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)
- Venue:
- WMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 989–993
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.96
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.wmt-1.96.pdf