自动化迅速发展背景下基于深度学习的翻译系统研究

Research on translation system based on deep learning in the context of rapid development of automation

通过基于深度学习的机器翻译模型,能够实现中英语言的自动化翻译。但当前的机器翻译模型大多存在梯度回流受阻、翻译精度不够理想等缺陷,为此,研究结合卷积神经网络和循环神经网络,构建了CRNN机器翻译模型。实验结果证明,该模型的句子翻译准确率达到99.52%,单词翻译准确率达到99.84%,均显著高于现有的机器翻译模型。上述结果表明,研究提出的机器翻译模型能够有效提高翻译精度,从而提升各国人们间的交流效率,同时也为语言翻译工作提供了新的思路和途径。

Through the machine translation model based on deep learning,automatic translation of Chinese and English languages can be realized.However,most of the current machine translation models have some defects,such as gradient backflow blocked,and translation accuracy is not ideal.Therefore,the CRNN machine translation model is built by combining convolutional neural network and cyclic neural network.The experimental results show that the sentence translation accuracy of the model reaches 99.52%,and the word translation accuracy reaches 99.84%,which are significantly higher than the existing machine translation models.The above results show that the machine translation model proposed in the study can effectively improve the translation accuracy,so as to improve the communication efficiency between people in various countries.At the same time,it also provides new ideas and ways for language translation.