---
title: "BAREC Shared Task 2025 on Arabic Readability Assessment"
description: "This report covers the first shared task on fine-grained readability assessment for Arabic, held at… 64 references, cited by 18 papers here."
authors: ["Khalid N. Elmadani", "Bashar Alhafni", "Hanada Taha", "Nizar Habash"]
year: 2025
venue: "Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks"
source: "https://aclanthology.org/2025.arabicnlp-sharedtasks.34/"
doi: "10.18653/v1/2025.arabicnlp-sharedtasks.34"
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/elmadani-2025-barec-shared-task-2025-arabic/"
---
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# BAREC Shared Task 2025 on Arabic Readability Assessment

**Authors:** [Khalid N. Elmadani](https://md.research.elamur.ai/free/en/authors/khalid-n-elmadani/), [Bashar Alhafni](https://md.research.elamur.ai/free/en/authors/bashar-alhafni/), [Hanada Taha](https://md.research.elamur.ai/free/en/authors/hanada-taha/) and [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/) · **Year:** 2025 · **Venue:** [Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks](https://md.research.elamur.ai/free/en/venues/arabicnlp/) ([ArabicNLP 2025](https://md.research.elamur.ai/free/en/venues/arabicnlp/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.arabicnlp-sharedtasks.34/) · [PDF](https://aclanthology.org/2025.arabicnlp-sharedtasks.34.pdf) · [DOI 10.18653/v1/2025.arabicnlp-sharedtasks.34](https://doi.org/10.18653/v1/2025.arabicnlp-sharedtasks.34) · [OpenAlex](https://openalex.org/W4416037809)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [18](https://md.research.elamur.ai/free/en/papers/elmadani-2025-barec-shared-task-2025-arabic/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This report covers the first shared task on fine-grained readability assessment for Arabic, held at ArabicNLP 2025. Arabic has rich morphology, a large lexicon and ambiguous script, and it has had few standardized readability resources. Systems had to assign one of 19 ordered levels, which come from the Taha/Arabi21 framework as adapted by the BAREC guidelines. They did this at two granularities: single sentences, and documents, where a document takes the level of its most difficult sentence. Three tracks limited what external data teams could use. The organizers supplied the BAREC Corpus, a new blind test set, ordinal-aware metrics with QWK as the headline score, and baselines. Of the 22 registered teams, 17 wrote system papers. Nearly every team used pre-trained Arabic language models. The best results came from morphological segmentation, losses that respect label order, handling of class imbalance, and ensembles. The winning team, !MSA, reached 87.5 QWK on sentences and 87.4 on documents. Adding outside data rarely improved on the strict-track results. The task sets a common benchmark for Arabic text leveling.

## Key points
- **BAREC Corpus:** 1,922 documents, 69,441 sentences and about 1.04M words.
  - It is split roughly 80/10/10 by document.
  - The blind test set adds 100 documents and 3,420 sentences.
- **Tracks:**
  - Strict allows BAREC only.
  - Constrained adds the SAMER Corpus (4,289 documents and 20,358 fragments, three levels) and the SAMER Lexicon (40K lemma+POS pairs, five levels).
  - Open allows any data.
- **Metrics:**
  - QWK is primary.
  - Exact accuracy is reported at 19 levels and also collapsed to 7, 5 and 3 levels.
  - Off-by-one accuracy and average distance (MAE) are also reported.
- **Winning system (!MSA):**
  - The text is pre-tokenized with D3Tok.
  - The ensemble combines AraBERTv2, AraElectra, MARBERT and CamelBERT.
  - These models are trained with cross-entropy, Ordinal Log Loss, MSE regression and Conditional Ordinal Regression.
  - Outputs are merged by a weighted average based on model confidence.
  - Track-specific augmentation included upsampling, SAMER data, and Gemini paraphrases of 12k BAREC entries.
- **Overall findings:**
  - Five teams beat the strongest baseline.
  - LLMs, GNNs and vision-language models did not beat PLM-based systems.
  - Classic ML and n-gram features were rarely used and were not competitive.

## Interesting ideas
- Mixing classification, regression and ordinal losses inside one ensemble treats ordered labels as ordered, so a near miss costs less than a far miss; this fits any graded label.
- Separating strict, constrained and open tracks shows how much extra resources really help, and here they mostly did not.
- Reporting exact accuracy at several collapsed granularities, plus off-by-one accuracy and MAE, shows whether errors are small or severe on a fine scale.
- Defining document difficulty as its hardest sentence is simple and explainable, though the authors note it biases documents upward.
- Morphological segmentation before modeling was a common factor among the top systems in a morphologically rich language.

## For Mova

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

- **I1** (learn): When training scorers with graded outputs, such as parser confidence tiers or difficulty bands, ensemble heads trained with ordinal and regression losses instead of plain softmax.
- **I2** (learn): Evaluate every model trained with added silver data or LLM-teacher labels against a gold-only "strict" run, so that gains from external supervision are measured instead of assumed.
- **I3** (answer): Score graded outputs, like the difficulty of school-course tests or numeric answers in word problems, both exactly and with distance-aware tolerance, so that near misses are not hidden behind a single accuracy number.
- **I4** (answer): A reader could rate each sentence of a fable or tale and point to the hardest one as the likely place comprehension breaks, while keeping the upward bias in mind.
- **I5** (tokens): This supports keeping explicit morphological and lemma analysis as a feature layer that downstream scorers can use, rather than relying only on raw subwords.

*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

- [New York University Abu Dhabi](https://md.research.elamur.ai/free/en/universities/new-york-university-abu-dhabi/) — University, Abu Dhabi, United Arab Emirates · 122 works · 4,602 citations
- [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) — University, Abu Dhabi, United Arab Emirates · 124 works · 5,676 citations

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## Cite

Cite as: Khalid N. Elmadani, Bashar Alhafni, Hanada Taha and Nizar Habash. 2025. BAREC Shared Task 2025 on Arabic Readability Assessment. Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks.

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

```bibtex
@inproceedings{elmadani2025barec,
  title = {{BAREC Shared Task 2025 on Arabic Readability Assessment}},
  author = {Khalid N. Elmadani, Bashar Alhafni, Hanada Taha and Nizar Habash},
  booktitle = {Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks},
  year = {2025},
  doi = {10.18653/v1/2025.arabicnlp-sharedtasks.34},
  url = {https://doi.org/10.18653/v1/2025.arabicnlp-sharedtasks.34}
}
```

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*Updated 1 November 2026*