Exploring a New Model Of College English Translation Classroom Via Natural Language Processing and Communication Technology

Abstract

The crucial duty of developing translation skills for China's modernization falls on higher education institutions that teach translation. Information and intelligent technology are becoming increasingly ingrained in people's lives as civilization grows and develops. In this work, we use natural language processing and communication technologies to build a new type of university English translation classroom. To address the challenge of inferring semantic implication linkages in natural language processing, we put forth a deep learning model based on semantic rounding and semantic fusing. The technique can be applied to university translation classes to help basic translation tasks with effective reading comprehension. Furthermore, we developed a wireless classroom interaction system that enables effective interoperability between teachers and students in the classroom by embedding a natural language processing model in real time. Our natural language processing model performs exceptionally well and is capable of making predictions in real time, according to experimental results. The entire solution gives universities English translation classes a whole new experience.