An Assistant System for Translation Flipped Classroom

Abstract

To achieve the goal of training translators that meet the current social needs, the innovation of translation teaching methods is necessary. Studies have proven that students in flipped classrooms (FCs) have greater performance than students in traditional classrooms. However, the preparation time for FCs could be three times higher than that of traditional classrooms, which leads to the reluctance of teachers to conduct FCs. Machine translation (MT) is believed to be a useful tool to improve the translation efficiency of human translators. However, in practice, teachers found that many students cannot work with MT effectively. To solve the above problems, this paper designs a Translation Flipped Classroom Assistance System (TFCAS) based on cloud computing and MT. A parameter is proposed to measure students’ ability to translate evaluation. TFCAS has reduced the burden of teachers in the FC mode and helped students become accustomed to working with MT. Application data stored in the MySQL database, such as sentence pairs, will be used to optimize the neural machine translation model we developed for the system. The system makes MT and the training of translators support each other’s sustainable development and conforms to the trend of deepening teaching reform.