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

<p>The integration of artificial intelligence (AI) across various domains marks a significant trend in contemporary development, with its application in English translation teaching in higher education emerging as a focal area of research. In response to the evolving needs of English translation instruction, this study advocates for a novel AI-supported teaching method leveraging a translation system. We incorporate a multi-hop attention mechanism within a Long Short-Term Memory (LSTM) network to develop an intelligent neural network machine translation model. This model is enhanced with a meta-learning recommendation algorithm, which delivers personalized translation learning materials to students, facilitating an intelligent teaching paradigm. The efficacy of this innovative teaching method was empirically tested by assessing its translation performance prior to classroom implementation. The evaluation involved comparative tests on English-to-Chinese and Chinese-to-English translation. The results reveal that students taught under this AI-enhanced model scored mean values of 27.68 and 37.42 in these respective tests, exhibiting statistically significant improvements over those taught via traditional methods (P&lt;0.05). The findings suggest that the intelligent translation teaching system proposed in this study holds promising potential for redefining English translation teaching frameworks in universities, indicating a substantial stride toward the integration of AI in educational methodologies.</p>
</abstract>

The integration of artificial intelligence (AI) across various domains marks a significant trend in contemporary development, with its application in English translation teaching in higher education emerging as a focal area of research. In response to the evolving needs of English translation instruction, this study advocates for a novel AI-supported teaching method leveraging a translation system. We incorporate a multi-hop attention mechanism within a Long Short-Term Memory (LSTM) network to develop an intelligent neural network machine translation model. This model is enhanced with a meta-learning recommendation algorithm, which delivers personalized translation learning materials to students, facilitating an intelligent teaching paradigm. The efficacy of this innovative teaching method was empirically tested by assessing its translation performance prior to classroom implementation. The evaluation involved comparative tests on English-to-Chinese and Chinese-to-English translation. The results reveal that students taught under this AI-enhanced model scored mean values of 27.68 and 37.42 in these respective tests, exhibiting statistically significant improvements over those taught via traditional methods (P<0.05). The findings suggest that the intelligent translation teaching system proposed in this study holds promising potential for redefining English translation teaching frameworks in universities, indicating a substantial stride toward the integration of AI in educational methodologies.

Artificial Intelligence Technology Boosts the Construction of Intelligent Translation Teaching Mode of English in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

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

The integration of artificial intelligence (AI) across various domains marks a significant trend in contemporary development, with its application in English...

Artificial Intelligence Technology Boosts the Construction of Intelligent Translation Teaching Mode of English in Colleges and Universities

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 17 mar 2024

Accettato: 10 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1673

Parole chiave
LSTM network, Multi-hop attention mechanism, Meta-learning recommendation algorithm, Artificial intelligence, Translation teaching mode

© 2024 Qilu Xu., published by Sciendo

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

Artificial Intelligence Technology Boosts the Construction of Intelligent Translation Teaching Mode of English in Colleges and Universities

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 17 mar 2024

Accettato: 10 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1673

Parole chiaveLSTM network, Multi-hop attention mechanism, Meta-learning recommendation algorithm, Artificial intelligence, Translation teaching mode

© 2024 Qilu Xu., published by Sciendo

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

Parole chiave
LSTM network, Multi-hop attention mechanism, Meta-learning recommendation algorithm, Artificial intelligence, Translation teaching mode