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NICT’s Machine Translation Systems for CCMT-2019 Translation Task

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1104)

Abstract

This paper describes the NICT’s neural machine translation systems for Chinese\(\leftrightarrow \)English directions in the CCMT-2019 shared news translation task. We used the provided parallel data augmented with a large quantity of back-translated monolingual data to train state-of-the-art NMT systems. We then employed techniques that have been proven to be most effective, such as fine-tuning, and model ensembling, to generate the primary submissions of Chinese\(\leftrightarrow \)English translation tasks.

Keywords

Neural machine translation CCMT-2019 NICT 

Notes

Acknowledgments

We are grateful to the anonymous reviewers and the area chair for their insightful comments and suggestions. Rui Wang was partially supported by JSPS grant-in-aid for early-career scientists (19K20354): “Unsupervised Neural Machine Translation in Universal Scenarios” and NICT tenure-track researcher startup fund “Toward Intelligent Machine Translation”.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Institute of Information and Communications TechnologyKyotoJapan

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