Mongolian-Chinese Machine Translation Correction Dataset

The accuracy of machine translation datasets plays a decisive role in the performance of translation models. Due to the particularity of character encoding in traditional Mongolian, spelling errors are very common, and the accuracy of character encoding of open resources on the Internet is less than 20%, which poses a major obstacle to intelligent text processing. This paper uses the Mongolian-Chinese bilingual public evaluation dataset of the 17th China Conference on Machine Translation (CCMT 2021) as the original corpus to complete automatic Mongolian correction, and construct a high-quality Mongolian-Chinese sentence pair correction data set for machine translation. The experimental results on the CWMT2017 test set show that the Mongolian-Chinese bilingual parallel sentence pair after the Mongolian text correction is better than the translation effect of the original evaluation data in both Mongolian->Chinese and Chinese->Mongolian directions, which verifies the translation effect of the Mongolian corrected text. The effectiveness and practicability of using to improve the performance of downstream natural language processing tasks.