An Innovative Translation Teaching Model Based on Mobile Technology: A Case Study of Translation Major Classrooms

Abstract

With the rapid development of information technology, the application of mobile technology in education has become increasingly widespread, particularly in language learning and translation teaching. In translation major classrooms, traditional teaching methods are gradually failing to meet the demands of training translation professionals in the new era. Existing studies have predominantly focused on the use of individual translation tools or basic translation support technologies, lacking systematic and in-depth discussions. Particularly in the context of real-time bilingual translation in teaching scenarios, how mobile technology and advanced machine translation algorithms can be integrated to improve translation efficiency and quality in the classroom remains an unresolved issue. A new innovative translation teaching model based on mobile technology was proposed in this study, with two core aspects being examined. First, to address the need for real-time bilingual translation in translation major classrooms, a hybrid tensor train decomposition (HTTD) method was introduced, which optimizes the flow of information and computational processes in translation tasks through efficient model decomposition and multi-dimensional data fusion. Second, based on HTTD, a lightweight machine translation model was developed, aiming to reduce the computational complexity and resource consumption during the translation process, ensuring the real-time performance and responsiveness of the translation system on mobile devices. This study not only provides a new technical support model for translation teaching but also offers innovative insights for the optimization and application of machine translation systems, holding significant theoretical and practical value.