Language System Path of Intelligent Machine Translation

In some scenarios of today's society, traditional manual translation or inefficient translation methods can no longer meet people's translation needs, so people have begun to research intelligent machine translation language systems. In the long process of research, the accuracy of machine translation systems has also made a qualitative leap and is increasingly approaching the level of human translation. However, there are still some issues with machine translation systems, such as relying on large-scale parallel corpora for training or lacking robustness when encountering spelling and grammar errors, resulting in inaccurate translation results. This article was dedicated to studying the language system path of intelligent machine translation in a neural network environment and successfully implementing relevant solutions. Through experimental analysis, it can be concluded that optimizing the neural network has successfully improved translation quality under low resource conditions. This fully demonstrates the excellent performance of neural network translation in resource limited situations, demonstrating its broad application potential.