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

<p>To smoothly realize the information conversion from the original language information to the target language, this paper constructs a deep learning-based fuzzy translation model for news reports so that the translated text can faithfully convey the meaning of the original language text information and achieve natural semantic equivalence. A neural probabilistic language model is used to construct objective functions in speech recognition and lexical annotation so that the translated text can provide a more appropriate linguistic representation of the polysemantic words in the original language text according to the differences in contextual morphology. A deep learning occurrence mechanism model is constructed through fuzzy semantic reasoning and fuzzy translation logic, and learning state indicators such as emotional interaction are designed to evaluate the occurrence status of fuzzy translation accurately. The simulation results show that the natural language processing (GLUE) test score of the deep learning-based fuzzy translation model for news reports is 89.8, 9.2, and 6.9 points higher than 80.6 and 82.9 for the other two models, respectively. The average error discrimination ability of the model designed in this paper is 93.57, and the average training set, development set, and test set values are 98.425, 10.16, and 45.95, respectively. Thus, it can be seen that the deep learning-based fuzzy translation model for news reports can more naturally and accurately respond to the dynamic changes in language, which promotes the rapid development of translation theory and practice.</p>
</abstract>

To smoothly realize the information conversion from the original language information to the target language, this paper constructs a deep learning-based fuzzy translation model for news reports so that the translated text can faithfully convey the meaning of the original language text information and achieve natural semantic equivalence. A neural probabilistic language model is used to construct objective functions in speech recognition and lexical annotation so that the translated text can provide a more appropriate linguistic representation of the polysemantic words in the original language text according to the differences in contextual morphology. A deep learning occurrence mechanism model is constructed through fuzzy semantic reasoning and fuzzy translation logic, and learning state indicators such as emotional interaction are designed to evaluate the occurrence status of fuzzy translation accurately. The simulation results show that the natural language processing (GLUE) test score of the deep learning-based fuzzy translation model for news reports is 89.8, 9.2, and 6.9 points higher than 80.6 and 82.9 for the other two models, respectively. The average error discrimination ability of the model designed in this paper is 93.57, and the average training set, development set, and test set values are 98.425, 10.16, and 45.95, respectively. Thus, it can be seen that the deep learning-based fuzzy translation model for news reports can more naturally and accurately respond to the dynamic changes in language, which promotes the rapid development of translation theory and practice.

Deep Learning-based Fuzzy Translation Problem in Chinese-English Epidemic News Reporting

Applied Mathematics and Nonlinear Sciences

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

To smoothly realize the information conversion from the original language information to the target language, this paper constructs a deep learning-based fuzzy...

Deep Learning-based Fuzzy Translation Problem in Chinese-English Epidemic News Reporting

Online veröffentlicht: 09. Juni 2023

Seitenbereich: -

Eingereicht: 08. Aug. 2022

Akzeptiert: 15. Dez. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00380

Schlüsselwörter
Deep learning model, Objective function, Fuzzy semantic inference, Natural language processing test, Fuzzy translation logic

© 2023 Ya Luo et al., published by Sciendo

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

Deep Learning-based Fuzzy Translation Problem in Chinese-English Epidemic News Reporting

Online veröffentlicht: 09. Juni 2023

Seitenbereich: -

Eingereicht: 08. Aug. 2022

Akzeptiert: 15. Dez. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00380

SchlüsselwörterDeep learning model, Objective function, Fuzzy semantic inference, Natural language processing test, Fuzzy translation logic

© 2023 Ya Luo et al., published by Sciendo

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

Schlüsselwörter
Deep learning model, Objective function, Fuzzy semantic inference, Natural language processing test, Fuzzy translation logic