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  • https://doi.org/10.1109/iccsie65832.2025.00021Copy DOI Icon

Chinese Japanese Flipped Classroom Scene Translation Technology Based on Iterative Back Translation

  • Jul 23, 2025
  • Lin Shi
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Abstract

The rapid development of artificial intelligence and intelligent algorithms is profoundly transforming the field of education, promoting the widespread deployment of language translation technology in teaching applications. In recent years, bilingual flipped classrooms have raised higher requirements for semantic modeling and ambiguity handling in translation systems. To improve the accuracy of Chinese Japanese translation in complex contexts, a neural network translation framework based on multi input fusion mechanism and iterative back translation strategy was developed. This method introduces contextual cues and ambiguous phrase information as auxiliary inputs based on Transformer, and combines bidirectional pseudo corpus generation and quality filtering mechanisms to achieve semantic enhancement of the Sino Japanese bidirectional translation system through continuous alternating training. The experimental results show that the proposed model has an accuracy of 0.94 in Test 1, a root mean square error of only 0.21, and a BLEU score of 36.1, which is 6.4 points higher than the original Transformer; In Test 2, the accuracy was 0.92 and the BLEU score was 35.4, which is better than the Transformer model. At different training scales, the proposed model has the lowest training time, with a validation stage inference time of 0.29 seconds, far superior to other models, and shows an accuracy of 0.89 in high ambiguity sentence recognition. The research results indicate that the model has significant advantages in flipped classroom language translation, and performs well in semantic understanding, generation efficiency, and robustness.

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