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

<p>With the trend of information technology, contemporary English language teaching is moving positively in the direction of intelligence. This study utilizes the text embedding model to represent language and convert it into a format that computers can process. Through the methods of adversarial training and self-learning training and adding the fine-tuning process to improve the effect of cross-linguistic word vectors, the machine reading comprehension and translation model is constructed to ensure the semantic consistency between it and the input source text. Meanwhile, this study combines the model to implement an innovative teaching model of English in colleges and universities and evaluates its effectiveness. The results show that the effectiveness of the proposed method in this paper on this indicator has been narrowed to a gap of 11.48%, which significantly confirms the effectiveness of the proposed model. In the direction of E2C, the cross-linguistic embedding model has the highest average F1 score (0.894). The average score of class 1 results is 82.6843, corresponding to a critical confidence level of 0.036&lt;0.05, which indicates that there is a significant difference between the results of class 1 and class 2. The reference value of this study is important for English teaching and related system development in colleges and universities.</p>
</abstract>

With the trend of information technology, contemporary English language teaching is moving positively in the direction of intelligence. This study utilizes the text embedding model to represent language and convert it into a format that computers can process. Through the methods of adversarial training and self-learning training and adding the fine-tuning process to improve the effect of cross-linguistic word vectors, the machine reading comprehension and translation model is constructed to ensure the semantic consistency between it and the input source text. Meanwhile, this study combines the model to implement an innovative teaching model of English in colleges and universities and evaluates its effectiveness. The results show that the effectiveness of the proposed method in this paper on this indicator has been narrowed to a gap of 11.48%, which significantly confirms the effectiveness of the proposed model. In the direction of E2C, the cross-linguistic embedding model has the highest average F1 score (0.894). The average score of class 1 results is 82.6843, corresponding to a critical confidence level of 0.036<0.05, which indicates that there is a significant difference between the results of class 1 and class 2. The reference value of this study is important for English teaching and related system development in colleges and universities.

Optimization Strategies and Efficacy Evaluation of Cross-Language Embedding Model in Teaching English in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

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

With the trend of information technology, contemporary English language teaching is moving positively in the direction of intelligence. This study utilizes the...

Optimization Strategies and Efficacy Evaluation of Cross-Language Embedding Model in Teaching English in Colleges and Universities

Published Online: May 30, 2024

Page range: -

Received: Feb 14, 2024

Accepted: Apr 22, 2024

DOI: https://doi.org/10.2478/amns-2024-1297

Keywords
English language teaching in higher education, Word embedding, Text embedding, Machine translation, Machine reading

© 2024 Jie Liu, published by Sciendo

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

Optimization Strategies and Efficacy Evaluation of Cross-Language Embedding Model in Teaching English in Colleges and Universities

Published Online: May 30, 2024

Page range: -

Received: Feb 14, 2024

Accepted: Apr 22, 2024

DOI: https://doi.org/10.2478/amns-2024-1297

KeywordsEnglish language teaching in higher education, Word embedding, Text embedding, Machine translation, Machine reading

© 2024 Jie Liu, published by Sciendo

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

Keywords
English language teaching in higher education, Word embedding, Text embedding, Machine translation, Machine reading