A Diachronic Assessment of Research on Machine Translation Methodology

Abstract

Approaches of machine translation are streamlined into three types based on strategies of knowledge processing, i.e. syntax-based methods, rule-based methods, and corpus-based methods, with their merits and demerits assessed. Consequently, the updated research tendency of the neural networks, as the current mainstream methods of machine translation, mainly comprising computational complexity reducing, words alignment enhancing, prior knowledge and constraints incorporating are stressed. Eventually, the future development orientations of the mainstream methods of neutral machine translation, particularly that of networks integration, data parallelizing and training are prospected.