Research on the Architecture Design of a Neural Machine Translation System Integrating Cross Language Memory Networks and Semantic Information
The current neural machine translation system suffers from translation errors and omissions when translating key tradi-tional cultural semantic information in foreign languages.Therefore,this study integrates key semantic information into a converter network model with cross language memory,designs a system architecture based on this,and verifies it.The experimental results showed that the scores of the research model were consistently higher than those of the control model,with an overall increase of about 1.60-2.25 points on the Chinese and English dataset and about 0.38-2.36 points on the British and German traditional culture data-set.The bilingual evaluation score of the constructed system on the Chinese English translation dataset is 37.6 points,which is better than the comparison system,and the lowest time consumption is 0.36 seconds under the same translation volume,with an accuracy rate of 85.4％.At the same time,its total score in traditional cultural poetry translation reached the highest of 0.90,higher than the comparative method.Overall,the neural machine translation system designed in the study has high performance in translation related to foreign traditional culture,and is effective in real-time translation of traditional culture.
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