{"id":9793,"date":"2026-08-09T14:37:24","date_gmt":"2026-08-09T06:37:24","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9793"},"modified":"2026-08-09T16:00:02","modified_gmt":"2026-08-09T08:00:02","slug":"jase-202611-34-026","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-026","title":{"rendered":"Exploring Artificial Intelligence-Based Automatic English Translation Methods and Their Performance Evaluation"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Weiwei Suo<\/i><\/a><\/p>\n\n\n\n

Foreign Linguistics and Applied Linguistics, Xi\u2019an FanYi University, Xi\u2019an 710105, China<\/p>\n<\/div>\n\n\n\n

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Received: March 25, 2026
Accepted: July 02, 2026
Publication Date: August 09, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Transformer Based Translation Architecture<\/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:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.026<\/a>\u00a0\u00a0<\/p>\n\n\n\n

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

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Artificial intelligence (AI) has significantly advanced machine translation through deep learning-based approaches. This study presents a comparative evaluation of Neural Machine Translation (NMT) and Transformer BASE models for Chinese-English automatic translation using a parallel corpus containing 200,000 bilingual sentence pairs. The dataset was pre-processed through sentence cleaning, tokenization, and Byte Pair Encoding (BPE)-based subword segmentation to improve translation quality. Both models were implemented and evaluated using Bilingual Evaluation Understudy (BLEU), METEOR, and Translation Edit Rate (TER) metrics. Experimental results demonstrated that the Transformer model outperformed the conventional Neural Machine Translation (NMT) model, achieving improvements of +6.30 BLEU and +5.60 METEOR while reducing TER by
3.77. Ablation analysis further confirmed the importance of multi-head self-attention and positional encoding in improving translation performance. The findings indicate that Transformer-based architectures provide higher translation accuracy, semantic fluency, and computational efficiency, making them suitable for modern multilingual translation applications.<\/p>\n\n\n\n

Keywords: Artificial Intelligence, Neural Machine Translation, Transformer Architecture, Bilingual Evaluation Understudy, Translation Edit Rate<\/em><\/p>\n\n\n\n

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