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Research on Mongolian-Chinese Machine Translation Annotated with Gated Recurrent Unit Part of Speech

  • Wanwan LiuEmail author
  • Yila Su
  • Wu Nier
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

With the development and progress of Information Technology, the translation between different languages has become particularly important. Statistical Machine Translation may be able to predict a relatively accurate target word with statistical analysis method, but it cannot get a much better translation as it couldn’t fully understand the semantic context information of source language words. In order to solve this problem, the model of Mongolian-Chinese Machine Translation System could be constructed based on GRU (Gated Recurrent Unit) neural network structure and the usage of global attention mechanism to obtain bilingual alignment information. In the process of constructing a dictionary, the bilingual words are annotated to further improve the alignment probability. The research result shows that the BLEU value is certainly promoted and improved compared with previous benchmark research and traditional statistical machine translation method.

Keywords

Machine translation Gated Recurrent Unit (GRU) Attention mechanism Alignment bilingual Part of speech 

Notes

Acknowledgments

This work was partially funded by the National Natural Science Foundation of China (61363052, 61502255), Natural Science Foundation of Inner Mongolia Autonomous Region funded projects (2016MS0605), Inner Mongolia Autonomous Region Ethnic Affairs Committee Fund funded projects (MW-2017-MGYWXXH-03).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Information EngineeringInner Mongolia University of TechnologyHohhotChina

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