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The results indicated that the three error correction algorithms exhibited little difference in detection performance when faced with distinct corpus databases. Furthermore, when dealing with the same corpus database, the bidirectional LSTM algorithm demonstrated the most robust detection performance, followed by the one based on the traditional unidirectional LSTM translation model, and lastly, the one based on the LSTM classification model performed the least effectively.<\/jats:p>","DOI":"10.3233\/idt-240111","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T11:51:16Z","timestamp":1710849076000},"page":"1403-1409","source":"Crossref","is-referenced-by-count":1,"title":["Research on error detection in English translation texts using machine learning algorithms"],"prefix":"10.1177","volume":"18","author":[{"given":"Xiaoyan","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-240111_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2022.108180","article-title":"Automatic Arabic Grammatical Error Correction based on Expectation-Maximization routing and target-bidirectional agreement","volume":"241","author":"Solyman","year":"2022","journal-title":"Knowl-Based Syst"},{"key":"10.3233\/IDT-240111_ref2","doi-asserted-by":"crossref","first-page":"106264","DOI":"10.1109\/ACCESS.2020.2998149","article-title":"Comparison of the evaluation metrics for Neural Grammatical Error Correction with Overcorrection","volume":"8","author":"Park","year":"2020","journal-title":"IEEE Access"},{"issue":"3","key":"10.3233\/IDT-240111_ref3","first-page":"184","article-title":"English speech recognition and multidimensional pronunciation evaluation","volume":"10","author":"Li","year":"2020","journal-title":"Front Educ Res"},{"issue":"4","key":"10.3233\/IDT-240111_ref4","doi-asserted-by":"crossref","first-page":"327","DOI":"10.5302\/J.ICROS.2018.18.8003","article-title":"Development of a Real-time Lip Recognition for Improving English Pronunciation using Deep Learning","volume":"24","author":"Lim","year":"2018","journal-title":"J Instit Control Robot Syst"},{"issue":"3","key":"10.3233\/IDT-240111_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3138\/cmlr.3605","article-title":"Grammar correction in the writing centre: Expectations and experiences of monolingual and multilingual writers","volume":"72","author":"Eckstein","year":"2016","journal-title":"Can Mod Lang Rev"},{"issue":"1","key":"10.3233\/IDT-240111_ref6","first-page":"175","article-title":"OCR error correction using statistical machine translation","volume":"7","author":"Afli","year":"2016","journal-title":"Int J Comput Ling Appl"},{"issue":"3","key":"10.3233\/IDT-240111_ref7","doi-asserted-by":"crossref","first-page":"85","DOI":"10.14434\/josotl.v15i3.13284","article-title":"Consciousness-raising, Error Correction and Proofreading","volume":"15","author":"O\u2019Brien","year":"2015","journal-title":"J Scholarship Teach Learn"},{"issue":"2","key":"10.3233\/IDT-240111_ref8","first-page":"278","article-title":"Pronunciation Recognition of \u2013ed Ending Words by the Students of English Education Study Program of the University of Bengkulu","volume":"4","author":"Giantari","year":"2020","journal-title":"J English Educ Teach"},{"issue":"12","key":"10.3233\/IDT-240111_ref9","first-page":"1","article-title":"English oral evaluation algorithm based on fuzzy measure and speech recognition","volume":"37","author":"Zhao","year":"2019","journal-title":"J Intell Fuzzy Syst"},{"key":"10.3233\/IDT-240111_ref10","doi-asserted-by":"crossref","unstructured":"Kleynhans N, Hartman W, Van Niekerk DR, van Heerden CJ, Schwartz R, Tsakalidis S, Davel M. 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