Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Abstract

With the advent of the information age, people can establish good communication through Internet technology. Mechanical translation has become a key means to solve people's communication problems. However, there are still obstacles to communication between different languages. In order to solve this problem, this paper uses existing neural network technology to the English-Chinese bidirectional machine translation model in the field of marine science and technology. Based on deep learning technology, we collect Chinese and English abstracts and partial full texts of Chinese and English papers with marine science and technology as the key words and build a professional corpus in English and Chinese about marine science and technology. In the Chinese-English bidirectional translation model, the local weight sharing is introduced into the Chinese encoder and the English encoder, and the output of the Chinese encoder sublayer and the English encoder sublayer is fused as the output of the respective encoders, and the performance of the translation model is evaluated using the BLEU parameters. Through the training of the translation model, compared with the transformer model, the BLEU value of the model with local weight sharing and encoder sublayer fusion output is improved by 1.6 and 3.8 in the Chinese-English and English-Chinese translation directions, respectively. The PPL values in the Chinese-English and English-Chinese translation directions decreased by 18.72% and 14.62%, respectively. We demonstrate the effectiveness of the language translation model. Experiments show that the research on machine language adaptive technology based on deep learning can more smoothly realize the two-way translation of literature in the field of marine science and technology. Compared with traditional mechanical translation, this paper proposes a translation model based on the deep neural algorithm, which improves the effect of model training by constructing a Chinese-English corpus with the theme of marine science and technology.