Research on Machine Translation (MT) System Based on Deep Reinforcement Learning

With the deepening of globalization and the rapid development of human society and economic society in the world, the demand for machine translation in human society is also increasing rapidly, and the progress of artificial intelligence technology has put forward new requirements for the quality of machine translation. At the same time, the development of machine translation research has set a benchmark for other fields of natural language processing. Therefore, the research on machine translation not only has high practical value, but also can promote the progress of the theoretical research on natural language processing. Based on the idea of deep learning, this paper selects English language features as feature parameters, uses fine-tuning Python model training technology and feature-based deep learning method, and conducts deep reinforcement learning on Anglo-German MACHINE translation model. The comparative experiment of Indian English-German neural machine translation model shows that, The machine translation system based on deep learning is obviously better than traditional machine translation, and the translation effect is obviously improved.