TTIC’s WMT-SLT 22 Sign Language Translation System
Bowen Shi, Diane Brentari, Gregory Shakhnarovich, Karen Livescu
Abstract
We describe TTIC’s model submission to WMT-SLT 2022 task on sign language translation (Swiss-German Sign Language (DSGS) - German). Our model consists of an I3D backbone for image encoding and a Transformerbased encoder-decoder model for sequence modeling. The I3D is pre-trained with isolated sign recognition using the WLASL dataset. The model is based on RGB images alone and does not rely on the pre-extracted human pose. We explore a few different strategies for model training in this paper. Our system achieves 0.3 BLEU score and 0.195 Chrf score on the official test set.- Anthology ID:
- 2022.wmt-1.96
- 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:
- 989–993
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.96
- DOI:
- Bibkey:
- Cite (ACL):
- Bowen Shi, Diane Brentari, Gregory Shakhnarovich, and Karen Livescu. 2022. TTIC’s WMT-SLT 22 Sign Language Translation System. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 989–993, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- TTIC’s WMT-SLT 22 Sign Language Translation System (Shi et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.96.pdf
Export citation
@inproceedings{shi-etal-2022-ttics,
title = "{TTIC}{'}s {WMT}-{SLT} 22 Sign Language Translation System",
author = "Shi, Bowen and
Brentari, Diane and
Shakhnarovich, Gregory and
Livescu, Karen",
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-1.96",
pages = "989--993",
abstract = "We describe TTIC{'}s model submission to WMT-SLT 2022 task on sign language translation (Swiss-German Sign Language (DSGS) - German). Our model consists of an I3D backbone for image encoding and a Transformerbased encoder-decoder model for sequence modeling. The I3D is pre-trained with isolated sign recognition using the WLASL dataset. The model is based on RGB images alone and does not rely on the pre-extracted human pose. We explore a few different strategies for model training in this paper. Our system achieves 0.3 BLEU score and 0.195 Chrf score on the official test set.",
}
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%0 Conference Proceedings %T TTIC’s WMT-SLT 22 Sign Language Translation System %A Shi, Bowen %A Brentari, Diane %A Shakhnarovich, Gregory %A Livescu, Karen %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 shi-etal-2022-ttics %X We describe TTIC’s model submission to WMT-SLT 2022 task on sign language translation (Swiss-German Sign Language (DSGS) - German). Our model consists of an I3D backbone for image encoding and a Transformerbased encoder-decoder model for sequence modeling. The I3D is pre-trained with isolated sign recognition using the WLASL dataset. The model is based on RGB images alone and does not rely on the pre-extracted human pose. We explore a few different strategies for model training in this paper. Our system achieves 0.3 BLEU score and 0.195 Chrf score on the official test set. %U https://aclanthology.org/2022.wmt-1.96 %P 989-993
Markdown (Informal)
[TTIC’s WMT-SLT 22 Sign Language Translation System](https://aclanthology.org/2022.wmt-1.96) (Shi et al., WMT 2022)
- TTIC’s WMT-SLT 22 Sign Language Translation System (Shi et al., WMT 2022)
ACL
- Bowen Shi, Diane Brentari, Gregory Shakhnarovich, and Karen Livescu. 2022. TTIC’s WMT-SLT 22 Sign Language Translation System. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 989–993, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.