Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22
Laia Tarres, Gerard I. Gállego, Xavier Giro-i-nieto, Jordi Torres
Correct Metadata for
Abstract
This paper describes the system developed at the Universitat Politècnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation techniques and pretraining the model with the PHOENIX-14T dataset. Our system obtains 0.50 BLEU score for the test set, improving the organizers’ baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings.- Anthology ID:
- 2022.wmt-1.97
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 994–1000
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.97/
- DOI:
- Bibkey:
- Cite (ACL):
- Laia Tarres, Gerard I. Gállego, Xavier Giro-i-nieto, and Jordi Torres. 2022. Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 994–1000, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22 (Tarres et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.97.pdf
Export citation
@inproceedings{tarres-etal-2022-tackling,
title = "Tackling Low-Resourced Sign Language Translation: {UPC} at {WMT}-{SLT} 22",
author = "Tarres, Laia and
G{\'a}llego, Gerard I. and
Giro-i-nieto, Xavier and
Torres, Jordi",
editor = {Koehn, Philipp and
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
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.97/",
pages = "994--1000",
abstract = "This paper describes the system developed at the Universitat Polit{\`e}cnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation techniques and pretraining the model with the PHOENIX-14T dataset. Our system obtains 0.50 BLEU score for the test set, improving the organizers' baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings."
}
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%0 Conference Proceedings %T Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22 %A Tarres, Laia %A Gállego, Gerard I. %A Giro-i-nieto, Xavier %A Torres, Jordi %Y Koehn, Philipp %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 Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F tarres-etal-2022-tackling %X This paper describes the system developed at the Universitat Politècnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation techniques and pretraining the model with the PHOENIX-14T dataset. Our system obtains 0.50 BLEU score for the test set, improving the organizers’ baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings. %U https://aclanthology.org/2022.wmt-1.97/ %P 994-1000
Markdown (Informal)
[Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22](https://aclanthology.org/2022.wmt-1.97/) (Tarres et al., WMT 2022)
- Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22 (Tarres et al., WMT 2022)
ACL
- Laia Tarres, Gerard I. Gállego, Xavier Giro-i-nieto, and Jordi Torres. 2022. Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 994–1000, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.