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

<p>Cultural dissemination and display methods with the progress of science and technology requirements continue to improve, especially in the AIGC era. The traditional cultural dissemination methods are no longer applicable, and “non-legacy digitization” needs to become more and more urgent. This paper analyzes the neural machine translation method and constructs a back-translation model by synthesizing a corpus and generating parameters. BLEU and other evaluation indexes are used to optimize the factors that affect the computation of synthetic data. An enhanced synthetic corpus is created using the kNN algorithm to construct a monolingual translation memory. Using the semantic information-sharing strategy, generate the parameters of the embedding layer of the non-legacy translation model while introducing the encoding layer to complete the overall construction of the non-legacy translation model. Comparative experiments and empirical analyses are set up to study the heterogeneity of the quality and dissemination effect of English translation of NRLs, and the experiments show that in the first set of translation tasks, the BLEU scores of the model proposed in this paper are 1 and 1.4 higher than those of the wait-k model when the value of k is 5 and 7, and the translation quality of the model proposed in this paper is better. The values of primary and secondary term coefficients of the English translation of non-legacy are 14.974 and -3.071, respectively, in the case of not establishing samples, showing an inverted U-shaped linear relationship.</p>
</abstract>

Cultural dissemination and display methods with the progress of science and technology requirements continue to improve, especially in the AIGC era. The traditional cultural dissemination methods are no longer applicable, and “non-legacy digitization” needs to become more and more urgent. This paper analyzes the neural machine translation method and constructs a back-translation model by synthesizing a corpus and generating parameters. BLEU and other evaluation indexes are used to optimize the factors that affect the computation of synthetic data. An enhanced synthetic corpus is created using the kNN algorithm to construct a monolingual translation memory. Using the semantic information-sharing strategy, generate the parameters of the embedding layer of the non-legacy translation model while introducing the encoding layer to complete the overall construction of the non-legacy translation model. Comparative experiments and empirical analyses are set up to study the heterogeneity of the quality and dissemination effect of English translation of NRLs, and the experiments show that in the first set of translation tasks, the BLEU scores of the model proposed in this paper are 1 and 1.4 higher than those of the wait-k model when the value of k is 5 and 7, and the translation quality of the model proposed in this paper is better. The values of primary and secondary term coefficients of the English translation of non-legacy are 14.974 and -3.071, respectively, in the case of not establishing samples, showing an inverted U-shaped linear relationship.

Research on English Translation of Intangible Cultural Heritage in the Age of AIGC

Applied Mathematics and Nonlinear Sciences

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

Cultural dissemination and display methods with the progress of science and technology requirements continue to improve, especially in the AIGC era. The...

Research on English Translation of Intangible Cultural Heritage in the Age of AIGC

Publié en ligne: 16 déc. 2023

Pages: -

Reçu: 11 janv. 2023

Accepté: 26 juin 2023

DOI: https://doi.org/10.2478/amns.2023.2.01496

Mots clés
Neural network translation method, Back translation model, kNN algorithm, Semantic information sharing, BLEU, Intangible cultural heritage

© 2023 Wulin Sun et al., published by Sciendo

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

Research on English Translation of Intangible Cultural Heritage in the Age of AIGC

Publié en ligne: 16 déc. 2023

Pages: -

Reçu: 11 janv. 2023

Accepté: 26 juin 2023

DOI: https://doi.org/10.2478/amns.2023.2.01496

Mots clésNeural network translation method, Back translation model, kNN algorithm, Semantic information sharing, BLEU, Intangible cultural heritage

© 2023 Wulin Sun et al., published by Sciendo

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

Mots clés
Neural network translation method, Back translation model, kNN algorithm, Semantic information sharing, BLEU, Intangible cultural heritage