<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Traditional Japanese translation methods have certain disadvantages, and the introduction of artificial intelligence technology into them can enhance the...

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

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 15 déc. 2023

Accepté: 22 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0105

Mots clés
Transformer, GPT-2 model, Attention mechanism, Seq2Seq model, Japanese translation

© 2024 Xiaoting He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 15 déc. 2023

Accepté: 22 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0105

Mots clésTransformer, GPT-2 model, Attention mechanism, Seq2Seq model, Japanese translation

© 2024 Xiaoting He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Transformer, GPT-2 model, Attention mechanism, Seq2Seq model, Japanese translation