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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References
Dabre, R., et al.: NICT’s supervised neural machine translation systems for the WMT19 news translation task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), Association for Computational Linguistics, Florence, Italy, pp. 168–174, August 2019. https://www.aclweb.org/anthology/W19-5313
Junczys-Dowmunt, M., et al.: Marian: fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations, Melbourne, Australia, pp. 116–121 (2018). http://aclweb.org/anthology/P18-4020
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
Koehn, P., et al.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, Prague, Czech Republic, pp. 177–180 (2007). http://aclweb.org/anthology/P07-2045
Marie, B., et al.: NICT’s unsupervised neural and statistical machine translation systems for the WMT19 news translation task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), Association for Computational Linguistics, Florence, Italy, pp. 294–301, August 2019. https://www.aclweb.org/anthology/W19-5330
Marie, B., Wang, R., Fujita, A., Utiyama, M., Sumita, E.: NICT’s neural and statistical machine translation systems for the WMT18 news translation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, Belgium, Brussels, pp. 449–455, October 2018. https://www.aclweb.org/anthology/W18-6419
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of 40th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, pp. 311–318, July 2002. https://doi.org/10.3115/1073083.1073135, http://www.aclweb.org/anthology/P02-1040
Sennrich, R., Haddow, B., Birch, A.: Improving neural machine translation models with monolingual data. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany, pp. 86–96 (2016). http://aclweb.org/anthology/P16-1009
Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistic, Berlin, Germany, pp. 1715–1725 (2016). http://aclweb.org/anthology/P16-1162
Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30 (2017). https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
Wang, R., Marie, B., Utiyama, M., Sumita, E.: NICT’s corpus filtering systems for the WMT18 parallel corpus filtering task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, Association for Computational Linguistics, Belgium, Brussels, pp. 963–967, October 2018. https://doi.org/10.18653/v1/W18-6489, https://www.aclweb.org/anthology/W18-6489
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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