mlsanityData to 14 Oct 2026

A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment

arXiv 2502.13520 ↗First posted 19 Feb 2025cs.CLcited by 8

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Abstract (arXiv)

This paper introduces the Balanced Arabic Readability Evaluation Corpus (BAREC), a large-scale, fine-grained dataset for Arabic readability assessment. BAREC consists of 69,441 sentences spanning 1+ million words, carefully curated to cover 19 readability levels, from kindergarten to postgraduate comprehension. The corpus balances genre diversity, topical coverage, and target audiences, offering a comprehensive resource for evaluating Arabic text complexity. The corpus was fully manually annotated by a large team of annotators. The average pairwise inter-annotator agreement, measured by Quadratic Weighted Kappa, is 81.8%, reflecting a high level of substantial agreement. Beyond presenting the corpus, we benchmark automatic readability assessment across different granularity levels, comparing a range of techniques. Our results highlight the challenges and opportunities in Arabic readability modeling, demonstrating competitive performance across various methods. To support research and education, we make BAREC openly available, along with detailed annotation guidelines and benchmark results.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 11 of its 57 references that are in the map. First posted on arXiv 19 Feb 2025.

Builds on

11 of 57 references.

References · 11

  1. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 362
  2. 2020ARBERT & MARBERT: Deep Bidirectional Transformers for Arabiccited by 166
  3. 2021The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Modelscited by 119
  4. 2019Making Better Mistakes: Leveraging Class Hierarchies with Deep Networkscited by 113
  5. 2024ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabiccited by 88
  6. 2019Text Readability Assessment for Second Language Learnerscited by 54
  7. 2021Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Featurescited by 36
  8. 2020Linguistic Features for Readability Assessmentcited by 35
  9. 2021Trends, Limitations and Open Challenges in Automatic Readability Assessment Researchcited by 28
  10. 2023ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessmentcited by 19
  11. 2024The SAMER Arabic Text Simplification Corpuscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025ArabJobs: A Multinational Corpus of Arabic Job Adscited by 3
  2. 2026Beyond LLM-as-a-Judge: Deterministic Metrics for Multilingual Generative Text Evaluationcited by 3
  3. 2026Arabic Sentence Segmentation Across Genres and Punctuation Conditionscited by 1
  4. 2025Lemmatization as a Classification Task: Results from Arabic across Multiple Genrescited by 0
  5. 2025!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessmentcited by 0
  6. 2025mucAI at BAREC Shared Task 2025: Towards Uncertainty Aware Arabic Readability Assessmentcited by 0
  7. 2025Lexicon-Enriched Graph Modeling for Arabic Document Readability Predictioncited by 0
  8. 2026Can LLMs Control Readability? A Multi-Dimensional Evaluation Framework for CEFR-Controlled Arabic Generationcited by 0