Spatio-temporal Sign Language Representation and Translation
Yasser Hamidullah, Josef Van Genabith, Cristina España-bonet
Correct Metadata for
Abstract
This paper describes the DFKI-MLT submission to the WMT-SLT 2022 sign language translation (SLT) task from Swiss German Sign Language (video) into German (text). State-of-the-art techniques for SLT use a generic seq2seq architecture with customized input embeddings. Instead of word embeddings as used in textual machine translation, SLT systems use features extracted from video frames. Standard approaches often do not benefit from temporal features. In our participation, we present a system that learns spatio-temporal feature representations and translation in a single model, resulting in a real end-to-end architecture expected to better generalize to new data sets. Our best system achieved 5 ± 1 BLEU points on the development set, but the performance on the test dropped to 0.11 ± 0.06 BLEU points.- Anthology ID:
- 2022.wmt-1.94
- 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:
- 977–982
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.94/
- DOI:
- Bibkey:
- Cite (ACL):
- Yasser Hamidullah, Josef Van Genabith, and Cristina España-bonet. 2022. Spatio-temporal Sign Language Representation and Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 977–982, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Spatio-temporal Sign Language Representation and Translation (Hamidullah et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.94.pdf
Export citation
@inproceedings{hamidullah-etal-2022-spatio,
title = "Spatio-temporal Sign Language Representation and Translation",
author = "Hamidullah, Yasser and
Van Genabith, Josef and
Espa{\~n}a-bonet, Cristina",
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-1.94/",
pages = "977--982",
abstract = "This paper describes the DFKI-MLT submission to the WMT-SLT 2022 sign language translation (SLT) task from Swiss German Sign Language (video) into German (text). State-of-the-art techniques for SLT use a generic seq2seq architecture with customized input embeddings. Instead of word embeddings as used in textual machine translation, SLT systems use features extracted from video frames. Standard approaches often do not benefit from temporal features. In our participation, we present a system that learns spatio-temporal feature representations and translation in a single model, resulting in a real end-to-end architecture expected to better generalize to new data sets. Our best system achieved $5 \pm 1$ BLEU points on the development set, but the performance on the test dropped to $0.11 \pm 0.06$ BLEU points."
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%0 Conference Proceedings %T Spatio-temporal Sign Language Representation and Translation %A Hamidullah, Yasser %A Van Genabith, Josef %A España-bonet, Cristina %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 hamidullah-etal-2022-spatio %X This paper describes the DFKI-MLT submission to the WMT-SLT 2022 sign language translation (SLT) task from Swiss German Sign Language (video) into German (text). State-of-the-art techniques for SLT use a generic seq2seq architecture with customized input embeddings. Instead of word embeddings as used in textual machine translation, SLT systems use features extracted from video frames. Standard approaches often do not benefit from temporal features. In our participation, we present a system that learns spatio-temporal feature representations and translation in a single model, resulting in a real end-to-end architecture expected to better generalize to new data sets. Our best system achieved 5 \pm 1 BLEU points on the development set, but the performance on the test dropped to 0.11 \pm 0.06 BLEU points. %U https://aclanthology.org/2022.wmt-1.94/ %P 977-982
Markdown (Informal)
[Spatio-temporal Sign Language Representation and Translation](https://aclanthology.org/2022.wmt-1.94/) (Hamidullah et al., WMT 2022)
- Spatio-temporal Sign Language Representation and Translation (Hamidullah et al., WMT 2022)
ACL
- Yasser Hamidullah, Josef Van Genabith, and Cristina España-bonet. 2022. Spatio-temporal Sign Language Representation and Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 977–982, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.