Advances in Translation Technology: A Systematic Review of Recent Developments

English Language

Translation technology has undergone significant advancements in recent years, driven by innovations in artificial intelligence, neural networks, and hybrid approaches. This systematic review explores recent developments in translation technology, focusing on neural machine translation (NMT), computer-assisted translation (CAT) tools, and AI-driven solutions for speech-to-text and text-to-speech systems. The review highlights their applications in various domains, including business communication, multilingual education, and real-time language interpretation. Despite significant progress, linguistic nuances, data limitations, and ethical concerns persist. This paper identifies emerging trends, such as context-aware translation systems and improved support for low-resource languages, and discusses their potential to shape the future of translation technology. The findings provide valuable insights for researchers, developers, and practitioners in the field, emphasizing the need for continuous innovation to enhance accuracy, accessibility, and ethical practices in translation technologies.

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