Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation
AbstractThis 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
Share & Cite This Article
Dong, R.; Yang, Y.; Jiang, T. Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation. Information 2019, 10, 202.
Dong R, Yang Y, Jiang T. Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation. Information. 2019; 10(6):202.Chicago/Turabian Style
Dong, Rui; Yang, Yating; Jiang, Tonghai. 2019. "Spelling Correction of Non-Word Errors in Uyghur–Chinese Machine Translation." Information 10, no. 6: 202.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.