We present BAREC-10M, an expanded version of the Balanced Arabic Readability Evaluation Corpus (BAREC). This new release extends the original 1M-word corpus to 10 million words and broadens its scope to include balanced multi-domain coverage annotated for morphology, syntax, and readability. The corpus integrates 45 sub-corpora drawn from diverse sources, including news, educational materials, literature, children’s texts, and religious discourse. Each text is labeled for domain, readership level, and genre, and automatically analyzed using state-of-the-art morphological and syntactic tools. To enhance coverage of underrepresented varieties, we manually digitized and included children’s materials, magazines, and curriculum-based content. The resulting dataset provides a balanced resource for studying Arabic linguistic variation across styles, audiences, and levels of complexity.

We present BAREC-LexBench, a benchmark for Arabic lemma-level readability assessment, designed to evaluate readability lexicons and computational models independently of their granularity levels. We build the benchmark through a large-scale pairwise comparison task, where annotators determine which of two Arabic lemmas is easier to read and understand. The benchmark consists of 36,000 annotated pairs covering both same- and cross-part-of-speech comparisons among open-class lemmas: adjectives, nouns, proper nouns, and verbs. The annotations were produced by expert Arabic educators with an average inter-annotator agreement of 77.7% (0.56 Kappa ∼ moderate agreement), and we analyze disagreement patterns to better understand where lemma-level readability judgments are most challenging. We benchmark a wide range of approaches, from readability lexicons and frequency-based lexicons to trainable models and large language models (LLMs). Finally, we demonstrate downstream uses of the benchmark by constructing a ranked readability lexicon covering open-class lemmas (BAREC-LexRank) and evaluating its contribution to sentence-level readability assessment. To support research and education, we make the dataset, ranked lexicon, and benchmark results openly available.

We present the results and findings of the BAREC Shared Task 2026, the second shared task on Arabic readability assessment, organized as part of the Fourth Arabic Natural Language Processing Conference (ArabicNLP 2026). The shared task focuses on automatic readability assessment using the BAREC Corpus, addressing fine-grained classification across 19 readability levels. It comprises two subtasks —sentence-level classification and document-level classification— and three tracks: (1) the Strict Track, which permits only the BAREC Corpus; (2) the Constrained Track, which permits the BAREC Corpus, the SAMER Corpus, and the SAMER Lexicon; and (3) the Open Track, which permits the use of any external resources. A total of 14 teams from 12 countries registered for the task, of which 10 submitted system description papers. All participating teams competed only in the sentence-level classification subtask. The top-performing system achieved a quadratic weighted kappa (QWK) of 85.3.

We present BAREC-TB, the first dependency treebank of Arabic children’s literature and educational texts. The 156K-word corpus includes texts drawn from eight sources, including curricula, reading materials, children’s stories, poems, and songs. The treebank is annotated according to the Columbia Arabic Treebank (CATiB) dependency representation. We compare BAREC-TB with existing Arabic treebanks, including the Penn Arabic Treebank and Camel Treebank, using corpus statistics, dependency-based measures of syntactic complexity, readability and vocabulary-related features. We also establish parsing benchmarks using both a trainable parser and large language models. The corpus will be made publicly available to support Arabic NLP, educational NLP, and language pedagogy research: https://barec.camel-lab.com/.

This paper presents the annotation guidelines of the Balanced Arabic Readability Evaluation Corpus (BAREC), a large-scale resource for fine-grained sentence-level readability assessment in Arabic. BAREC includes 69,441 sentences (1M+ words) labeled across 19 levels, from kindergarten to postgraduate. Based on the Taha/Arabi21 framework, the guidelines were refined through iterative training with native Arabic-speaking educators. We highlight key linguistic, pedagogical, and cognitive factors in determining readability and report high inter-annotator agreement: Quadratic Weighted Kappa 81.8% (substantial/excellent agreement) in the last annotation phase. We also benchmark automatic readability models across multiple classification granularities (19-, 7-, 5-, and 3-level). The corpus and guidelines are publicly available: http://barec.camel-lab.com.

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: http://barec.camel-lab.com.

We present BAREC Demo, a web-based system for fine-grained, sentence-level Arabic readability assessment. The demo is part of the Balanced Arabic Readability Evaluation Corpus (BAREC) project, which manually annotated 69,000 sentences (over one million words) from diverse genres and domains using a 19-level readability scale inspired by the Taha/Arabi21 framework, covering reading abilities from kindergarten to postgraduate levels. The project also developed models for automatic readability assessment.The demo provides two main functionalities for educators, content creators, language learners, and researchers: (1) a Search interface to explore the annotated dataset for text selection and resource development, and (2) an Analyze interface, which uses trained models to assign detailed readability labels to Arabic texts at the sentence level.The system and all of its resources are accessible at https://barec.camel-lab.com.

We present the results and findings of the BAREC Shared Task 2025 on Arabic Readability Assessment, organized as part of The Third Arabic Natural Language Processing Conference (ArabicNLP 2025). The BAREC 2025 shared task focuses on automatic readability assessment using BAREC Corpus, addressing fine-grained classification into 19 readability levels. The shared task includes two sub-tasks: sentence-level classification and document-level classification, and three tracks: (1) Strict Track, where only BAREC Corpus is allowed; (2) Constrained Track, restricted to the BAREC Corpus, SAMER Corpus, and SAMER Lexicon, and (3) Open Track, allowing any external resources. A total of 22 teams from 12 countries registered for the task. Among these, 17 teams submitted system description papers. The winning team achieved 87.5 QWK on the sentence-level task and 87.4 QWK on the document-level task.