[Retracted] Deep Learning Model‐Based Machine Learning for Chinese and Japanese Translation

要旨

This paper takes deep learning as the background of researcher design, combines the relevant cutting‐edge research results in recent years, addresses the linguistic characteristics of Japanese and the problems faced by completing Japanese machine translation system, and determines the neural network structure of encoding‐decoding for Japanese translation based on the characteristics of high similarity between Japanese and Chinese and after referring to the neural network architecture of English translation, and the basic structure and the corresponding improvement of the hidden layer unit calculation are carried out. The training model is optimized and an integrated Japanese machine translation system is implemented. Finally, the translation models of Japanese and Chinese intertranslation and Japanese and Chinese intertranslation are tested, and the optimal model fusion achieves a BLEU value of 39.52.

主題