NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021
Hideya Mino, Kazutaka Kinugawa, Hitoshi Ito, Isao Goto, Ichiro Yamada, Takenobu Tokunaga
Abstract
This paper describes the system of our team (NHK) for the WAT 2021 Japanese-English restricted machine translation task. In this task, the aim is to improve quality while maintaining consistent terminology for scientific paper translation. This task has a unique feature, where some words in a target sentence are given in addition to a source sentence. In this paper, we use a lexically-constrained neural machine translation (NMT), which concatenates the source sentence and constrained words with a special token to input them into the encoder of NMT. The key to the successful lexically-constrained NMT is the way to extract constraints from a target sentence of training data. We propose two extraction methods: proper-noun constraint and mistranslated-word constraint. These two methods consider the importance of words and fallibility of NMT, respectively. The evaluation results demonstrate the effectiveness of our lexical-constraint method.- Anthology ID:
- 2021.wat-1.2
- 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:
- 46–52
- Language:
- URL:
- https://aclanthology.org/2021.wat-1.2
- DOI:
- 10.18653/v1/2021.wat-1.2
- Bibkey:
- Cite (ACL):
- Hideya Mino, Kazutaka Kinugawa, Hitoshi Ito, Isao Goto, Ichiro Yamada, and Takenobu Tokunaga. 2021. NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 46–52, Online. Association for Computational Linguistics.
- Cite (Informal):
- NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021 (Mino et al., WAT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wat-1.2.pdf
Export citation
@inproceedings{mino-etal-2021-nhks,
title = "{NHK}{'}s Lexically-Constrained Neural Machine Translation at {WAT} 2021",
author = "Mino, Hideya and
Kinugawa, Kazutaka and
Ito, Hitoshi and
Goto, Isao and
Yamada, Ichiro and
Tokunaga, Takenobu",
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-1.2",
doi = "10.18653/v1/2021.wat-1.2",
pages = "46--52",
abstract = "This paper describes the system of our team (NHK) for the WAT 2021 Japanese-English restricted machine translation task. In this task, the aim is to improve quality while maintaining consistent terminology for scientific paper translation. This task has a unique feature, where some words in a target sentence are given in addition to a source sentence. In this paper, we use a lexically-constrained neural machine translation (NMT), which concatenates the source sentence and constrained words with a special token to input them into the encoder of NMT. The key to the successful lexically-constrained NMT is the way to extract constraints from a target sentence of training data. We propose two extraction methods: proper-noun constraint and mistranslated-word constraint. These two methods consider the importance of words and fallibility of NMT, respectively. The evaluation results demonstrate the effectiveness of our lexical-constraint method.",
}
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%0 Conference Proceedings %T NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021 %A Mino, Hideya %A Kinugawa, Kazutaka %A Ito, Hitoshi %A Goto, Isao %A Yamada, Ichiro %A Tokunaga, Takenobu %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 mino-etal-2021-nhks %X This paper describes the system of our team (NHK) for the WAT 2021 Japanese-English restricted machine translation task. In this task, the aim is to improve quality while maintaining consistent terminology for scientific paper translation. This task has a unique feature, where some words in a target sentence are given in addition to a source sentence. In this paper, we use a lexically-constrained neural machine translation (NMT), which concatenates the source sentence and constrained words with a special token to input them into the encoder of NMT. The key to the successful lexically-constrained NMT is the way to extract constraints from a target sentence of training data. We propose two extraction methods: proper-noun constraint and mistranslated-word constraint. These two methods consider the importance of words and fallibility of NMT, respectively. The evaluation results demonstrate the effectiveness of our lexical-constraint method. %R 10.18653/v1/2021.wat-1.2 %U https://aclanthology.org/2021.wat-1.2 %U https://doi.org/10.18653/v1/2021.wat-1.2 %P 46-52
Markdown (Informal)
[NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021](https://aclanthology.org/2021.wat-1.2) (Mino et al., WAT 2021)
- NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021 (Mino et al., WAT 2021)
ACL
- Hideya Mino, Kazutaka Kinugawa, Hitoshi Ito, Isao Goto, Ichiro Yamada, and Takenobu Tokunaga. 2021. NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 46–52, Online. Association for Computational Linguistics.