Machine Translation Based on Neural Network: A Case Study of Est Translation

Machine translation based on neural network plays an important role in EST translation. EST translation involves the processing of technical terms and complex sentence patterns, which requires accurate translation and fluent language. By building a neural network model and learning a large number of corpora, neural network machine translation can better understand and express the internal logic and semantic information of language, thus improving the accuracy and fluency of EST translation. Global science and technology giants have successively launched neural machine translation systems based on neural network algorithms. Compared with statistical machine translation languages, neural machine translation is more fluent and accurate, optimizing translation services. Machine translation is the process of translating text from one language to another. It has been used for many years and proved to be effective in the field of language translation. Machine translation (MT) is an automatic system that translates text into other languages. It is considered as a form of artificial intelligence (AI). Machine translation software can be used by humans and computers; However, it requires human intervention in order to correctly interpret or modify errors. This paper will discuss machine translation based on neural network. We will compare two methods applicable to different types of data sets: supervised learning method and unsupervised learning method.