Research on interactive system of Chinese-English translation model in cloud computing environment based on deep learning

Abstract

Abstract At present, human translation is characterized by low efficiency, high cost, high consumption of resources and time, so it is urgent to study automatic translation technology to meet the needs of information interaction and language communication in various countries under the background of economic globalization. This topic has also become a hot issue in the field of translation. After a long period of development, machine translation technology has gradually become popular and widely used, replacing human translation technology as the mainstream technology. Therefore, this paper implements the construction of an interactive system of English Chinese bilingual translation model based on cloud computing environment by introducing deep learning algorithm. The system can learn by itself on the basis of analyzing source documents, and predict target translation output by extracting features. The system is equipped with a translation engine based on the data source, which can realize interactive translation, and the final output can be achieved by processing the predicted translation output and based on the translator's understanding. Through the design of simulation experiments for system detection, the results show that the Chinese English translation model system has a high translation quality. By comparing it with the baseline system, it can be seen that both of them can translate sentences normally in 8 PR cycles (PR * 8, about 18% KSMR), and the data set improvement rate is about + 35 BLEU, that is, 76 BLEU is finally achieved. This proves that users can maintain the translation accuracy while reducing the number of effective interactions by using the optimized translation model to translate the target text, showing the effectiveness. In this paper, an interactive Chinese English translation system is designed by combining deep learning with cloud computing.