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Since the early stages of schooling, many children are exposed to different learning disabilities, usually manifest as dyslexia, dysgraphia, and dyscalculia. Those disabilities impact on the normal academic achievement of the child and may even affect its social life. Learning disorders have neurobiological origins and are often inherited regardless of the geographical region or the socio-economic status. It should not be seen as physical disorders (visual or hearing handicaps or mental retardation). They are simply a different way of acquiring new knowledge and skills. Learning disabled children only require an early intervention, adjustments in the teaching process, and compensatory efficient strategies. They need more encounters with words and more practice and revision opportunities. For the reason to follow the normal academic achievement process, either manual or electronic tools have been introduced to help children with learning disabilities (LD). Our contribution is a set of games for children with dyslexia and dysgraphia. The games delicately target Arabic learning disabilities and lend a helping hand to the child with LD to overcome his/her reading and writing complications. This set of games is evaluated and tested by 46 pupils, aged from seven to nine years old, from whom 20 pupils are considered as individuals with learning problems in general and 5 students determined as dyslexics. The results statistically reflect the system's efficiency and its distinctive role in improving the learning process.

Text readability is one of the main research areas widely developed in several languages, but it is highly limited when dealing with the Arabic language. The main challenge in this area is to identify an optimal set of features that represent texts and allow us to evaluate their readability level. To address this challenge, we propose in this study various feature selection methods that can significantly retrieve the set of discriminating features representing Arabic texts. The second aim of this paper is to evaluate different sentence-embedding approaches (ArabicBert, AraBert and XLM-R) and compare their performances to those obtained using the selected linguistic features. We performed experiments with both SVM and Random Forest classifiers on two different corpora dedicated to learning Arabic as a foreign language (L2). The obtained results show that reducing the number of features improves the performance of the readability-prediction models by more than 25% and 16% for the two adopted corpora, respectively. In addition, the fine-tuned Arabic-BERT model performs better than the other sentence-embedding methods, but it provided less improvement than the feature-based models. Combining these methods with the most discriminating features produced the best performance. [ABSTRACT FROM AUTHOR]

Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the crosslanguage studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentencealignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages. [ABSTRACT FROM AUTHOR]