Design of intelligent English translation teaching system combined with bidirectional encoder representation

With the continuous progress of artificial intelligence technology, an intelligent education system has become an essential means to improve teaching efficiency and quality. This study aims to design an intelligent English translation teaching system that uses a bidirectional encoder to represent LSTM technology. This advanced natural language processing model can help us understand the context of the language well. The core of the system design is to realize the bidirectional understanding and deep semantic analysis of English text through the LSTM model and provide personalized learning content and real-time translation feedback on this basis. The system first encodes students' input using LSTM, captures semantic and syntactic information in sentences, and then generates an accurate target language translation through decoding. In addition, the system integrates machine learning algorithms that analyze students' learning habits and error patterns to provide customized exercises and tutoring. The experimental results show that the system can help students master English translation skills and also improve students' engagement and interest through an interactive learning interface. The accuracy of translation, processing accuracy of long sentences, and processing accuracy of polysemous words reached 92 %, 89 %, and 90 %, respectively. The scores for system adaptability, user interaction, and personalized teaching satisfaction were 9.8 and 8.5, respectively, higher than the traditional methods' scores of 5, 6, and 5. Implementing this system is expected to significantly improve the effect of English translation teaching.