Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22

Laia Tarres, Gerard I. Gállego, Xavier Giro-i-nieto, Jordi Torres


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)
Venue:
WMT
SIG:
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)
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PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2022.wmt-1.97.pdf