The Analysis of Language Information in Translation System Under Deep Learning

Under the trend of economic globalization, communication involving different languages is becoming more and more frequent. Traditional manual translation and conventional machine translation methods (such as rule-based and statistical methods) have been unable to fully meet people's urgent and massive demands for high-quality translations. In view of the low accuracy of long and difficult sentences in translation, this paper proposes an innovative English translation system based on a deep learning model and language information management to improve accuracy and enhance the cultural confidence of translators. First, multi-granular morphology is combined with deep learning to pretrain the translated language and text information with multi-granular feature vectors as preprocessing of language and text. Then the neural network model is used to architect the machine translation model to translate the preprocessed language and text. Finally, a bilingual evaluation is conducted.