TencentFmRD Neural Machine Translation for WMT18

Bojie Hu; Ambyer Han; Shen Huang

doi:10.18653/v1/W18-6413

TencentFmRD Neural Machine Translation for WMT18

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Abstract

This paper describes the Neural Machine Translation (NMT) system of TencentFmRD for Chinese↔English news translation tasks of WMT 2018. Our systems are neural machine translation systems trained with our original system TenTrans. TenTrans is an improved NMT system based on Transformer self-attention mechanism. In addition to the basic settings of Transformer training, TenTrans uses multi-model fusion techniques, multiple features reranking, different segmentation models and joint learning. Finally, we adopt some data selection strategies to fine-tune the trained system and achieve a stable performance improvement. Our Chinese→English system achieved the second best BLEU scores and fourth best cased BLEU scores among all WMT18 submitted systems.

Anthology ID:: W18-6413
Volume:: Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Month:: October
Year:: 2018
Address:: Belgium, Brussels
Editors:: Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
Venue:: WMT
SIG:: SIGMT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 410–417
Language:
URL:: https://aclanthology.org/W18-6413/
DOI:: 10.18653/v1/W18-6413
Bibkey:
Cite (ACL):: Bojie Hu, Ambyer Han, and Shen Huang. 2018. TencentFmRD Neural Machine Translation for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 410–417, Belgium, Brussels. Association for Computational Linguistics.
Cite (Informal):: TencentFmRD Neural Machine Translation for WMT18 (Hu et al., WMT 2018)
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PDF:: https://aclanthology.org/W18-6413.pdf

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@inproceedings{hu-etal-2018-tencentfmrd,
    title = "{T}encent{F}m{RD} Neural Machine Translation for {WMT}18",
    author = "Hu, Bojie  and
      Han, Ambyer  and
      Huang, Shen",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Specia, Lucia  and
      Turchi, Marco  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-6413/",
    doi = "10.18653/v1/W18-6413",
    pages = "410--417",
    abstract = "This paper describes the Neural Machine Translation (NMT) system of TencentFmRD for Chinese{\ensuremath{\leftrightarrow}}English news translation tasks of WMT 2018. Our systems are neural machine translation systems trained with our original system TenTrans. TenTrans is an improved NMT system based on Transformer self-attention mechanism. In addition to the basic settings of Transformer training, TenTrans uses multi-model fusion techniques, multiple features reranking, different segmentation models and joint learning. Finally, we adopt some data selection strategies to fine-tune the trained system and achieve a stable performance improvement. Our Chinese{\textrightarrow}English system achieved the second best BLEU scores and fourth best cased BLEU scores among all WMT18 submitted systems."
}

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%0 Conference Proceedings
%T TencentFmRD Neural Machine Translation for WMT18
%A Hu, Bojie
%A Han, Ambyer
%A Huang, Shen
%Y Bojar, Ondřej
%Y Chatterjee, Rajen
%Y Federmann, Christian
%Y Fishel, Mark
%Y Graham, Yvette
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Specia, Lucia
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 October
%I Association for Computational Linguistics
%C Belgium, Brussels
%F hu-etal-2018-tencentfmrd
%X This paper describes the Neural Machine Translation (NMT) system of TencentFmRD for Chinese\ensuremathłeftrightarrowEnglish news translation tasks of WMT 2018. Our systems are neural machine translation systems trained with our original system TenTrans. TenTrans is an improved NMT system based on Transformer self-attention mechanism. In addition to the basic settings of Transformer training, TenTrans uses multi-model fusion techniques, multiple features reranking, different segmentation models and joint learning. Finally, we adopt some data selection strategies to fine-tune the trained system and achieve a stable performance improvement. Our Chinese→English system achieved the second best BLEU scores and fourth best cased BLEU scores among all WMT18 submitted systems.
%R 10.18653/v1/W18-6413
%U https://aclanthology.org/W18-6413/
%U https://doi.org/10.18653/v1/W18-6413
%P 410-417

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Markdown (Informal)

[TencentFmRD Neural Machine Translation for WMT18](https://aclanthology.org/W18-6413/) (Hu et al., WMT 2018)

TencentFmRD Neural Machine Translation for WMT18 (Hu et al., WMT 2018)

ACL

Bojie Hu, Ambyer Han, and Shen Huang. 2018. TencentFmRD Neural Machine Translation for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 410–417, Belgium, Brussels. Association for Computational Linguistics.