A Study of the Assistive Nature of Artificial Intelligence Technology for Japanese Translation and Interpretation

Abstract

Abstract Traditional Japanese translation methods have certain disadvantages, and the introduction of artificial intelligence technology into them can enhance the effect of Japanese interpretation and translation. In this paper, the Japanese language data of Twitter and Facebook are used as the basis to construct a Japanese language interpretation and translation corpus, and the GPT-2 model is constructed on the basis of Transformer for Japanese text translation. To optimize the Seq2Seq model for Japanese speech interpretation, the Attention mechanism is introduced to establish a Japanese speech translation model. A Japanese oral and written corpus was used to analyze the validity of the methods mentioned above. The results show that the class/form ratio in the Japanese oral/translated corpus fluctuates between [0.1231, 0.1448], but the survival rate of borrowed words under the scientific category reaches the highest of 54.14%, and the average number of occurrences of each word is between [4.35, 4.95]. Japanese verbal and translated texts had an average sentence length of 40 hours, and their translation accuracy was approximately 74.16%. The quality of translation can be effectively improved, and cultural exchange between China and Japan can be enhanced by integrating AI technology with Japanese interpretation and translation.