From Text to Arabic Translation: A Rule-Based Machine Translation System

The translation between Arabic and Arabic Sign Language (ArSL) is a significant challenge, as the two languages have distinct structures, word orders, and lexicons. This language gap contributes to the isolation of deaf people from society, emphasizing the need for effective translation systems.

In this context, we propose a rule-based machine translation system to translate Arabic text into ArSL. Our approach involves a morphological, syntactic, and semantic analysis of an Arabic sentence to produce a sentence with the grammar and structure of ArSL. This system is designed to overcome the language barriers faced by deaf people in Arab countries.

Our evaluation on a corpus of sentences reveals that our translation system provides accurate translations for more than 80% of the translated sentences. Furthermore, we have developed a parallel corpus in the health domain, consisting of 600 sentences, which will be freely available for researchers to utilize and improve upon our work.

To facilitate transcription of ArSL, we propose a gloss system that can represent this language. This system enables the representation of ArSL by analyzing its morphological, syntactic, and semantic components.

The development of this translation system is crucial for bridging the gap between Arabic-speaking communities and deaf individuals. By providing an accurate and reliable means of communication, we aim to empower deaf people in Arab countries to participate more fully in society.

In conclusion, our rule-based machine translation system offers a promising solution to the challenge of translating Arabic text into ArSL. We believe that this technology has the potential to significantly improve the lives of deaf individuals in Arab countries and promote greater understanding between different linguistic communities.

To arabic translation