@inproceedings{sandoval-castaneda-etal-2023-ttics,
title = "{TTIC}{'}s Submission to {WMT}-{SLT} 23",
author = "Sandoval-Castaneda, Marcelo and
Li, Yanhong and
Shi, Bowen and
Brentari, Diane and
Livescu, Karen and
Shakhnarovich, Gregory",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.35",
doi = "10.18653/v1/2023.wmt-1.35",
pages = "344--350",
abstract = "In this paper, we describe TTIC{'}s submission to WMT 2023 Sign Language Translation task on the Swiss-German Sign Language (DSGS) to German track. Our approach explores the advantages of using large-scale self-supervised pre-training in the task of sign language translation, over more traditional approaches that rely heavily on supervision, along with costly labels such as gloss annotations. The proposed model consists of a VideoSwin transformer for image encoding, and a T5 model adapted to receive VideoSwin features as input instead of text. In WMT-SLT 22{'}s development set, this system achieves 2.03 BLEU score, a 59{\%} increase over the previous best reported performance. In the official test set, our primary submission achieves 1.1 BLEU score and 17.0 chrF score.",
}
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<abstract>In this paper, we describe TTIC’s submission to WMT 2023 Sign Language Translation task on the Swiss-German Sign Language (DSGS) to German track. Our approach explores the advantages of using large-scale self-supervised pre-training in the task of sign language translation, over more traditional approaches that rely heavily on supervision, along with costly labels such as gloss annotations. The proposed model consists of a VideoSwin transformer for image encoding, and a T5 model adapted to receive VideoSwin features as input instead of text. In WMT-SLT 22’s development set, this system achieves 2.03 BLEU score, a 59% increase over the previous best reported performance. In the official test set, our primary submission achieves 1.1 BLEU score and 17.0 chrF score.</abstract>
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%0 Conference Proceedings
%T TTIC’s Submission to WMT-SLT 23
%A Sandoval-Castaneda, Marcelo
%A Li, Yanhong
%A Shi, Bowen
%A Brentari, Diane
%A Livescu, Karen
%A Shakhnarovich, Gregory
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F sandoval-castaneda-etal-2023-ttics
%X In this paper, we describe TTIC’s submission to WMT 2023 Sign Language Translation task on the Swiss-German Sign Language (DSGS) to German track. Our approach explores the advantages of using large-scale self-supervised pre-training in the task of sign language translation, over more traditional approaches that rely heavily on supervision, along with costly labels such as gloss annotations. The proposed model consists of a VideoSwin transformer for image encoding, and a T5 model adapted to receive VideoSwin features as input instead of text. In WMT-SLT 22’s development set, this system achieves 2.03 BLEU score, a 59% increase over the previous best reported performance. In the official test set, our primary submission achieves 1.1 BLEU score and 17.0 chrF score.
%R 10.18653/v1/2023.wmt-1.35
%U https://aclanthology.org/2023.wmt-1.35
%U https://doi.org/10.18653/v1/2023.wmt-1.35
%P 344-350
Markdown (Informal)
[TTIC’s Submission to WMT-SLT 23](https://aclanthology.org/2023.wmt-1.35) (Sandoval-Castaneda et al., WMT 2023)
ACL
- Marcelo Sandoval-Castaneda, Yanhong Li, Bowen Shi, Diane Brentari, Karen Livescu, and Gregory Shakhnarovich. 2023. TTIC’s Submission to WMT-SLT 23. In Proceedings of the Eighth Conference on Machine Translation, pages 344–350, Singapore. Association for Computational Linguistics.