Translation Systems: A Synoptic Survey of Deep Learning Approaches and Techniques

Within artificial intelligence, deep learning is a subset that emulates the information processing mechanism of the human brain. allowing machines to learn and make decisions on their own. This technology has revolutionized the field of language translation. Deep learning models analyze vast amounts of linguistic data, allowing visualization of context, and idiomatic expressions. This results in translations that read more naturally and capture the true essence of the source text. In order to accomplish the goal of higher accuracy, this study explores a variety of methodologies, including rule-based, neural-based, hybrid-based, and statistical-based machine translation systems, along with associated pre-processing strategies. It details the process of data collection, pre-processing, model building, training, and evaluation. The paper concludes by highlighting its key contribution in the field.