Abstract
This paper presents neural machine translation systems and their combination built for the WMT20 English-Polish and Japanese->English translation tasks. We show that using a Transformer Big architecture, additional training data synthesized from monolingual data, and combining many NMT systems through n-best list reranking improve translation quality. However, while we observed such improvements on the validation data, we did not observed similar improvements on the test data. Our analysis reveals that the presence of translationese texts in the validation data led us to take decisions in building NMT systems that were not optimal to obtain the best results on the test data.- Anthology ID:
- 2020.wmt-1.23
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 230–238
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.23
- DOI:
- Bibkey:
- Cite (ACL):
- Benjamin Marie, Raphael Rubino, and Atsushi Fujita. 2020. Combination of Neural Machine Translation Systems at WMT20. In Proceedings of the Fifth Conference on Machine Translation, pages 230–238, Online. Association for Computational Linguistics.
- Cite (Informal):
- Combination of Neural Machine Translation Systems at WMT20 (Marie et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.23.pdf
- Video:
- https://slideslive.com/38939600
Export citation
@inproceedings{marie-etal-2020-combination,
title = "Combination of Neural Machine Translation Systems at {WMT}20",
author = "Marie, Benjamin and
Rubino, Raphael and
Fujita, Atsushi",
editor = {Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.23",
pages = "230--238",
abstract = "This paper presents neural machine translation systems and their combination built for the WMT20 English-Polish and Japanese-{\textgreater}English translation tasks. We show that using a Transformer Big architecture, additional training data synthesized from monolingual data, and combining many NMT systems through n-best list reranking improve translation quality. However, while we observed such improvements on the validation data, we did not observed similar improvements on the test data. Our analysis reveals that the presence of translationese texts in the validation data led us to take decisions in building NMT systems that were not optimal to obtain the best results on the test data.",
}
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<abstract>This paper presents neural machine translation systems and their combination built for the WMT20 English-Polish and Japanese-\textgreaterEnglish translation tasks. We show that using a Transformer Big architecture, additional training data synthesized from monolingual data, and combining many NMT systems through n-best list reranking improve translation quality. However, while we observed such improvements on the validation data, we did not observed similar improvements on the test data. Our analysis reveals that the presence of translationese texts in the validation data led us to take decisions in building NMT systems that were not optimal to obtain the best results on the test data.</abstract>
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%0 Conference Proceedings %T Combination of Neural Machine Translation Systems at WMT20 %A Marie, Benjamin %A Rubino, Raphael %A Fujita, Atsushi %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F marie-etal-2020-combination %X This paper presents neural machine translation systems and their combination built for the WMT20 English-Polish and Japanese-\textgreaterEnglish translation tasks. We show that using a Transformer Big architecture, additional training data synthesized from monolingual data, and combining many NMT systems through n-best list reranking improve translation quality. However, while we observed such improvements on the validation data, we did not observed similar improvements on the test data. Our analysis reveals that the presence of translationese texts in the validation data led us to take decisions in building NMT systems that were not optimal to obtain the best results on the test data. %U https://aclanthology.org/2020.wmt-1.23 %P 230-238
Markdown (Informal)
[Combination of Neural Machine Translation Systems at WMT20](https://aclanthology.org/2020.wmt-1.23) (Marie et al., WMT 2020)
- Combination of Neural Machine Translation Systems at WMT20 (Marie et al., WMT 2020)
ACL
- Benjamin Marie, Raphael Rubino, and Atsushi Fujita. 2020. Combination of Neural Machine Translation Systems at WMT20. In Proceedings of the Fifth Conference on Machine Translation, pages 230–238, Online. Association for Computational Linguistics.