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

<p>Semantic understanding enhancement methods and deep learning are popular areas of artificial intelligence research and have significant potential in natural language processing. The English translation is one of the typical application scenarios combining these two technologies. In order to thoroughly analyze the information contained in English texts and improve the accuracy of English text translation, this study proposes an unbalanced Bi-LSTM model. Firstly, the BERT model is used to vectorize the original English corpus and extract the preliminary semantic features. Then, the unbalanced Bi-LSTM network is used to increase the weight of the textual information containing important semantics to further improve the effect of the key features on the recognition of the English text and, at the same time, an attention mechanism that introduces the word vectors is used to widen the gap between the key textual information and the non-key information, so as to improve the effect of the English translation. The accuracy of English text translation can be significantly enhanced by comparing the classification effect with various models, as shown by the results. The accuracy of the model can reach over 90% in about 60 pieces of translation training, and the mean square average is only 1.52. Its translation effect has won the recognition of more than 50% of professionals. The model's ability to translate English is evident.</p>
</abstract>

Semantic understanding enhancement methods and deep learning are popular areas of artificial intelligence research and have significant potential in natural language processing. The English translation is one of the typical application scenarios combining these two technologies. In order to thoroughly analyze the information contained in English texts and improve the accuracy of English text translation, this study proposes an unbalanced Bi-LSTM model. Firstly, the BERT model is used to vectorize the original English corpus and extract the preliminary semantic features. Then, the unbalanced Bi-LSTM network is used to increase the weight of the textual information containing important semantics to further improve the effect of the key features on the recognition of the English text and, at the same time, an attention mechanism that introduces the word vectors is used to widen the gap between the key textual information and the non-key information, so as to improve the effect of the English translation. The accuracy of English text translation can be significantly enhanced by comparing the classification effect with various models, as shown by the results. The accuracy of the model can reach over 90% in about 60 pieces of translation training, and the mean square average is only 1.52. Its translation effect has won the recognition of more than 50% of professionals. The model's ability to translate English is evident.

A Study of the Combination of Semantic Understanding Enhancement Methods and Deep Learning Techniques in English Translation

Applied Mathematics and Nonlinear Sciences

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

Semantic understanding enhancement methods and deep learning are popular areas of artificial intelligence research and have significant potential in natural...

A Study of the Combination of Semantic Understanding Enhancement Methods and Deep Learning Techniques in English Translation

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 14 mar 2024

Accettato: 01 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1672

Parole chiave
Bi-LSTM model, BERT model, Attention mechanism, English translation

© 2024 Gang Zhou., published by Sciendo

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

A Study of the Combination of Semantic Understanding Enhancement Methods and Deep Learning Techniques in English Translation

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 14 mar 2024

Accettato: 01 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1672

Parole chiaveBi-LSTM model, BERT model, Attention mechanism, English translation

© 2024 Gang Zhou., published by Sciendo

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

Parole chiave
Bi-LSTM model, BERT model, Attention mechanism, English translation