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.
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Notes
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The Chinese-English task is jointly held by CCMT-2019 and WMT19. Therefore, part of these two system description papers are overlapped.
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NVIDIA® Tesla® P100 16 Gb.
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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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Chen, K., Wang, R., Utiyama, M., Sumita, E. (2019). NICT’s Machine Translation Systems for CCMT-2019 Translation Task. In: Huang, S., Knight, K. (eds) Machine Translation. CCMT 2019. Communications in Computer and Information Science, vol 1104. Springer, Singapore. https://doi.org/10.1007/978-981-15-1721-1_8
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DOI: https://doi.org/10.1007/978-981-15-1721-1_8
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