Monolingual Data Augmentation based Chinese to Tibetan Machines Translation

Recently, neural machine translation has made great progress in the machine translation of Chinese-English, English-French and other languages with the help of large-scale training data and powerful modeling capabilities of deep learning models.

Neural machine translation usually relies on large-scale parallel sentence pairs for training. However, the machine translation of Chinese and minority languages such as Chinese-Tibetan, Chinese-Mongolian and other Chinese and minority languages lack large-scale bilingual parallel data, resulting in the lack of performance of Chinese and ethnic language machine translation . Aiming at resolving the problem of lack of training data for Chinese and ethnic language machine translation, this paper utilizes the current situation of large-scale annotation data and mature natural language processing tools in Chinese, and proposes a way to use monolingual data augmentation to expand the scale of Chinese-Tibetan parallel sentence pairs.