Mongolian-Chinese Machine Translation Based on Text Context Information

Mongolian-Chinese neural machine translation has the problem that it cannot make full use of context information for document-level translation. In order to solve this problem, a Mongolian-Chinese neural machine translation model using the context information of the passage is proposed.The model makes better use of context information for document-level translation by introducing local encoders and global encoders in the encoder and caching mechanisms in the decoder. Through experiments, the document-level Mongolian-Chinese machine translation model integrated with context information is compared with the sentence-level Mongolian-Chinese machine translation model based on Transformer. The experimental results verify the advantages of the document-level Mongolian-Chinese machine translation model integrated with context information in translation performance.