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Then, the model was improved by a generative adversarial network (GAN) to improve the translation effect of the model. Finally, experiments were carried out on the linguistic data consortium (LDC) dataset. It was found that the average Bilingual Evaluation Understudy (BLEU) value of the improved Transformer model improved by 0.49, and the average perplexity value reduced by 10.06 compared with the Transformer model, but the computation speed was not greatly affected. The translation results of the two example sentences showed that the translation of the improved Transformer model was closer to the results of human translation. The experimental results verify that the improved Transformer model can improve the translation quality and be further promoted and applied in practice to further improve the English translation and meet application needs in real life.<\/jats:p>","DOI":"10.1515\/jisys-2022-0038","type":"journal-article","created":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T01:46:50Z","timestamp":1651283210000},"page":"532-540","source":"Crossref","is-referenced-by-count":4,"title":["Research on an English translation method based on an improved transformer model"],"prefix":"10.1515","volume":"31","author":[{"given":"Hongxia","family":"Li","sequence":"first","affiliation":[{"name":"Xi\u2019an Innovation College, Yan\u2019an University , Yan\u2019an , Shaanxi 716000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Tuo","sequence":"additional","affiliation":[{"name":"Xi\u2019an Innovation College, Yan\u2019an University , Yan\u2019an , Shaanxi 716000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2022,4,29]]},"reference":[{"key":"2022120618433987019_j_jisys-2022-0038_ref_001","doi-asserted-by":"crossref","unstructured":"Liu H, Zhang M, Fern\u00e1ndez AP, Xie N, Li B, Liu Q. 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