Article: Adaptive neural machine translation with attention mechanisms for English texts Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.15 pp.25 - 40 Abstract: Neural machine translation (NMT) is the study endeavoured to build systems that would assimilate human language deciphering and production by utilising far-reaching linguistic and contextual forms. This article provides the details about an adaptive neural machine translation (ANMT) model which has incorporated the attentional mechanisms to deal with English texts. A proposed model is compared to existing best practice translation frameworks which are then included with two different approaches such as idiomatic expressions, domain-specific terminologies, and low-resource scenarios. We propose a new adaptation where user feedback loops are used as a method for refining translations based on emerging linguistic patterns. The experimental results confirm that ANMT was a success and the translation mistakes had lessened when new models were adopted; additionally, indicating that NMT experts had received a much better score compared to the baseline language model. This means that ANMT is a significant step in the evolution of AI technologies in translation. Inderscience Publishers - linking academia, business and industry through research

Title: Adaptive neural machine translation with attention mechanisms for English texts

Authors: Weiwei Suo

Addresses: Foreign Linguistics and Applied Linguistics, Xi'an Fanyi University, Xi'an, 710105, China

Abstract: Neural machine translation (NMT) is the study endeavoured to build systems that would assimilate human language deciphering and production by utilising far-reaching linguistic and contextual forms. This article provides the details about an adaptive neural machine translation (ANMT) model which has incorporated the attentional mechanisms to deal with English texts. A proposed model is compared to existing best practice translation frameworks which are then included with two different approaches such as idiomatic expressions, domain-specific terminologies, and low-resource scenarios. We propose a new adaptation where user feedback loops are used as a method for refining translations based on emerging linguistic patterns. The experimental results confirm that ANMT was a success and the translation mistakes had lessened when new models were adopted; additionally, indicating that NMT experts had received a much better score compared to the baseline language model. This means that ANMT is a significant step in the evolution of AI technologies in translation.

Keywords: neural machine translation; NMT; attention mechanisms; adaptive learning; context-aware translation; deep learning.

DOI: 10.1504/IJICT.2025.146372

International Journal of Information and Communication Technology, 2025 Vol.26 No.15, pp.25 - 40

Received: 17 Mar 2025
Accepted: 07 Apr 2025
Published online: 27 May 2025 *

Title: Adaptive neural machine translation with attention mechanisms for English texts

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