From Analysis to Implementation: A Comprehensive Review for Advancing Arabic-English Machine Translation

Abstract

In an increasingly interconnected world, the demand for accurate Arabic-English translation has surged, highlighting the complexities in handling Arabic's intricate morphology and diverse linguistic structures. This research delves into various translation models, including Convolutional Neural Networks (CNNs), LSTM, Neural Machine Translation (NMT), BERT, and innovative fusion architectures like the Transformer-CNN. Each model's strengths and limitations are scrutinized through comprehensive evaluations and comparisons, unveiling their potential to address translation challenges. The research then builds two models, the first based on LSTM and the second on BERT, and tests their performance in translating English to Arabic. The paper then conducts an in-depth analysis of the results. The comparative analysis provides insights into the landscape of Arabic-English translation models, guiding future research toward refining models, leveraging diverse datasets, and establishing standardized evaluation benchmarks to bridge existing gaps.