Optimization and Application of Neural Network Machine Translation Model Based on Deep Learning

In the context of economic globalization, with the rapid growth of internet technology, international communication in various industries is becoming increasingly frequent, and the demand for cross language communication is becoming increasingly evident. Traditional manual translation, due to issues such as translation efficiency, is gradually unable to meet the current translation requirements of people. Machine translation (MT), as an effectual tool, can achieve equivalent transformation between different languages while preserving the original semantics, and has great practical significance. In recent years, with the rapid growth of machine learning (ML) and deep learning (DL) technologies, DL has been integrated with natural language processing gradually. However, due to the difficulties in optimizing network models and the accuracy bias in forcing labeled data in language processing, this paper proposes a neural network machine translation (NMT) model optimization method based on Generative Adversarial Networks (GANs). This method mainly combines GANs to train MT models. By constructing generator and discriminator modules and playing games with each other, high-quality translation outputs are generated. The experimental results show that after introducing GAN, the translation performance has been improved compared to traditional NMT models.