Applications of Deep Learning in Japanese Machine Translation

In the context of the acceleration of global economic integration, the communication between China and Japan is increasing, and the demand for efficient and accurate Japanese machine translation is also growing. However, the traditional machine translation model is often difficult to meet the needs of practical applications in terms of translation quality. In recent years, the development of deep learning technology has brought new opportunities and promising solutions to the field of machine translation. This study explores the application effect of deep learning technology, especially Bi-LSTM, in Japanese machine translation, and makes a comparative analysis with LSTM. By constructing a Chinese-Japanese parallel corpus and combining with multi-dimensional evaluation indicators such as BLEU score, METEOR score and manual evaluation, this paper systematically evaluates the performance of Bi-LSTM model in terms of grammatical accuracy, semantic integrity, cultural adaptability and fluency. The results show that the Bi-LSTM model outperforms the LSTM model significantly in all indicators, especially in dealing with long-distance dependencies and cross-language cultural differences, which provides a new method to improve the quality of Japanese machine translation.