Journal of Applied Science and Engineering

Published by Tamkang University Press

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Improving AI English translation systems by using multilingual models

 2026

 Volume 35

Ke Li

School of Foreign Languages, Wuhan Business University, Hubei, Wuhan, 430056, China

Received: March 23, 2026
Accepted: July. 06, 2026
Publication Date: September 16, 2026

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AI-based English translation workflow with multi-modal inputs and processing stages

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Artificial intelligence (AI)-based English translation systems using large language models enhance translation accuracy but rely heavily on large datasets, making them prone to bias and challenges in handling subtle linguistic contexts[cite: 7]. This study incorporates advanced AI techniques to improve the precision, accuracy, and overall performance of English translation systems in addressing linguistic issues[cite: 7]. A Scalable Sailfish-tuned Adaptive Gated Recurrent Unit (SS-AGRU) model is proposed to maximize translation accuracy while improving adaptability and efficiency[cite: 7]. The dataset consists of multilingual parallel data containing diverse sentence structures, idiomatic expressions, and domain-specific content to enable robust model training[cite: 7]. Preprocessing includes handling missing data, stopword removal, and tokenization to normalize text[cite: 7]. Word2Vec is employed to capture semantic relationships, while TF-IDF highlights term importance, enhancing input quality and model performance[cite: 7]. The proposed approach integrates AGRU with an optimized Scalable Sailfish (SS) algorithm, improving sequence processing, contextual understanding, parameter optimization, and computational efficiency[cite: 7]. Experimental results demonstrate strong performance, achieving a BLEU score of 50.12, translation time of 38.7 minutes, low TER of 0.337, and WER of 8.89[cite: 7]. The SS-AGRU model outperforms baseline methods in translation accuracy, contextual comprehension, and computational efficiency[cite: 7]. Overall, combining SS-AGRU with the optimized SS algorithm significantly enhances accuracy, flexibility, and computational performance in AI-based language translation systems[cite: 7].

Keywords: English translation, AI, Large language models, Scalable Sailfish Algorithm-tuned Adaptable Gated Recurrent Unit (SS-AGRU)

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