Design and Implementation of Automatic Machine Translation Model Based on Deep Learning Algorithm

As existing machine translation (MT) mainly uses rules or statistical models, it is not effective in dealing with complex languages and diverse contexts, and it is difficult to cope with low-resource language translation and long text context understanding. To solve these problems, this article applies deep learning algorithms into automatic MT models to improve translation quality and efficiency. In the data collection stage, in addition to using the BCC (BLCU Corpus Center) Chinese corpus, the richness of parallel corpora is also increased through crawler tools. In the preprocessing stage, Jieba is used for Chinese word segmentation, and statistics and dictionaries are combined to reduce ambiguity. The Transformer architecture is integrated with the pre-trained BERT (Bidirectional Encoder Representations from Transformers) model to more precisely capture deep semantics, and fine-tune the model to enhance its understanding of specific translation tasks and optimize the interaction between the decoder and the encoder. During training, the Adam algorithm optimization, weight decay, and Dropout strategy are used to prevent overfitting, and mixed precision training is used to improve efficiency. Then, the Horovod framework is used to achieve multi-machine parallel computing. The research results show that the average scores of the MT model using deep learning algorithms on BLEU (Bilingual Evaluation Understudy) and METEOR (Metric for Evaluation of Translation with Explicit ORdering) reach 39.73% and 75.15%, respectively, which is significantly better than the traditional DNN algorithm. At the same time, the model translation takes only 0.36 seconds, which significantly improves efficiency. The model combines pre-training with self-attention mechanism and performs well in the translation of complex sentences and low-resource languages, verifying its accuracy and stability.