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.

Sentence segmentation in Arabic is challenging due to ambiguous and inconsistent punctuation, with many texts lacking reliable sentence boundary markers. Existing approaches rely heavily on punctuation cues and are typically evaluated on well-formed text, limiting their robustness in realistic Arabic settings. To address this, we introduce AraSEG, a genre-diverse sentence segmentation corpus spanning eight genres and a wide range of punctuation and document structure conditions. Using AraSEG, we evaluate LLMs, lightweight encoder models, and dependency parser-based models under increasingly challenging segmentation settings. Our experiments show that lightweight encoders, and even dependency parser-based models, outperform LLMs under the hardest conditions. We further investigate the effects of training data size and genre diversity, finding that performance eventually saturates and cross-genre generalization remains challenging. We also demonstrate that accurate sentence segmentation substantially improves downstream dependency parsing. We make our code, data, and models publicly available.

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 the findings of the Arabic Sentence Segmentation (AraSEG) Shared Task, organized at the Fourth Arabic Natural Language Processing Conference (ArabicNLP 2026). Given an Arabic document, the task requires systems to identify sentence boundaries and segment the text into coherent sentences. Systems are evaluated on the genre-diverse AraSEG corpus under four input settings that vary in punctuation availability and paragraph structure, each with a Closed Track restricted to the provided training data and an Open Track permitting publicly available external resources. 22 teams from 13 countries registered, 16 submitted systems, and 14 submitted system-description papers. Across the eight subtask-track combinations, the best systems achieve F1 scores ranging from 89.1 to 95.3. Performance declines as segmentation cues are removed, with the largest drop occurring when both punctuation and paragraph structure are unavailable. Pretrained-encoder ensembles and careful prediction calibration emerge as the most effective approaches, while gains from LLMs, external data, and complex architectures vary across systems and settings.

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.

The impressive advancement of Large Language Models (LLMs) in English has not been matched across all languages. In particular, LLM performance in Arabic lags behind, due to data scarcity, linguistic diversity of Arabic and its dialects, morphological complexity, etc. Progress is further hindered by the quality of Arabic benchmarks, which typically rely on static, publicly available data, lack comprehensive task coverage, or do not provide dedicated platforms with blind test sets. This makes it challenging to measure actual progress and to mitigate data contamination. Here, we aim to bridge these gaps. In particular, we introduce BALSAM, a comprehensive, community-driven benchmark aimed at advancing Arabic LLM development and evaluation. It includes 78 NLP tasks from 14 broad categories, with 52K examples divided into 37K test and 15K development, and a centralized, transparent platform for blind evaluation. We envision BALSAM as a unifying platform that sets standards and promotes collaborative research to advance Arabic LLM capabilities.

Training neural models for translating between low-resource languages is challenging due to the scarcity of direct parallel data between such languages. Pivot-based neural machine translation (NMT) systems overcome data scarcity by including a high-resource pivot language in the process of translating between low-resource languages. We propose synthetic pivoting, a novel approach to pivot-based translation in which the pivot sentences are generated synthetically from both the source and target languages. Synthetic pivot sentences are generated through sequence-level knowledge distillation, with the aim of changing the structure of pivot sentences to be closer to that of the source or target languages, thereby reducing pivot translation complexity. We incorporate synthetic pivoting into two paradigms for pivoting: cascading and direct translation using synthetic source and target sentences. We find that the performance of pivot-based systems highly depends on the quality of the NMT model used for sentence regeneration. Furthermore, training back-translation models on these sentences can make the models more robust to input-side noise. The results show that synthetic data generation improves pivot-based systems translating between low-resource Southern African languages by up to 5.6 BLEU points after fine-tuning.

The paper describes the University of Cape Town’s submission to the constrained track of the WMT22 Shared Task: Large-Scale Machine Translation Evaluation for African Languages. Our system is a single multilingual translation model that translates between English and 8 South / South East African Languages, as well as between specific pairs of the African languages. We used several techniques suited for low-resource machine translation (MT), including overlap BPE, back-translation, synthetic training data generation, and adding more translation directions during training. Our results show the value of these techniques, especially for directions where very little or no bilingual training data is available.