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Abstract
This paper describes our systems that were submitted to the restricted translation task at WAT 2021. In this task, the systems are required to output translated sentences that contain all given word constraints. Our system combined input augmentation and constrained beam search algorithms. Through experiments, we found that this combination significantly improves translation accuracy and can save inference time while containing all the constraints in the output. For both En->Ja and Ja->En, our systems obtained the best evaluation performances in automatic and human evaluation.- Anthology ID:
- 2021.wat-1.3
- Volume:
- Proceedings of the 8th Workshop on Asian Translation (WAT2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Toshiaki Nakazawa, Hideki Nakayama, Isao Goto, Hideya Mino, Chenchen Ding, Raj Dabre, Anoop Kunchukuttan, Shohei Higashiyama, Hiroshi Manabe, Win Pa Pa, Shantipriya Parida, Ondřej Bojar, Chenhui Chu, Akiko Eriguchi, Kaori Abe, Yusuke Oda, Katsuhito Sudoh, Sadao Kurohashi, Pushpak Bhattacharyya
- Venue:
- WAT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 53–61
- Language:
- URL:
- https://aclanthology.org/2021.wat-1.3/
- DOI:
- 10.18653/v1/2021.wat-1.3
- Bibkey:
- Cite (ACL):
- Katsuki Chousa and Makoto Morishita. 2021. Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 53–61, Online. Association for Computational Linguistics.
- Cite (Informal):
- Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021 (Chousa & Morishita, WAT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wat-1.3.pdf
- Data
- ASPEC
Export citation
@inproceedings{chousa-morishita-2021-input,
title = "Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: {NTT} at {WAT} 2021",
author = "Chousa, Katsuki and
Morishita, Makoto",
editor = "Nakazawa, Toshiaki and
Nakayama, Hideki and
Goto, Isao and
Mino, Hideya and
Ding, Chenchen and
Dabre, Raj and
Kunchukuttan, Anoop and
Higashiyama, Shohei and
Manabe, Hiroshi and
Pa, Win Pa and
Parida, Shantipriya and
Bojar, Ond{\v{r}}ej and
Chu, Chenhui and
Eriguchi, Akiko and
Abe, Kaori and
Oda, Yusuke and
Sudoh, Katsuhito and
Kurohashi, Sadao and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wat-1.3/",
doi = "10.18653/v1/2021.wat-1.3",
pages = "53--61",
abstract = "This paper describes our systems that were submitted to the restricted translation task at WAT 2021. In this task, the systems are required to output translated sentences that contain all given word constraints. Our system combined input augmentation and constrained beam search algorithms. Through experiments, we found that this combination significantly improves translation accuracy and can save inference time while containing all the constraints in the output. For both En-{\ensuremath{>}}Ja and Ja-{\ensuremath{>}}En, our systems obtained the best evaluation performances in automatic and human evaluation."
}
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%0 Conference Proceedings %T Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021 %A Chousa, Katsuki %A Morishita, Makoto %Y Nakazawa, Toshiaki %Y Nakayama, Hideki %Y Goto, Isao %Y Mino, Hideya %Y Ding, Chenchen %Y Dabre, Raj %Y Kunchukuttan, Anoop %Y Higashiyama, Shohei %Y Manabe, Hiroshi %Y Pa, Win Pa %Y Parida, Shantipriya %Y Bojar, Ondřej %Y Chu, Chenhui %Y Eriguchi, Akiko %Y Abe, Kaori %Y Oda, Yusuke %Y Sudoh, Katsuhito %Y Kurohashi, Sadao %Y Bhattacharyya, Pushpak %S Proceedings of the 8th Workshop on Asian Translation (WAT2021) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F chousa-morishita-2021-input %X This paper describes our systems that were submitted to the restricted translation task at WAT 2021. In this task, the systems are required to output translated sentences that contain all given word constraints. Our system combined input augmentation and constrained beam search algorithms. Through experiments, we found that this combination significantly improves translation accuracy and can save inference time while containing all the constraints in the output. For both En-\ensuremath>Ja and Ja-\ensuremath>En, our systems obtained the best evaluation performances in automatic and human evaluation. %R 10.18653/v1/2021.wat-1.3 %U https://aclanthology.org/2021.wat-1.3/ %U https://doi.org/10.18653/v1/2021.wat-1.3 %P 53-61
Markdown (Informal)
[Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021](https://aclanthology.org/2021.wat-1.3/) (Chousa & Morishita, WAT 2021)
- Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021 (Chousa & Morishita, WAT 2021)
ACL
- Katsuki Chousa and Makoto Morishita. 2021. Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 53–61, Online. Association for Computational Linguistics.