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

<p>In order to solve the problems of business English translation teaching, a corpus-based teaching model is proposed. The API and web crawler are used to collect data from the business English corpus, and the overall corpus structure design is completed according to the acquired data. The corpus is pre-processed with lexical processing, theme extraction, category labeling, and other operations to ensure the feasibility of the corpus. The limitations of traditional business English translation teaching are highlighted, and the structure and implementation process of the corpus-based business English translation teaching model are thoroughly examined. The research subjects are selected, and experimental comparisons are applied empirically to analyze the corpus-based business English translation teaching. The data show that the mean translation score of the experimental group increased by 8.1247. In contrast, the mean translation score of the control group not only didn’t improve but also decreased by 0.8806, and the mean interpreting score of the experimental group increased by 0.608. In contrast, the mean interpreting score of the control group increased by 0.172, which indicates that the corpus-based business English teaching mode has a facilitating effect on the improvement of the student’s abilities in all aspects of English.</p>
</abstract>

In order to solve the problems of business English translation teaching, a corpus-based teaching model is proposed. The API and web crawler are used to collect data from the business English corpus, and the overall corpus structure design is completed according to the acquired data. The corpus is pre-processed with lexical processing, theme extraction, category labeling, and other operations to ensure the feasibility of the corpus. The limitations of traditional business English translation teaching are highlighted, and the structure and implementation process of the corpus-based business English translation teaching model are thoroughly examined. The research subjects are selected, and experimental comparisons are applied empirically to analyze the corpus-based business English translation teaching. The data show that the mean translation score of the experimental group increased by 8.1247. In contrast, the mean translation score of the control group not only didn’t improve but also decreased by 0.8806, and the mean interpreting score of the experimental group increased by 0.608. In contrast, the mean interpreting score of the control group increased by 0.172, which indicates that the corpus-based business English teaching mode has a facilitating effect on the improvement of the student’s abilities in all aspects of English.

Problems and Reform of Business English Translation Teaching in the Background of Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

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

In order to solve the problems of business English translation teaching, a corpus-based teaching model is proposed. The API and web crawler are used to collect...

Problems and Reform of Business English Translation Teaching in the Background of Artificial Intelligence

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 15. Feb. 2024

Akzeptiert: 21. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1208

Schlüsselwörter
Participle processing, Topic extraction, Category labeling, Corpus, Business English translation

© 2024 Derui Fan, published by Sciendo

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

Problems and Reform of Business English Translation Teaching in the Background of Artificial Intelligence

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 15. Feb. 2024

Akzeptiert: 21. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1208

SchlüsselwörterParticiple processing, Topic extraction, Category labeling, Corpus, Business English translation

© 2024 Derui Fan, published by Sciendo

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

Schlüsselwörter
Participle processing, Topic extraction, Category labeling, Corpus, Business English translation