A Review on Research and Applications of Machine Translation

Machine translation, which is a crucial research area within the domain of natural language processing, aims to automatically convert source language sentences into target language sentences while keeping semantic consistency to the greatest extent possible. In recent years, with the continuous progress of artificial intelligence and deep learning technologies, neural machine translation models have gradually become the dominant force in machine translation. When compared with the approaches of statistical machine translation, neural machine translation models have demonstrated significant improvements in translation reliability and accuracy. They can even attain levels similar to those of human translation experts in various translation tasks. As a result, neural machine translation technology has been widely applied in online translation systems such as Google and Baidu to meet the online translation requirements of a large number of Internet users. However, since neural machine translation models adopt the self-regressive decoding method, when decoding the target language sentence, it is required to decode word by word, and the time needed to generate the entire target language sentence will increase as the sequence length grows. This limitation restricts the application and promotion of neural machine translation models in online commercial translation systems.