Abstract
This paper reports on our participation with the MUCOW test suite at the WMT 2020 news translation task. We introduced MUCOW at WMT 2019 to measure the ability of MT systems to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. MUCOW is created automatically using existing resources, and the evaluation process is also entirely automated. We evaluate all participating systems of the language pairs English -> Czech, English -> German, and English -> Russian and compare the results with those obtained at WMT 2019. While current NMT systems are fairly good at handling ambiguous source words, we could not identify any substantial progress - at least to the extent that it is measurable by the MUCOW method - in that area over the last year.- Anthology ID:
- 2020.wmt-1.40
- 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:
- 365–370
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.40
- DOI:
- Bibkey:
- Cite (ACL):
- Yves Scherrer, Alessandro Raganato, and Jörg Tiedemann. 2020. The MUCOW word sense disambiguation test suite at WMT 2020. In Proceedings of the Fifth Conference on Machine Translation, pages 365–370, Online. Association for Computational Linguistics.
- Cite (Informal):
- The MUCOW word sense disambiguation test suite at WMT 2020 (Scherrer et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.40.pdf
- Video:
- https://slideslive.com/38939567
- Code
- Helsinki-NLP/MuCoW
Export citation
@inproceedings{scherrer-etal-2020-mucow,
title = "The {MUCOW} word sense disambiguation test suite at {WMT} 2020",
author = {Scherrer, Yves and
Raganato, Alessandro and
Tiedemann, J{\"o}rg},
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-1.40",
pages = "365--370",
abstract = "This paper reports on our participation with the MUCOW test suite at the WMT 2020 news translation task. We introduced MUCOW at WMT 2019 to measure the ability of MT systems to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. MUCOW is created automatically using existing resources, and the evaluation process is also entirely automated. We evaluate all participating systems of the language pairs English -{\textgreater} Czech, English -{\textgreater} German, and English -{\textgreater} Russian and compare the results with those obtained at WMT 2019. While current NMT systems are fairly good at handling ambiguous source words, we could not identify any substantial progress - at least to the extent that it is measurable by the MUCOW method - in that area over the last year.",
}
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<abstract>This paper reports on our participation with the MUCOW test suite at the WMT 2020 news translation task. We introduced MUCOW at WMT 2019 to measure the ability of MT systems to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. MUCOW is created automatically using existing resources, and the evaluation process is also entirely automated. We evaluate all participating systems of the language pairs English -\textgreater Czech, English -\textgreater German, and English -\textgreater Russian and compare the results with those obtained at WMT 2019. While current NMT systems are fairly good at handling ambiguous source words, we could not identify any substantial progress - at least to the extent that it is measurable by the MUCOW method - in that area over the last year.</abstract>
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%0 Conference Proceedings %T The MUCOW word sense disambiguation test suite at WMT 2020 %A Scherrer, Yves %A Raganato, Alessandro %A Tiedemann, Jörg %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 scherrer-etal-2020-mucow %X This paper reports on our participation with the MUCOW test suite at the WMT 2020 news translation task. We introduced MUCOW at WMT 2019 to measure the ability of MT systems to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. MUCOW is created automatically using existing resources, and the evaluation process is also entirely automated. We evaluate all participating systems of the language pairs English -\textgreater Czech, English -\textgreater German, and English -\textgreater Russian and compare the results with those obtained at WMT 2019. While current NMT systems are fairly good at handling ambiguous source words, we could not identify any substantial progress - at least to the extent that it is measurable by the MUCOW method - in that area over the last year. %U https://aclanthology.org/2020.wmt-1.40 %P 365-370
Markdown (Informal)
[The MUCOW word sense disambiguation test suite at WMT 2020](https://aclanthology.org/2020.wmt-1.40) (Scherrer et al., WMT 2020)
- The MUCOW word sense disambiguation test suite at WMT 2020 (Scherrer et al., WMT 2020)
ACL
- Yves Scherrer, Alessandro Raganato, and Jörg Tiedemann. 2020. The MUCOW word sense disambiguation test suite at WMT 2020. In Proceedings of the Fifth Conference on Machine Translation, pages 365–370, Online. Association for Computational Linguistics.