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Open AccessArticle

Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation

Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Science, Urumqi 830011, China
Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Author to whom correspondence should be addressed.
Information 2019, 10(6), 202;
Received: 22 April 2019 / Revised: 28 May 2019 / Accepted: 4 June 2019 / Published: 6 June 2019
(This article belongs to the Special Issue Natural Language Processing and Text Mining)
This research was conducted to solve the out-of-vocabulary problem caused by Uyghur spelling errors in Uyghur–Chinese machine translation, so as to improve the quality of Uyghur–Chinese machine translation. This paper assesses three spelling correction methods based on machine translation: 1. Using a Bilingual Evaluation Understudy (BLEU) score; 2. Using a Chinese language model; 3. Using a bilingual language model. The best results were achieved in both the spelling correction task and the machine translation task by using the BLEU score for spelling correction. A maximum F1 score of 0.72 was reached for spelling correction, and the translation result increased the BLEU score by 1.97 points, relative to the baseline system. However, the method of using a BLEU score for spelling correction requires the support of a bilingual parallel corpus, which is a supervised method that can be used in corpus pre-processing. Unsupervised spelling correction can be performed by using either a Chinese language model or a bilingual language model. These two methods can be easily extended to other languages, such as Arabic. View Full-Text
Keywords: spelling correction; natural language processing; machine translation; language model; Uyghur spelling correction; natural language processing; machine translation; language model; Uyghur
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Dong, R.; Yang, Y.; Jiang, T. Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation. Information 2019, 10, 202.

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