Machine Translation Model Optimization Based on Deep Learning

Machine translation (MT) is widely used in cross-language communication as globalization accelerates. However, existing MT models, such as rule-based methods, still have issues, such as inadequate accuracy of translation. To address these problems, this paper proposes an optimization method on the basis of deep learning to optimize MT models' detail control and quality. It combines convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to enhance the model's understanding and detail control. Regarding algorithm implementation, CNN is used to select local characteristics of words in the sentence, while LSTM is used to catch long-term dependencies between sentence structure and semantics. By introducing attention mechanisms, the model can concentrate on relevant details of the source language, improving translation accuracy and fluency. The results of the experiment demonstrate that the optimized model, combining grammatical, morphological features, and dynamic deep information, can apparently improve the whole performance of neural MT. In the translation task of English-Chinese, the BLEU score of the optimized model increased from 28.3 to 31.8 compared with the baseline model, presenting its advantages in processing complex sentences and contexts.