Abstract
The ability of machine translation (MT) models to correctly place markup is crucial to generating high-quality translations of formatted input. This paper compares two commonly used methods of representing markup tags and tests the ability of MT models to learn tag placement via training data augmentation. We study the interactions of tag representation, data augmentation size, tag complexity, and language pair to show the drawbacks and benefits of each method. We construct and release new test sets containing tagged data for three language pairs of varying difficulty.- Anthology ID:
- 2020.wmt-1.138
- 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:
- 1160–1173
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.138
- DOI:
- Bibkey:
- Cite (ACL):
- Greg Hanneman and Georgiana Dinu. 2020. How Should Markup Tags Be Translated?. In Proceedings of the Fifth Conference on Machine Translation, pages 1160–1173, Online. Association for Computational Linguistics.
- Cite (Informal):
- How Should Markup Tags Be Translated? (Hanneman & Dinu, WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.138.pdf
- Video:
- https://slideslive.com/38939626
- Code
- amazon-research/mt-markup-tags
Export citation
@inproceedings{hanneman-dinu-2020-markup,
title = "How Should Markup Tags Be Translated?",
author = "Hanneman, Greg and
Dinu, Georgiana",
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-1.138",
pages = "1160--1173",
abstract = "The ability of machine translation (MT) models to correctly place markup is crucial to generating high-quality translations of formatted input. This paper compares two commonly used methods of representing markup tags and tests the ability of MT models to learn tag placement via training data augmentation. We study the interactions of tag representation, data augmentation size, tag complexity, and language pair to show the drawbacks and benefits of each method. We construct and release new test sets containing tagged data for three language pairs of varying difficulty.",
}
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%0 Conference Proceedings %T How Should Markup Tags Be Translated? %A Hanneman, Greg %A Dinu, Georgiana %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 hanneman-dinu-2020-markup %X The ability of machine translation (MT) models to correctly place markup is crucial to generating high-quality translations of formatted input. This paper compares two commonly used methods of representing markup tags and tests the ability of MT models to learn tag placement via training data augmentation. We study the interactions of tag representation, data augmentation size, tag complexity, and language pair to show the drawbacks and benefits of each method. We construct and release new test sets containing tagged data for three language pairs of varying difficulty. %U https://aclanthology.org/2020.wmt-1.138 %P 1160-1173
Markdown (Informal)
[How Should Markup Tags Be Translated?](https://aclanthology.org/2020.wmt-1.138) (Hanneman & Dinu, WMT 2020)
- How Should Markup Tags Be Translated? (Hanneman & Dinu, WMT 2020)
ACL
- Greg Hanneman and Georgiana Dinu. 2020. How Should Markup Tags Be Translated?. In Proceedings of the Fifth Conference on Machine Translation, pages 1160–1173, Online. Association for Computational Linguistics.