Design and Development of English Translation Teaching Assistance System Based on Deep Neural Network Model

With the growing demand for bilingual proficiency and cross-cultural communication, there is a critical need for intelligent systems that can support effective translation teaching. This study presents the design and development of an English Translation Teaching Assistance System based on a Deep Neural Network (DNN) model, aimed at enhancing translation accuracy, fluency, and pedagogical support in higher education settings. The system incorporates a sequence-to-sequence deep learning architecture with attention mechanisms, enabling dynamic learning of contextual semantics and syntactic structures in English-Chinese translation tasks. Key features include automated translation evaluation, contextual feedback, error annotation, and interactive learning modules tailored for student engagement. A bilingual corpus comprising over 1.2 million parallel sentence pairs was used to train the model, drawn from literary, technical, and academic domains. The system achieved an accuracy of 97.8 and outperformed traditional rule-based and statistical machine translation tools in classroom experiments. In user testing with 200 students, the platform demonstrated a translation learning improvement of 35 %, with high student satisfaction for realtime feedback and linguistic explanation components. This intelligent teaching assistant system serves as a practical tool for augmenting translation instruction, offering personalized support, automated evaluation, and adaptive learning through deep neural networks, ultimately fostering more effective language education.