MTPE Model Translation Course Recommendations Based on Mobile Cloud Computing Technology

Abstract

As long as translators adapt to new technologies and are willing to learn new skills and adapt to the evolving needs of the market, the translation industry will continue to thrive. The purpose of this paper is to study MTPE model translation course recommendation based on mobile cloud computing technology. The characteristics of mobile cloud computing and distributed cloud computing translation course recommendation services and algorithms are studied. On the basis of machine translation, a classification system of error types (science and technology, humanities, medical articles) is established to guide students to identify machine translation errors, evaluate and make statistics and analysis on students' cognitive ability of translation quality and post-translation editing ability, and propose corresponding teaching strategies. After using the MTPE model based on mobile cloud technology for experimental teaching, the overall recognition rate of students is significantly improved, and the average number of vocabulary recognition errors is 88 and 23 times more than before experimental teaching. The average number of grammatical meaning recognition errors is 50 or 9 times more than that before experimental teaching. The recognition rate of contextual meaning is the highest, with an average of 86 errors. Other errors average 82; There are an average of 69 style correction questions. This shows that this technology can improve the students' error recognition rate and improve the learning effect.