Leveraging AI-Powered Neural Machine Translation to Bridge the Gap: Translating Arabic Islamic Terminology into English

Abstract

This paper assesses the appropriateness of NMT in translating Arabic Islamic terms into English, determining the main issues, and providing solutions. Some of the key concerns highlighted in the study are the scarcity of large corpora, the inability of NMT to deal with specific features of languages and the lack of adequate context and cultural supplements. In response to these challenges, four main recommendations have been highlighted in the report as follows. First, it prescribes the formation of special corpora containing various Islamic materials like the Quran, Hadiths, and theological writings. This will help to feed NMT systems with the inherent contextually relevant content data for the most work-related tasks. Secondly, it recommends the integration of other approaches with NMT such as rule-based and statistical translation that can handle idiomatic expressions and contextual interpretation. Third, it adopts the post-editing technique whereby the machine-translated text is checked by human translators to ensure adherence to cultural and contextual considerations. Lastly, the report urges more multi-ethnic cooperation among researchers, innovators, and translators to improve the accuracy and usefulness of translations. The paper finds that although NMT has advanced greatly in the field of translation technology, there are still challenges in translating complicated works in the Arabic and Islamic contexts. The adoption of these suggestions could enhance the quality of translations by a significant notch, thus producing translations that are more precise and sensitive to cultural differences. Thus, the conclusions drawn from the research affect the development of new technologies and cooperation in machine translation. © (2024), (Elite Scientific Publications). All rights reserved.