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

<p>This research establishes a phrase-based statistical machine translation platform composed of multiple functional modules, including data preprocessing, word alignment, translation model training, ordering model training, language model training, decoding, and parameter optimization. Special emphasis is placed on the quality of bilingual word alignment, utilizing both the IBM model and the statistical collocation model to enhance alignment accuracy. The study reveals that this new platform significantly enhances translation efficiency across diverse corpora, such as legal documents, news articles, and parliamentary records. Notably, the translation of parliamentary records demonstrates a remarkable reduction in average translation time by 27.53% and a decrease in the number of decoding iterations by 56.46% when compared to System1 using the Baseline system. Furthermore, a BLEU-based evaluation indicates that the Baseline system consistently surpasses the comparison system in translating various text types. This study underscores the practical effectiveness of the developed translation platform and its contribution to the advancement of natural language processing technology.</p>
</abstract>

This research establishes a phrase-based statistical machine translation platform composed of multiple functional modules, including data preprocessing, word alignment, translation model training, ordering model training, language model training, decoding, and parameter optimization. Special emphasis is placed on the quality of bilingual word alignment, utilizing both the IBM model and the statistical collocation model to enhance alignment accuracy. The study reveals that this new platform significantly enhances translation efficiency across diverse corpora, such as legal documents, news articles, and parliamentary records. Notably, the translation of parliamentary records demonstrates a remarkable reduction in average translation time by 27.53% and a decrease in the number of decoding iterations by 56.46% when compared to System1 using the Baseline system. Furthermore, a BLEU-based evaluation indicates that the Baseline system consistently surpasses the comparison system in translating various text types. This study underscores the practical effectiveness of the developed translation platform and its contribution to the advancement of natural language processing technology.

On Applying Natural Language Processing Technology to Optimize Accuracy of English Interactive Translation Platform

Applied Mathematics and Nonlinear Sciences

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

This research establishes a phrase-based statistical machine translation platform composed of multiple functional modules, including data preprocessing, word...

On Applying Natural Language Processing Technology to Optimize Accuracy of English Interactive Translation Platform

Published Online: Jul 02, 2024

Page range: -

Received: Mar 13, 2024

Accepted: Jun 04, 2024

DOI: https://doi.org/10.2478/amns-2024-1524

Keywords
Word alignment, Tonal order model, Parameter optimization, Decoding, English translation platform

© 2024 Min Zhang, published by Sciendo

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

On Applying Natural Language Processing Technology to Optimize Accuracy of English Interactive Translation Platform

Published Online: Jul 02, 2024

Page range: -

Received: Mar 13, 2024

Accepted: Jun 04, 2024

DOI: https://doi.org/10.2478/amns-2024-1524

KeywordsWord alignment, Tonal order model, Parameter optimization, Decoding, English translation platform

© 2024 Min Zhang, published by Sciendo

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

Keywords
Word alignment, Tonal order model, Parameter optimization, Decoding, English translation platform