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Abstract
This paper describes our submission to the WMT20 news translation shared task in English to Japanese direction. Our main approach is based on transferring knowledge of domain and linguistic characteristics by pre-training the encoder-decoder model with large amount of in-domain monolingual data through unsupervised and supervised prediction task. We then fine-tune the model with parallel data and in-domain synthetic data, generated with iterative back-translation. For additional gain, we generate final results with an ensemble model and re-rank them with averaged models and language models. Through these methods, we achieve +5.42 BLEU score compare to the baseline model.- Anthology ID:
- 2020.wmt-1.11
- 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:
- 139–144
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.11/
- DOI:
- Bibkey:
- Cite (ACL):
- Jiwan Kim, Soyoon Park, Sangha Kim, and Yoonjung Choi. 2020. An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 139–144, Online. Association for Computational Linguistics.
- Cite (Informal):
- An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task (Kim et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.11.pdf
- Video:
- https://slideslive.com/38939599
Export citation
@inproceedings{kim-etal-2020-iterative,
title = "An Iterative Knowledge Transfer {NMT} System for {WMT}20 News Translation Task",
author = "Kim, Jiwan and
Park, Soyoon and
Kim, Sangha and
Choi, Yoonjung",
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-1.11/",
pages = "139--144",
abstract = "This paper describes our submission to the WMT20 news translation shared task in English to Japanese direction. Our main approach is based on transferring knowledge of domain and linguistic characteristics by pre-training the encoder-decoder model with large amount of in-domain monolingual data through unsupervised and supervised prediction task. We then fine-tune the model with parallel data and in-domain synthetic data, generated with iterative back-translation. For additional gain, we generate final results with an ensemble model and re-rank them with averaged models and language models. Through these methods, we achieve +5.42 BLEU score compare to the baseline model."
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<abstract>This paper describes our submission to the WMT20 news translation shared task in English to Japanese direction. Our main approach is based on transferring knowledge of domain and linguistic characteristics by pre-training the encoder-decoder model with large amount of in-domain monolingual data through unsupervised and supervised prediction task. We then fine-tune the model with parallel data and in-domain synthetic data, generated with iterative back-translation. For additional gain, we generate final results with an ensemble model and re-rank them with averaged models and language models. Through these methods, we achieve +5.42 BLEU score compare to the baseline model.</abstract>
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%0 Conference Proceedings %T An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task %A Kim, Jiwan %A Park, Soyoon %A Kim, Sangha %A Choi, Yoonjung %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 kim-etal-2020-iterative %X This paper describes our submission to the WMT20 news translation shared task in English to Japanese direction. Our main approach is based on transferring knowledge of domain and linguistic characteristics by pre-training the encoder-decoder model with large amount of in-domain monolingual data through unsupervised and supervised prediction task. We then fine-tune the model with parallel data and in-domain synthetic data, generated with iterative back-translation. For additional gain, we generate final results with an ensemble model and re-rank them with averaged models and language models. Through these methods, we achieve +5.42 BLEU score compare to the baseline model. %U https://aclanthology.org/2020.wmt-1.11/ %P 139-144
Markdown (Informal)
[An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task](https://aclanthology.org/2020.wmt-1.11/) (Kim et al., WMT 2020)
- An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task (Kim et al., WMT 2020)
ACL
- Jiwan Kim, Soyoon Park, Sangha Kim, and Yoonjung Choi. 2020. An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task. In Proceedings of the Fifth Conference on Machine Translation, pages 139–144, Online. Association for Computational Linguistics.