---
title: "Revisiting Common Assumptions about Arabic Dialects in NLP"
description: "The paper tests four beliefs that Arabic NLP often treats as given: dialectal sentences belong to one… 94 references, cited by 5 papers here."
authors: ["Amr Keleg", "Sharon Goldwater", "Walid Magdy"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.166/"
doi: "10.18653/v1/2025.acl-long.166"
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/keleg-2025-revisiting-common-assumptions-about-arabic/"
---
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# Revisiting Common Assumptions about Arabic Dialects in NLP

**Authors:** [Amr Keleg](https://md.research.elamur.ai/free/en/authors/amr-keleg/), [Sharon Goldwater](https://md.research.elamur.ai/free/en/authors/sharon-goldwater/) and [Walid Magdy](https://md.research.elamur.ai/free/en/authors/walid-magdy/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **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/2025.acl-long.166/) · [PDF](https://aclanthology.org/2025.acl-long.166.pdf) · [DOI 10.18653/v1/2025.acl-long.166](https://doi.org/10.18653/v1/2025.acl-long.166) · [OpenAlex](https://openalex.org/W4412886684)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [5](https://md.research.elamur.ai/free/en/papers/keleg-2025-revisiting-common-assumptions-about-arabic/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper tests four beliefs that Arabic NLP often treats as given:
- dialectal sentences belong to one regional dialect;
- only short sentences fit several dialects;
- hand-made lists of "distinctive" dialect words reliably reveal a sentence's dialect;
- speakers of different dialects rate the dialectness of a shared sentence the same way.

To check them, the authors extend the NADI 2024 tweet set into the MLADI (Multi-label ADI) dataset. Native speakers from 11 Arab countries, three per country, mark whether each tweet could have been written in their country-level dialect. If yes, they also give an ALDi level from L0 (MSA) to L3 (informal colloquial).

The analysis covers 978 dialectal sentences, and all four beliefs turn out to be oversimplified or sometimes wrong:
- more than half the sentences fit several regions;
- ALDi predicts how many dialects a sentence fits better than length does;
- cue-word lists are less distinctive than assumed;
- dialectness ratings shift systematically by the rater's region.

The authors argue that dialect identification should be framed as a multi-label task. They also argue that dataset-building shortcuts based on these beliefs may be holding back Arabic NLP.

## Key points
- **Source data:** NADI 2024 contains 1,120 tweets (70 MSA, 1,050 DA), geolocated across the 14 most populous Arab countries. 72 DA tweets judged valid in none of the 11 annotated dialects were dropped, leaving 978.
- **Annotators:** 33 in total, three from each of 11 countries. The original NADI 2024 annotation used 27 annotators from 9 countries.
- **Overlap:** over 56% of the sentences are acceptable in more than one regional dialect. Based on the overlap analysis, Gulf and Gulf of Aden were merged into one region.
- **Rater region effect:** a permutation test shows Maghrebi annotators give lower ALDi than annotators from three other regions:

  | Compared with | Mean difference | p-value |
  |---|---|---|
  | Nile Basin | −0.09 | 0.007 |
  | Levant | −0.13 | 0.00002 |
  | Gulf/Gulf of Aden | −0.14 | 0.0002 |

  Nile Basin annotators also rate lower than Levantine ones (−0.05, p = 0.04). Other region pairs show no significant difference.
- **Resources:** the code is released on GitHub, along with an MLADI leaderboard hosted on Hugging Face.

## Interesting ideas
- Instead of forcing one dialect label per sentence, record every variety in which native speakers accept the sentence, since overlap is common rather than marginal.
- Measure the actual precision of supposedly "distinctive" keyword lists before using them to harvest or label data, because unverified lists quietly leak shared words.
- To check whether two rater groups truly differ, shuffle the raters randomly into two groups many times. Then see how often the shuffled split shows as large a gap as the real one, which gives a cheap, assumption-light significance test.
- A graded score of how far a sentence departs from the standard variety predicted cross-dialect validity better than sentence length, suggesting register is a stronger signal than surface size.
- Identical spellings can carry different senses or connotations in different varieties, which can skew both dialectness judgments and keyword-based data collection.

## For Mova

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

- **I1** (tree): When converting or pooling treebanks across English varieties, tag each sentence with the set of varieties where it is acceptable, so variety-specific parser checks are not penalised on shared material.
- **I2** (learn): Any hand-written lexical trigger lists used to pick silver data or fire repair rules should be scored against annotated samples before they are trusted.
- **I3** (learn): The same shuffle test can tell whether one family of witness parsers or LLM teachers disagrees systematically with another, or only by chance, serving as a built-in negative control.
- **I4** (tokens): Compute a standardness/informality score early in the pipeline and use it to route informal inputs to more tolerant morphology and lemma handling, rather than relying on length heuristics.
- **I5** (world): Allow WordNet-based sense selection to be conditioned on the detected variety or register, so a shared surface form is not mapped to a single ontology node by default.

*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: Amr Keleg, Sharon Goldwater and Walid Magdy. 2025. Revisiting Common Assumptions about Arabic Dialects in NLP. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{keleg2025revisiting,
  title = {{Revisiting Common Assumptions about Arabic Dialects in NLP}},
  author = {Amr Keleg, Sharon Goldwater and Walid Magdy},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.166},
  url = {https://doi.org/10.18653/v1/2025.acl-long.166}
}
```

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## References

94 references, 57 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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*Updated 1 November 2026*