sentential (tags and fillers), intra-sentential, and morphological. In the morphological type, Arabic clitics attach to foreign words. Borrowings are deliberately counted as CSW.
  • Papers are sorted into five language-pair buckets: MSA-DA, MSA-Foreign, DA-Foreign, Arabic-Foreign and MSA-DA-Foreign.
    • The most studied diglossic pair is MSA-Egyptian.
    • The leading bilingual pairs are Egyptian-English and Algerian-French.
  • Venues: conferences 50%, workshops 36% (13% shared tasks), journals 7%, arXiv 4%, theses 3%.
    • Neural methods reached Arabic CSW in 2016.
    • Pretrained models arrived in 2020, compared with 2016 for CSW research overall.
  • Data sources are led by social media (36 datasets), then transcriptions (21) and speech recordings (18). Word-level LID is the most studied task, with 32 empirical papers and 22 resources.
  • Best reported results:
    • Word-level LID: F1 of 91.9 (diglossic) and 95.0 (bilingual).
    • NER: F1 of 85.2 (diglossic) and 79.4 (bilingual).
    • MT into Arabic: BLEU 87.2.
    • ASR: WER between 24.8% and 53.8%.

Interesting ideas

  • I1. Morphological switching, where native affixes wrap foreign stems, is treated as its own category rather than as noise.
  • I2. Telling borrowing apart from switching is admitted to be hard, so most work simply marks every foreign token the same way.
  • I3. String-matching metrics unfairly punish a model that writes a borrowed word in a different but valid script or spelling.
  • I4. Text sources such as social media show fewer and different switching phenomena than natural speech, so the corpus genre biases what a model ever sees.
  • I5. Coverage claims can look inflated when a corpus reports no per-sentence mixing statistics, so the authors call for finer-grained corpus statistics.

For Mova

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

  • I1 (tokens): the English morphology analyser could explicitly split foreign or novel stems from English inflection (a loan stem plus -ed or -s) and record each piece's origin, instead of failing on unknown words.
  • I2 (tokens): the tagger could give foreign material one uniform, explainable mark and leave fine distinctions to later stages, instead of guessing at the tokenizer level.
  • I3 (back): text → graph → text output could be scored by whether the meaning graph is preserved, or with spelling-variant equivalence, so legitimate surface variation is not counted as error.
  • I4 (learn): the silver-data pipeline should check genre coverage and report which constructions are missing because of the source, not only how much data it has.
  • I5 (learn): evaluation sets could carry per-sentence statistics of the phenomena they test, so that a good score cannot hide that a hard case appears only a handful of times.

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

🎓University of StuttgartStuttgart · Germany91 works · 46,376 citations🎓New York University Abu DhabiAbu Dhabi · United Arab Emirates122 works · 4,602 citations🎓University of HoustonHouston · United States11 works · 1,210 citations🎓German International UniversityCairo · Egypt4 works · 13 citations

 Fields, topics and concepts

📚 Shelves 4The 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 works

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

Cite

Cite as Injy Hamed, Caroline Sabty, Slim Abdennadher, Ngoc Thang Vu, Thamar Solorio and Nizar Habash. 2025. A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions. Proceedings of the 31st International Conference on Computational Linguistics.

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

@inproceedings{hamed2025survey,
  title = {{A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions}},
  author = {Injy Hamed, Caroline Sabty, Slim Abdennadher, Ngoc Thang Vu, Thamar Solorio and Nizar Habash},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.307/}
}

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