A Survey on Current Trends in Machine Translation Models for Arabic to English Translation
Ayesha Akhtar · 2026
A very important application of natural language processing happens to be machine translation (MT). As the world becomes more inclusive of different nationalities with cooperation in various domains becoming commonplace, effective and unambiguous communication becomes mandatory. In this context, effective and articulate communication plays a crucial role. English happens to be the most spoken language while Arabic happens to be the fifth most spoken language globally. Both languages are very different in the historical background, etymology, scripts with geographical and cultural differences, which happens to be the main challenge to be addressed in the translation of Arabic to English. Therefore, it is important to convert Arabic to English through a machine translation model which is trained on bilingual corpora incorporating both structural as well as semantic differences of the two languages. This research work analyzes the effectiveness of Statistical Machine Translation and Neural Machine Translation (NMT) for the conversion of Arabic Texts to English. This study adopts a comparative analytical research design to evaluate the performance of benchmark SMT and NMT models for Arabic to English translation. The research aims to assess these models based on several metrics, including translation accuracy, fluency, adequacy, and contextual handling. The current benchmark models along with their salient points have been discussed. The important baseline metrics have been mathematically defined and their significance is highlighted. Finally, potential future research directions have been presented.