Mobile Assisted Language Translation Intelligent Recommendation System Based on Natural Language Processing

Mobile learning is an inevitable result of technological progress, which is achieved through networks. Compared to traditional learning modes, using mobile phones for learning is not limited by geography or learning forms, and has great freedom in time and space. This is an informal remote teaching based on digitization and mobile computer devices. Autonomous learning breaks through the limitations of traditional learning models and enables learners to better utilize their fragmented time for learning. Based on the above issues, this article discussed how to build a mobile assisted language translation intelligent recommendation system using NPL (Natural Language Processing) technology. Finally, experimental data showed that the system designed in this article had the smallest parameter size but the shortest training time, with values of only 16M and 5.649min/epoch, respectively. This indicated that the language translation system designed in this article had strong advantages, which can reduce the size of model parameters and thus reduce the training time of the model. The experiment verified the superiority of the mobile assisted language translation system based on NPL.