Research on Automatic Generation Algorithm of College English Translation Teaching Content Based on Corpus

The traditional translation teaching mode relies on teachers' manual selection and arrangement of teaching content, which is time-consuming and difficult to ensure the timeliness, comprehensiveness and pertinence of the content. In this study, corpus is used as translation assistant technology, teaching technology and research tool, combined with translation teaching in the digital age, and the teaching content is generated in an automatic, personalized and intelligent way. The research design includes data preprocessing module, feature extraction module, model training module, content generation module and evaluation optimization module. By using “Parallel corpus of Chinese EFL learners (PACEL)” and “Ten-Through English Compositions of Chinese Learners (TECCL)”, this study automatically extracts translation features from the corpus and generates translation exercises and explanations that meet the teaching requirements. Experimental analysis shows that compared with the rule-based teaching content generation method, the algorithm proposed in this study has advantages in accuracy, fluency and practicability, and has fast processing speed and high efficiency. In addition, the algorithm has shown good stability and reliability in many experiments, and has good generalization ability and robustness. The algorithm proposed in this study shows good performance in the experiment, which provides an effective automatic generation method for college English translation teaching.