metadata

license: cc-by-sa-4.0
task_categories:
  - text-classification
language:
  - ar
tags:
  - readability
size_categories:
  - 10K<n<100K
pretty_name: BAREC Corpus v1.0

BAREC Corpus v1.0

Dataset Summary

BAREC (the Balanced Arabic Readability Evaluation Corpus) is a large-scale dataset for fine-grained Arabic readability assessment. The dataset includes over 1M words, annotated at the sentence level across 19 readability levels, with additional mappings to coarser 7, 5, and 3 level schemes.

Supported Tasks

The dataset supports multi-class readability classification in the following formats:

19 levels (default)
7 levels
5 levels
3 levels

Languages

Arabic (Modern Standard Arabic)

Dataset Structure

Data Instances

{'ID': 10100010008, 'Sentence': 'عيد سعيد', 'Word_Count': 2, 'Word': 'عيد سعيد', 'Lex': 'عيد سعيد', 'D3Tok': 'عيد سعيد', 'D3Lex': 'عيد سعيد', 'Readability_Level': '2-ba', 'Readability_Level_19': 2, 'Readability_Level_7': 1, 'Readability_Level_5': 1, 'Readability_Level_3': 1, 'Annotator': 'A4', 'Document': 'BAREC_Majed_0229_1983_001.txt', 'Source': 'Majed', 'Book': 'Edition: 229', 'Author': '#', 'Domain': 'Arts & Humanities', 'Text_Class': 'Foundational'}

Data Fields

ID: Unique sentence identifier.
Sentence: The sentence text.
Word_Count: Number of words in the sentence.
Word: Simply tokenized and dediacritized sentences.
Lex: Each word is replaced by its predicited lemma (dediacritized).
D3Tok: We tokenize words into their base and clitics forms.
D3Lex: We replace the base forms in D3Tok with the predicited lemmas.
Readability_Level: The readability level in 19-levels scheme, ranging from 1-alif to 19-qaf.
Readability_Level_19: The readability level in 19-levels scheme, ranging from 1 to 19.
Readability_Level_7: The readability level in 7-levels scheme, ranging from 1 to 7.
Readability_Level_5: The readability level in 5-levels scheme, ranging from 1 to 5.
Readability_Level_3: The readability level in 3-levels scheme, ranging from 1 to 3.
Annotator: The annotator ID (A1-A5 or IAA).
Document: Source document file name.
Source: Document source.
Book: Book name.
Author: Author name.
Domain: Domain (Arts & Humanities, STEM or Social Sciences).
Text_Class: Readership group (Foundational, Advanced or Specialized).

Data Splits

The BAREC dataset has three splits: Train (80%), Dev (10%), and Test (10%).
The splits are in the document level.
The splits are balanced accross Readability Levels, Domains, and Text Classes.

Evaluation

We define the Readability Assessment task as an ordinal classification task. The following metrics are used for evaluation:

Accuracy (Acc¹⁹): The percentage of cases where reference and prediction classes match in the 19-level scheme.
Accuracy (Acc⁷, Acc⁵, Acc³): The percentage of cases where reference and prediction classes match after collapsing the 19 levels into 7, 5, or 3 levels, respectively.
Adjacent Accuracy (±1 Acc¹⁹): Also known as off-by-1 accuracy. The proportion of predictions that are either exactly correct or off by at most one level in the 19-level scheme.
Average Distance (Dist): Also known as Mean Absolute Error (MAE). Measures the average absolute difference between predicted and true labels.
Quadratic Weighted Kappa (QWK): An extension of Cohen’s Kappa that measures the agreement between predicted and true labels, applying a quadratic penalty to larger misclassifications (i.e., predictions farther from the true label are penalized more heavily).

Citation

If you use BAREC in your work, please cite the following papers:

@inproceedings{elmadani-etal-2025-readability,
    title = "A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment",
    author = "Elmadani, Khalid N.  and
      Habash, Nizar  and
      Taha-Thomure, Hanada",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{habash-etal-2025-guidelines,
    title = "Guidelines for Fine-grained Sentence-level Arabic Readability Annotation",
    author = "Habash, Nizar  and
      Taha-Thomure, Hanada  and
      Elmadani, Khalid N.  and
      Zeino, Zeina  and
      Abushmaes, Abdallah",
    booktitle = "Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX)",
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics"
}