Research on Grammatical Structure Analysis and Neural Network Optimization in Japanese Machine Translation

This paper focuses on the grammatical structure analysis and neural network optimization method in Japanese machine translation, aiming at improving the fluency and accuracy of translation. In this paper, a Japanese grammar parsing module combining dependency syntax and case grammar is proposed. Through the improved double affine attention mechanism and conditional random field (CRF) model, the dependency of Japanese sentences can be effectively analyzed and the ambiguity of case auxiliary words can be solved. Aiming at the complex honorific system in Japanese, a rule-based morphological transformation library and LSTM model are designed to realize the accurate transformation of honorific. In the aspect of neural network optimization, the multi-task learning (MTL) framework is adopted to jointly train translation and grammar-related tasks, and the grammar-aware attention mechanism and dynamic gating fusion mechanism are introduced to further improve the model performance. The experimental results show that this method is superior to the existing models in overall translation quality, grammatical accuracy and honorific treatment ability, which provides a new idea for the development of Japanese machine translation.