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

<p>In the multimedia context, it is important to enrich the teaching forms, challenge the traditional teaching concepts and realize the innovation of education mode. In this paper, a detailed review of translation strategies for college students in the multimedia context is presented, and the traditional GLR translation teaching analysis algorithm is analyzed. To compensate for the shortcomings of low translation teaching efficiency caused by over-fitting in the traditional GLR translation teaching analysis algorithm, a Bayesian model is constructed, and an adversarial neural network is built on its basis. Generate a translation teaching innovation model applicable to the translation teaching of university students. The translation teaching method is evaluated using the BLEU evaluation method. Experimental results: Both the correct translation rate of utterances based on the statistical computing method and dynamic memory algorithm reached 90%-95%. The traditional GLR translation teaching analysis algorithm achieved 95% correctness in recognizing declarative sentences, while the correctness rate for question and exclamation sentences was less than 95%. The correct translation rate of all the statements of the innovative model of translation teaching reached more than 97%. It can be seen that: The innovative model of translation teaching for college students with multimedia backgrounds is simpler and faster in calculation and more practical than other translation teaching algorithms, which is suitable for English translation work of college students and meets the proofreading needs of college students for translation teaching.</p>
</abstract>

In the multimedia context, it is important to enrich the teaching forms, challenge the traditional teaching concepts and realize the innovation of education mode. In this paper, a detailed review of translation strategies for college students in the multimedia context is presented, and the traditional GLR translation teaching analysis algorithm is analyzed. To compensate for the shortcomings of low translation teaching efficiency caused by over-fitting in the traditional GLR translation teaching analysis algorithm, a Bayesian model is constructed, and an adversarial neural network is built on its basis. Generate a translation teaching innovation model applicable to the translation teaching of university students. The translation teaching method is evaluated using the BLEU evaluation method. Experimental results: Both the correct translation rate of utterances based on the statistical computing method and dynamic memory algorithm reached 90%-95%. The traditional GLR translation teaching analysis algorithm achieved 95% correctness in recognizing declarative sentences, while the correctness rate for question and exclamation sentences was less than 95%. The correct translation rate of all the statements of the innovative model of translation teaching reached more than 97%. It can be seen that: The innovative model of translation teaching for college students with multimedia backgrounds is simpler and faster in calculation and more practical than other translation teaching algorithms, which is suitable for English translation work of college students and meets the proofreading needs of college students for translation teaching.

Research on Innovation of Translation Teaching and Translation Strategies for College Students in Multimedia Background

Applied Mathematics and Nonlinear Sciences

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

In the multimedia context, it is important to enrich the teaching forms, challenge the traditional teaching concepts and realize the innovation of education...

Research on Innovation of Translation Teaching and Translation Strategies for College Students in Multimedia Background

Pubblicato online: 28 apr 2023

Pagine: -

Ricevuto: 16 mag 2022

Accettato: 14 set 2022

DOI: https://doi.org/10.2478/amns.2023.1.00087

Parole chiave
Multimedia, Teaching innovation, GLR analysis algorithm, Adversarial neural network, BLEU evaluation method

© 2023 Dan Li, published by Sciendo

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

Research on Innovation of Translation Teaching and Translation Strategies for College Students in Multimedia Background

Pubblicato online: 28 apr 2023

Pagine: -

Ricevuto: 16 mag 2022

Accettato: 14 set 2022

DOI: https://doi.org/10.2478/amns.2023.1.00087

Parole chiaveMultimedia, Teaching innovation, GLR analysis algorithm, Adversarial neural network, BLEU evaluation method

© 2023 Dan Li, published by Sciendo

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

Parole chiave
Multimedia, Teaching innovation, GLR analysis algorithm, Adversarial neural network, BLEU evaluation method