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The Impact of Named Entity Translation for Neural Machine Translation

  • Jinghui Yan
  • Jiajun Zhang
  • JinAn Xu
  • Chengqing Zong
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 954)

Abstract

Named entity translation has been shown in many studies that could have positive impact on performance of sentence level neural machine translation. In this paper, we study a mainstream structure that incorporating an external named entity translation model to neural machine translation. We give several comparison experiments by applying different named entity translation model structures, to clearly represent the impact of this structure in improving quality of neural machine translation. The experiments show that the proposed approach is able to achieve posistive result on some datasets and we give our analysis of influence factors.

Keywords

Named entity Neural machine translation Named entity translation 

Notes

Acknowledgement

The research work described in this paper has been supported by the National Key Research and Development Program of China under Grant No. 2016QY02D0303 and the Natural Science Foundation of China under Grant No. 61673380.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jinghui Yan
    • 1
  • Jiajun Zhang
    • 2
    • 3
  • JinAn Xu
    • 1
  • Chengqing Zong
    • 2
    • 3
    • 4
  1. 1.Beijing Jiaotong UniversityBeijingChina
  2. 2.National Laboratory of Pattern RecognitionInstitute of Automation, CASBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.CAS Center for Excellence in Brain Science and Intelligence TechnologyBeijingChina

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