Design Mechanism of an English Translation Teaching Assistance System Based on Artificial Intelligence Technology

This work proposes an English Translation Teaching Assistance System (ETTAS) based on Artificial Intelligence (AI) technology. It combines Natural Language Processing (NLP), recommendation algorithms, and neural networks to facilitate the teaching processes of English Translation. NLP allows understanding sentence parsing and linguistic structures, which which helps in obtaining accurate feedback on the translation teaching effects. This offers a personalized experience, which is a key feature of recommendation algorithms with respect to the learning process, as it offers optimal suggestions based on individual performance. When a neural network makes a mistake or produces bad output, it identifies and analyzes the error and provides elaborate feedback. The proposed teaching assistance system includes two parts, namely, Online English Translation Teaching Subsystem and Assignment Intelligent Analysis and Review Subsystem. The first one concentrates on interactive teaching tasks, such as those involving exercises and data mining; while the second one performs target post-processing on the mined data for intelligent analysis and feedback. This work discusses the architecture of the system, its main modules, and interfaces, and illustrates how these elements work together to enhance the quality of English translation education.