Year: 2025
Type: article
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.
Author: Weiwei Suo
Institution: Xi'an Fanyi University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410827105