Abstract
This paper describes NTT’s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus cleaning, and right-to-left n-best re-ranking techniques. Through our experiments, we identified two keys for improving accuracy: filtering noisy training sentences and right-to-left re-ranking. We also found that the Transformer model requires more training data than the RNN-based model, and the RNN-based model sometimes achieves better accuracy than the Transformer model when the corpus is small.- Anthology ID:
- W18-6421
- 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:
- 461–466
- Language:
- URL:
- https://aclanthology.org/W18-6421
- DOI:
- 10.18653/v1/W18-6421
- Bibkey:
- Cite (ACL):
- Makoto Morishita, Jun Suzuki, and Masaaki Nagata. 2018. NTT’s Neural Machine Translation Systems for WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 461–466, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- NTT’s Neural Machine Translation Systems for WMT 2018 (Morishita et al., WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6421.pdf
- Data
- WMT 2018
Export citation
@inproceedings{morishita-etal-2018-ntts,
title = "{NTT}{'}s Neural Machine Translation Systems for {WMT} 2018",
author = "Morishita, Makoto and
Suzuki, Jun and
Nagata, Masaaki",
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/W18-6421",
doi = "10.18653/v1/W18-6421",
pages = "461--466",
abstract = "This paper describes NTT{'}s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus cleaning, and right-to-left n-best re-ranking techniques. Through our experiments, we identified two keys for improving accuracy: filtering noisy training sentences and right-to-left re-ranking. We also found that the Transformer model requires more training data than the RNN-based model, and the RNN-based model sometimes achieves better accuracy than the Transformer model when the corpus is small.",
}
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%0 Conference Proceedings %T NTT’s Neural Machine Translation Systems for WMT 2018 %A Morishita, Makoto %A Suzuki, Jun %A Nagata, Masaaki %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 morishita-etal-2018-ntts %X This paper describes NTT’s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus cleaning, and right-to-left n-best re-ranking techniques. Through our experiments, we identified two keys for improving accuracy: filtering noisy training sentences and right-to-left re-ranking. We also found that the Transformer model requires more training data than the RNN-based model, and the RNN-based model sometimes achieves better accuracy than the Transformer model when the corpus is small. %R 10.18653/v1/W18-6421 %U https://aclanthology.org/W18-6421 %U https://doi.org/10.18653/v1/W18-6421 %P 461-466
Markdown (Informal)
[NTT’s Neural Machine Translation Systems for WMT 2018](https://aclanthology.org/W18-6421) (Morishita et al., WMT 2018)
- NTT’s Neural Machine Translation Systems for WMT 2018 (Morishita et al., WMT 2018)
ACL
- Makoto Morishita, Jun Suzuki, and Masaaki Nagata. 2018. NTT’s Neural Machine Translation Systems for WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 461–466, Belgium, Brussels. Association for Computational Linguistics.