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The use of machine translation has become ubiquitous across various translation practices, especially with the advent of neural machine translation and the integration of deep learning and artificial intelligence in translation program development. While the accuracy and quality of machine translation outcomes have significantly improved, challenges persist particularly in legal translation from English to Arabic. The unique nature of legal discourse and structural differences between English and Arabic make accurately translating legal language features a daunting task. This study aims to evaluate the quality of neural machine translation in rendering legal Latin phraseology into Arabic by comparing two websites: Google Translate and Yandex. A corpus-based approach was adopted where 270 Latin-origin legal terms and phrases were collected, scrutinised, and translated using both platforms. The evaluation focuses on four criteria: inappropriate translations, no translations provided, borrowing (phonetic transliteration into Arabic), and equivalence—the culturally and functionally suitable translation. Key findings indicate that despite significant advancements in machine translation technology, accuracy remains a critical issue, with approximately half of the terms not translated correctly. While Google Translate is widely used, Yandex demonstrated higher accuracy in this context. Furthermore, the majority of phrases selected for this study were not accurately translated by either website. The solution to this problem lies in enhancing the training process. Arabic users and translators should contribute more translations to enrich Arabic corpora online. Additionally, it's been observed that there is a lack of English-Arabic dictionaries or databases dedicated to Legal Language Processing (LLP). Therefore, initiating a research project addressing this issue could be of utmost importance. Regarding specialized language, improving the quality of Neural Machine Translation (NMT) raises questions about its reliability for both learners and professional translators. Accordingly, the study recommends further research on assessing machine translation quality, improving neural machine translation terminology accuracy, and enhancing machine learning models with more Arabic content and corpora.
Sofiane Djeffal (Tue,) studied this question.