Research on the Application of Deep Neural Networks in Machine Translation Method

Against the background of globalization, language barriers have become an important challenge for countries to achieve trade, cultural exchanges and cooperation, and machine translation technology is gradually becoming a key tool to solve this problem. However, in the face of increasingly diversified and personalized practical needs, traditional machine translation methods have limitations in dealing with complex language expressions and diverse language structures, and it is difficult to give consideration to the accuracy and fluency of translation. In this regard, based on the current situation, this paper will deeply explore the application feasibility of artificial intelligence and neural network technology in the field of machine translation, and put forward a new set of machine translation methods combined with deep neural network, which provides new ideas and directions for the development of machine translation technology. Practice has proved that this method can capture the long-term dependence in text sequence data by introducing LSTM network, and at the same time, combined with attention mechanism, it can dynamically pay attention to different parts of the source language sentences in the translation process, thus improving the accuracy and contextual coherence of translation and achieving a smoother and more natural translation effect.