ARCHIVES

Abstract: Deep learning models have recently demonstrated enormous advancements in machine translation. These systems require a lot of memory and have high processing costs. This paper offers an overview of deep learning architectures used in machine translation. This paper focuses on designing a transformer-based language translator and analyzes the performance of language translators by comparing other models. Despite the large number of studies that have been offered over the past few years, nothing has been done to examine how this new technological transformer is developing.This analysis of the existing literature's traces the origins of the ideas behind neural machine translation, examines the key branches, and contends on some possible future research directions in this field.