Advancing Machine Translation: A Comparative Analysis of Evolving Technologies
D. I. De Silva, D. G. P. Hansadi · 2024
This paper presents a comprehensive analysis of evolving machine translation technologies, focusing on Rule-Based, Statistical, Neural, and Hybrid approaches. The study examines the historical development, strengths, and limitations of each method, with Neural Machine Translation highlighted as the most promising due to its ability to learn translation patterns directly from data. Key recommendations for advancing machine translation include enhancing data quality, leveraging computational resources, and fostering interdisciplinary collaboration. The paper emphasizes the need for standardizing evaluation metrics and creating benchmark datasets to facilitate fair comparisons. By integrating these recommendations, the field of machine translation can move towards more accurate, efficient, and user-centric solutions for multilingual communication, ultimately bridging linguistic barriers and enhancing global interactions.