<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Under the background of big data development, more intelligent technologies make work and life convenient, and the traditional English-US literature translation methods and means are difficult to adapt to the fast-paced era of big data, so this paper conducts research and analysis on this problem. In terms of the English-Chinese translation strategy based on BERT and multi-similarity fusion of English and American literature, the article first introduces the concept and principle of BERT, and multi-similarity fusion algorithm then constructs the model of BERT and multi-similarity algorithm fusion, analyzes and discusses the English and American literature translation dataset respectively, and compares it with three different similarity algorithms in terms of three indicators. The data shows that BERT has the greatest effect on the accuracy rate, followed by BLEU value and Manhattan distance. Compared with the fusion of the three methods, the data analysis of a single method is better, and the three selected index values all remain above 90%, and this study is conducive to improving the accuracy and efficiency of English and American literature work, which is of great historical significance for international literary communication. </p>
</abstract>

Under the background of big data development, more intelligent technologies make work and life convenient, and the traditional English-US literature translation methods and means are difficult to adapt to the fast-paced era of big data, so this paper conducts research and analysis on this problem. In terms of the English-Chinese translation strategy based on BERT and multi-similarity fusion of English and American literature, the article first introduces the concept and principle of BERT, and multi-similarity fusion algorithm then constructs the model of BERT and multi-similarity algorithm fusion, analyzes and discusses the English and American literature translation dataset respectively, and compares it with three different similarity algorithms in terms of three indicators. The data shows that BERT has the greatest effect on the accuracy rate, followed by BLEU value and Manhattan distance. Compared with the fusion of the three methods, the data analysis of a single method is better, and the three selected index values all remain above 90%, and this study is conducive to improving the accuracy and efficiency of English and American literature work, which is of great historical significance for international literary communication.

English-Chinese translation strategies based on BERT and multi-similarity fusion of English and American literature

Applied Mathematics and Nonlinear Sciences

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

Under the background of big data development, more intelligent technologies make work and life convenient, and the traditional English-US literature...

English-Chinese translation strategies based on BERT and multi-similarity fusion of English and American literature

Online veröffentlicht: 14. Juli 2023

Seitenbereich: -

Eingereicht: 19. Jan. 2022

Akzeptiert: 26. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.2.00050

Schlüsselwörter
Big data, BERT algorithm, Multi-similarity fusion algorithm, English and American literature, English-Chinese translation

© 2023 Si Dai Ke Jiang·Ta Xi Bai Ke, published by Sciendo

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

English-Chinese translation strategies based on BERT and multi-similarity fusion of English and American literature

Online veröffentlicht: 14. Juli 2023

Seitenbereich: -

Eingereicht: 19. Jan. 2022

Akzeptiert: 26. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.2.00050

SchlüsselwörterBig data, BERT algorithm, Multi-similarity fusion algorithm, English and American literature, English-Chinese translation

© 2023 Si Dai Ke Jiang·Ta Xi Bai Ke, published by Sciendo

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

Schlüsselwörter
Big data, BERT algorithm, Multi-similarity fusion algorithm, English and American literature, English-Chinese translation