Design of English Machine Translation Models Based on Deep Neural Network Algorithms

To enhance the accuracy and fluency of English machine translation, this study designed a translation model based on deep neural network algorithms. Utilizing an encoder-decoder architecture with an integrated attention mechanism, the model's ability to capture linguistic context is significantly improved. Various aspects such as data preprocessing, model architecture design, training processes, and model optimization were analyzed through comparative studies. The results demonstrate that our model outperforms traditional models on standard datasets, conclusively proving the effectiveness and value of deep learning technology in the field of machine translation.