This paper presents a set of bilingual Standard Arabic (SA)-Moroccan Sign Language (MSL) tools and resources to improve Moroccan Deaf children’s SA skills. An MSL Generator based on rule-based machine translation (MT) is described that enables users and educators of Deaf children, in particular, to enter Arabic text and generate its corresponding MSL translation in both graphic and video format. The generated graphics can be printed and imported into an Arabic reading passage. We have also developed MSL Clip and Create software that includes a bilingual database of 3,000 MSL signs and SA words, a Publisher for the incorporation of MSL graphic support into SA reading passages, and six Templates that create customized bilingual crossword puzzles, word searches, Bingo cards, matching games, flashcards, and fingerspelling scrambles. A crowdsourcing platform for MSL data collection is also described. A major social benefit of the development of these resources is in relation to equity and the status of deaf people in Moroccan society. More appropriate resources for the bilingual education of Deaf children (in MSL and SA) will lead to improved quality of educational services.

Large Language Models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks across different languages. However, their performance in low-resource languages and dialects, such as Moroccan Arabic (MA), requires further investigation. This study evaluates the performance of ChatGPT-4, different fine-tuned BERT models, FastText as text representation, and traditional machine learning models on MA sentiment analysis. Experiments were done on two open source MA datasets: an X(Twitter) Moroccan Arabic corpus (MAC) and a Moroccan Arabic YouTube corpus (MYC) datasets to assess their capabilities on sentiment text classification. We compare the performance of fully fine-tuned and pre-trained Arabic BERT-based models with ChatGPT-4 in zero-shot settings.