Abstract
In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference. We notice that some words are always copied during paraphrasing, which we call copy knowledge. Considering the stability of such knowledge, a good candidate translation should contain all these words appeared in the reference sentence. Therefore, in this participation of the WMT’2018 metrics shared task we introduce a simple statistical method for copy knowledge extraction, and incorporate it into Meteor metric, resulting in a new machine translation metric Meteor++. Our experiments show that Meteor++ can nicely integrate copy knowledge and improve the performance significantly on WMT17 and WMT15 evaluation sets.- Anthology ID:
- W18-6454
- 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:
- 740–745
- Language:
- URL:
- https://aclanthology.org/W18-6454
- DOI:
- 10.18653/v1/W18-6454
- Bibkey:
- Cite (ACL):
- Yinuo Guo, Chong Ruan, and Junfeng Hu. 2018. Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 740–745, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation (Guo et al., WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6454.pdf
Export citation
@inproceedings{guo-etal-2018-meteor,
title = "{M}eteor++: Incorporating Copy Knowledge into Machine Translation Evaluation",
author = "Guo, Yinuo and
Ruan, Chong and
Hu, Junfeng",
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/W18-6454",
doi = "10.18653/v1/W18-6454",
pages = "740--745",
abstract = "In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference. We notice that some words are always copied during paraphrasing, which we call \textbf{copy knowledge}. Considering the stability of such knowledge, a good candidate translation should contain all these words appeared in the reference sentence. Therefore, in this participation of the WMT{'}2018 metrics shared task we introduce a simple statistical method for copy knowledge extraction, and incorporate it into Meteor metric, resulting in a new machine translation metric \textbf{Meteor++}. Our experiments show that Meteor++ can nicely integrate copy knowledge and improve the performance significantly on WMT17 and WMT15 evaluation sets.",
}
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%0 Conference Proceedings %T Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation %A Guo, Yinuo %A Ruan, Chong %A Hu, Junfeng %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 guo-etal-2018-meteor %X In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference. We notice that some words are always copied during paraphrasing, which we call copy knowledge. Considering the stability of such knowledge, a good candidate translation should contain all these words appeared in the reference sentence. Therefore, in this participation of the WMT’2018 metrics shared task we introduce a simple statistical method for copy knowledge extraction, and incorporate it into Meteor metric, resulting in a new machine translation metric Meteor++. Our experiments show that Meteor++ can nicely integrate copy knowledge and improve the performance significantly on WMT17 and WMT15 evaluation sets. %R 10.18653/v1/W18-6454 %U https://aclanthology.org/W18-6454 %U https://doi.org/10.18653/v1/W18-6454 %P 740-745
Markdown (Informal)
[Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation](https://aclanthology.org/W18-6454) (Guo et al., WMT 2018)
- Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation (Guo et al., WMT 2018)
ACL
- Yinuo Guo, Chong Ruan, and Junfeng Hu. 2018. Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 740–745, Belgium, Brussels. Association for Computational Linguistics.