Design and Analysis of Chinese-Korean Translation System Based on Deep Transfer Learning

Abstract

In order to better establish the Chinese-Korean translation system model, the deep transfer learning and the model system are tested and analyzed, and the following analysis results are obtained. By discussing the different adjustment mechanisms of deep transfer learning under MMD metric and Wasserstein metric, we can see that, in the MMD metric model, through analyzing datasets 1 and 2, the highest accuracy rate is 83.1% under multisource weight adjustment mechanism under MMD metric and the lower accuracy rate is 62.7% under no weight adjustment mechanism, and the accuracy rates of datasets 1 and 2 are higher than the average. Under Wasserstein metric, the accuracy of dataset 1 is 82.5% under multisource weight and 68.5% under no source weight, both of which are higher than the average. Three EEGNet models, EEGNet_0, EEGNet_1, and EEGNet_2, were established for comparative testing; according to the test results, it can be seen that EEGNet_1 has high accuracy and can be preferred for system establishment. By comparing the Chinese-Korean translation model with the blockchain model and the traditional translation model, it can be seen that when the translation sentences are 100 sentences, the average response time and peak traffic response time of the Chinese-Korean translation model are lower than those of the traditional translation model and the test conclusion is passed. When the test sentences are 1000 sentences, the average response time and peak traffic corresponding time of the Chinese-Korean translation model are still lower than those of the traditional method. Therefore, it can be seen that the efficiency and winning rate of the Chinese-Korean translation model are higher than those of the traditional translation system and meet the needs. According to the analysis of the performance test results of the translation system, it can be seen that the average response time and success rate of the Chinese and Korean translation system under different data are higher than those of the traditional translation system. When the test data are 500, the average response time of the translation system is 13 ms and the accuracy rate is 100%. When the test data are 3000, the average response time is 99 ms and the success rate is 99.6%. Therefore, the success rate of the translation system is basically above 99.6%, which is higher than that of the traditional translation system. In contrast, the Chinese-Korean translation system can improve the translation efficiency and accuracy and can be preferred.