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# Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation

**Authors:** [Hamdy Mubarak](https://md.mova.elamur.ai/free/en/docs/papers/authors/hamdy-mubarak/); Majd Hawasly; Abubakr Mohamed · **Year:** 2026 · **Venue:** Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) **Link:** [aclanthology.org/2026.eacl-long.296](https://aclanthology.org/2026.eacl-long.296/) · library `eacl-2026-mubarak-nahw-comprehensive-benchmark-arabic-grammar` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**0** citations](https://openalex.org/works?filter=cites:W7140094933) openalex · 2026-10-18[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/eacl-2026-mubarak-nahw-comprehensive-benchmark-arabic-grammar/) of the 140673 papers whose bibliographies we read[Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://aclanthology.org/volumes/2026.eacl-long/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2026.eacl-long.296/)
- [PDF](https://aclanthology.org/2026.eacl-long.296.pdf)
- [DOI 10.18653/v1/2026.eacl-long.296](https://doi.org/10.18653/v1/2026.eacl-long.296)
- [OpenAlex](https://openalex.org/W7140094933)

**Authors (3)**

1. [Hamdy Mubarak](https://openalex.org/A5122024401) h 1 · 4 citations · 14 works
2. [Majd Hawasly](https://openalex.org/A5021151663) [ORCID](https://orcid.org/0000-0003-1823-5580) · h 10 · 381 citations · 71 works
3. [Abubakr Mohamed](https://openalex.org/A5129995054) h 0 · 0 citations · 2 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper builds Nahw, a benchmark for Modern Standard Arabic grammar taken from school grammar material. It has four tasks in a ladder: grammar understanding (5K multiple-choice questions, Nahw-MCQ, grades 6 to 12), error detection and correction in 100 short passages with about five errors each (Nahw-Passage, 511 errors), and explanation of a correction, scored by two linguists. Benchmarked open models reach about 51% accuracy on the MCQs at best (ALLaM-7B), and GPT-4o is the strongest system (67% on average over the tasks, 63.9 F1 on detection); the authors also show that 10K GPT-4o-written MCQs lift a 4B model by 15.8% relative, while 5K natural questions lift it by 44.8%. An LLM judge rated the explanations about twice as high as the human linguists did, so it cannot replace them.

## Ideas

- I1. Turn each Hebrew grammar chapter ([Syntax: the clause and the sentence](https://md.mova.elamur.ai/free/en/docs/languages/hbo/08-syntax/), [7. Syntax](https://md.mova.elamur.ai/free/en/docs/languages/he/07-syntax/)) into a four-rung test: a multiple-choice item, a detection item (which word is wrong), a correction item and a short explanation check, each with a rule-based scorer.
- I2. Build a correction set where the right answer differs only by a vowel mark, to test the unpointed mode of `tools/hbo`. The paper notes this is where models fail most.
- I3. Generate grammar items with the teacher model as training data only, and keep a natural held-out set that is never used for training. Report the gain on the natural set, since the paper's gain from synthetic data was less than half the gain from natural data.

## For Mova

I1 goes into [Syntax: the clause and the sentence](https://md.mova.elamur.ai/free/en/docs/languages/hbo/08-syntax/) and [7. Syntax](https://md.mova.elamur.ai/free/en/docs/languages/he/07-syntax/) as the test layout for each chapter. I2 is a test for `tools/hbo` in unpointed mode, with the vowel-sensitive cases listed in [Phonology of Biblical Hebrew](https://md.mova.elamur.ai/free/en/docs/languages/hbo/03-phonology/). I3 follows the teacher rule: the teacher writes items, the held-out natural set measures them. The LLM-judge finding is a warning against any judge in Mova's scoring.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2026.eacl-long.296](https://doi.org/10.18653/v1/2026.eacl-long.296)

```
@inproceedings{mubarak2026nahw,
  title = {{Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation}},
  author = {Mubarak, Hamdy and Hawasly, Majd and Mohamed, Abubakr},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.296},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.296}
}
```

- [← previous Lemmatization as a Classification Task: Results from Arabic across Multiple Genres](https://md.mova.elamur.ai/free/en/docs/papers/ctx-2025-saeed-lemmatization-as-classification-task-results/)
- [next → Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach](https://md.mova.elamur.ai/free/en/docs/papers/emnlp-2021-lazar-filling-gaps-ancient-akkadian-texts/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

## References

23 references, 0 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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