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

• URL: http://arxiv.org/abs/2502.13520v1
• Date: Wed, 19 Feb 2025 08:16:11 GMT
• Title: A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment
• Authors: Khalid N. Elmadani, Nizar Habash, Hanada Taha-Thomure,
• Abstract summary: This paper introduces the Balanced Arabic Readability Evaluation Corpus BAREC. BAREC consists of 68,182 sentences spanning 1+ million words, carefully curated to cover 19 readability levels. The corpus was fully manually annotated by a large team of annotators.
• Score: 10.853984915007961
• License:
• Abstract: This paper introduces the Balanced Arabic Readability Evaluation Corpus BAREC, a large-scale, fine-grained dataset for Arabic readability assessment. BAREC consists of 68,182 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 81.3%, 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 will make BAREC openly available, along with detailed annotation guidelines and benchmark results.

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