Khalid N. Elmadani

Yousef Altaher, Ali Fadel, Mazen Alotaibi et al.

Masader (Alyafeai et al., 2021) created a metadata structure to be used for cataloguing Arabic NLP datasets. However, developing an easy way to explore such a catalogue is a challenging task. In order to give the optimal experience for users and researchers exploring the catalogue, several design and user experience challenges must be resolved. Furthermore, user interactions with the website may provide an easy approach to improve the catalogue. In this paper, we introduce Masader Plus, a web interface for users to browse Masader. We demonstrate data exploration, filtration, and a simple API that allows users to examine datasets from the backend. Masader Plus can be explored using this link https://arbml.github.io/masader. A video recording explaining the interface can be found here https://www.youtube.com/watch?v=SEtdlSeqchk.

Khalid N. Elmadani, Francois Meyer, Jan Buys

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.

Khalid N. Elmadani, Nizar Habash, Hanada Taha-Thomure

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.

Nizar Habash, Hanada Taha-Thomure, Khalid N. Elmadani et al.

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.

Khalid N. Elmadani, Mukhtar Elgezouli, Anas Showk

Fine-tuning a pretrained BERT model is the state of the art method for extractive/abstractive text summarization, in this paper we showcase how this fine-tuning method can be applied to the Arabic language to both construct the first documented model for abstractive Arabic text summarization and show its performance in Arabic extractive summarization. Our model works with multilingual BERT (as Arabic language does not have a pretrained BERT of its own). We show its performance in English corpus first before applying it to Arabic corpora in both extractive and abstractive tasks.