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<p>In recent years, the accuracy and linguistic fluency of translation based on intelligent algorithms have been relatively close to that of human beings, and the application of intelligent algorithms has gradually become a research hotspot in the academic world. In this paper, it is found through relevant research that semantic retention, cultural appropriateness, linguistic fluency, content comprehensiveness and text readability are important factors in the application of intelligent algorithms for the translation of literary works for cross-cultural communication. To explore the degree of influence of these factors on the translation quality of literary works, this paper analyzes them using a multiple linear regression model. The first step in building the mathematical model for multiple linear regression is to construct it. Then, the formulas for calculating the goodness-of-fit and significance test of multiple linear regression equations are introduced. Finally, the regression model was tested using residual analysis and multicollinearity. The regression results show that the quality of literary translation = 0.165+0.124*Semantic retention + 0.356*Cultural appropriateness + 0.254*Linguistic fluency - 0.001*Content comprehensiveness + 0.256*Text accessibility. According to the results of the analysis, the impact of content comprehensiveness on the translation quality of literary works is not significant, and cultural appropriateness has the greatest impact on the translation quality of literary works.</p>
</abstract>

In recent years, the accuracy and linguistic fluency of translation based on intelligent algorithms have been relatively close to that of human beings, and the application of intelligent algorithms has gradually become a research hotspot in the academic world. In this paper, it is found through relevant research that semantic retention, cultural appropriateness, linguistic fluency, content comprehensiveness and text readability are important factors in the application of intelligent algorithms for the translation of literary works for cross-cultural communication. To explore the degree of influence of these factors on the translation quality of literary works, this paper analyzes them using a multiple linear regression model. The first step in building the mathematical model for multiple linear regression is to construct it. Then, the formulas for calculating the goodness-of-fit and significance test of multiple linear regression equations are introduced. Finally, the regression model was tested using residual analysis and multicollinearity. The regression results show that the quality of literary translation = 0.165+0.124*Semantic retention + 0.356*Cultural appropriateness + 0.254*Linguistic fluency - 0.001*Content comprehensiveness + 0.256*Text accessibility. According to the results of the analysis, the impact of content comprehensiveness on the translation quality of literary works is not significant, and cultural appropriateness has the greatest impact on the translation quality of literary works.

A Model for Assessing the Impact of Intelligent Algorithms on the Translation Quality of Literary Works in Cross-Cultural Communication

Applied Mathematics and Nonlinear Sciences

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

In recent years, the accuracy and linguistic fluency of translation based on intelligent algorithms have been relatively close to that of human beings, and the...

A Model for Assessing the Impact of Intelligent Algorithms on the Translation Quality of Literary Works in Cross-Cultural Communication

Publié en ligne: 05 août 2024

Pages: -

Reçu: 28 mars 2024

Accepté: 24 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2091

Mots clés
Multiple linear regression, Goodness of fit, Significance test, Regression model, Translation of literary works

© 2024 Xue Li, published by Sciendo

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

A Model for Assessing the Impact of Intelligent Algorithms on the Translation Quality of Literary Works in Cross-Cultural Communication

Publié en ligne: 05 août 2024

Pages: -

Reçu: 28 mars 2024

Accepté: 24 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2091

Mots clésMultiple linear regression, Goodness of fit, Significance test, Regression model, Translation of literary works

© 2024 Xue Li, published by Sciendo

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

Mots clés
Multiple linear regression, Goodness of fit, Significance test, Regression model, Translation of literary works