{"id":12249,"date":"2026-09-16T12:20:07","date_gmt":"2026-09-16T04:20:07","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=12249"},"modified":"2026-09-16T15:21:52","modified_gmt":"2026-09-16T07:21:52","slug":"jase-202612-35-044","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-044","title":{"rendered":"Improving AI English translation systems by using multilingual models"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Ke Li<\/i><\/a><\/p>\n\n\n\n

School of Foreign Languages, Wuhan Business University, Hubei, Wuhan, 430056, China<\/p>\n<\/div>\n\n\n\n

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Received: March 23, 2026
Accepted: July. 06, 2026
Publication Date: September 16, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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AI-based English translation workflow with multi-modal inputs and processing stages<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.044<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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].<\/p>\n\n\n\n

Keywords: English translation, AI, Large language models, Scalable Sailfish Algorithm-tuned Adaptable Gated Recurrent Unit (SS-AGRU)<\/em><\/p>\n\n\n\n

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