Incorporating Terminology Constraints in Automatic Post-Editing
David Wan, Chris Kedzie, Faisal Ladhak, Marine Carpuat, Kathleen McKeown
Correct Metadata for
Abstract
Users of machine translation (MT) may want to ensure the use of specific lexical terminologies. While there exist techniques for incorporating terminology constraints during inference for MT, current APE approaches cannot ensure that they will appear in the final translation. In this paper, we present both autoregressive and non-autoregressive models for lexically constrained APE, demonstrating that our approach enables preservation of 95% of the terminologies and also improves translation quality on English-German benchmarks. Even when applied to lexically constrained MT output, our approach is able to improve preservation of the terminologies. However, we show that our models do not learn to copy constraints systematically and suggest a simple data augmentation technique that leads to improved performance and robustness.- Anthology ID:
- 2020.wmt-1.141
- 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:
- 1193–1204
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.141/
- DOI:
- Bibkey:
- Cite (ACL):
- David Wan, Chris Kedzie, Faisal Ladhak, Marine Carpuat, and Kathleen McKeown. 2020. Incorporating Terminology Constraints in Automatic Post-Editing. In Proceedings of the Fifth Conference on Machine Translation, pages 1193–1204, Online. Association for Computational Linguistics.
- Cite (Informal):
- Incorporating Terminology Constraints in Automatic Post-Editing (Wan et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.141.pdf
- Video:
- https://slideslive.com/38939650
- Code
- zerocstaker/constrained_ape
- Data
- eSCAPE
Export citation
@inproceedings{wan-etal-2020-incorporating,
title = "Incorporating Terminology Constraints in Automatic Post-Editing",
author = "Wan, David and
Kedzie, Chris and
Ladhak, Faisal and
Carpuat, Marine and
McKeown, Kathleen",
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-1.141/",
pages = "1193--1204",
abstract = "Users of machine translation (MT) may want to ensure the use of specific lexical terminologies. While there exist techniques for incorporating terminology constraints during inference for MT, current APE approaches cannot ensure that they will appear in the final translation. In this paper, we present both autoregressive and non-autoregressive models for lexically constrained APE, demonstrating that our approach enables preservation of 95{\%} of the terminologies and also improves translation quality on English-German benchmarks. Even when applied to lexically constrained MT output, our approach is able to improve preservation of the terminologies. However, we show that our models do not learn to copy constraints systematically and suggest a simple data augmentation technique that leads to improved performance and robustness."
}
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%0 Conference Proceedings %T Incorporating Terminology Constraints in Automatic Post-Editing %A Wan, David %A Kedzie, Chris %A Ladhak, Faisal %A Carpuat, Marine %A McKeown, Kathleen %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 wan-etal-2020-incorporating %X Users of machine translation (MT) may want to ensure the use of specific lexical terminologies. While there exist techniques for incorporating terminology constraints during inference for MT, current APE approaches cannot ensure that they will appear in the final translation. In this paper, we present both autoregressive and non-autoregressive models for lexically constrained APE, demonstrating that our approach enables preservation of 95% of the terminologies and also improves translation quality on English-German benchmarks. Even when applied to lexically constrained MT output, our approach is able to improve preservation of the terminologies. However, we show that our models do not learn to copy constraints systematically and suggest a simple data augmentation technique that leads to improved performance and robustness. %U https://aclanthology.org/2020.wmt-1.141/ %P 1193-1204
Markdown (Informal)
[Incorporating Terminology Constraints in Automatic Post-Editing](https://aclanthology.org/2020.wmt-1.141/) (Wan et al., WMT 2020)
- Incorporating Terminology Constraints in Automatic Post-Editing (Wan et al., WMT 2020)
ACL
- David Wan, Chris Kedzie, Faisal Ladhak, Marine Carpuat, and Kathleen McKeown. 2020. Incorporating Terminology Constraints in Automatic Post-Editing. In Proceedings of the Fifth Conference on Machine Translation, pages 1193–1204, Online. Association for Computational Linguistics.