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    <abstract>Sign Language Translation (SLT) is a complex task that involves accurately interpreting sign language gestures and translating them into spoken or written language and vice versa. Its primary objective is to facilitate communication between individuals with hearing difficulties using deep learning systems. Existing approaches leverage gloss annotations of sign language gestures to assist the model in capturing the movement and differentiating various gestures. However, constructing a large-scale gloss-annotated dataset is both expensive and impractical to cover multiple languages, and pre-trained generative models cannot be efficiently used due to the lack of textual source context in SLT. To address these challenges, we propose a gloss-free framework for the WMT23 SLT task. Our system primarily consists of a visual extractor for extracting video embeddings and a generator responsible for producing the translated text. We also employ an embedding alignment block that is trained to align the embedding space of the visual extractor with that of the generator. Despite undergoing extensive training and validation, our system consistently falls short of meeting the baseline performance. Further analysis shows that our model’s poor projection rate prevents it from learning diverse visual embeddings. Our codes and model checkpoints are available at https://github.com/HKUST-KnowComp/SLT.</abstract>
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%0 Conference Proceedings
%T KnowComp Submission for WMT23 Sign Language Translation Task
%A Xu, Baixuan
%A Shi, Haochen
%A Zheng, Tianshi
%A Zong, Qing
%A Wang, Weiqi
%A Wang, Zhaowei
%A Song, Yangqiu
%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 xu-etal-2023-knowcomp
%X Sign Language Translation (SLT) is a complex task that involves accurately interpreting sign language gestures and translating them into spoken or written language and vice versa. Its primary objective is to facilitate communication between individuals with hearing difficulties using deep learning systems. Existing approaches leverage gloss annotations of sign language gestures to assist the model in capturing the movement and differentiating various gestures. However, constructing a large-scale gloss-annotated dataset is both expensive and impractical to cover multiple languages, and pre-trained generative models cannot be efficiently used due to the lack of textual source context in SLT. To address these challenges, we propose a gloss-free framework for the WMT23 SLT task. Our system primarily consists of a visual extractor for extracting video embeddings and a generator responsible for producing the translated text. We also employ an embedding alignment block that is trained to align the embedding space of the visual extractor with that of the generator. Despite undergoing extensive training and validation, our system consistently falls short of meeting the baseline performance. Further analysis shows that our model’s poor projection rate prevents it from learning diverse visual embeddings. Our codes and model checkpoints are available at https://github.com/HKUST-KnowComp/SLT.
%R 10.18653/v1/2023.wmt-1.36
%U https://aclanthology.org/2023.wmt-1.36
%U https://doi.org/10.18653/v1/2023.wmt-1.36
%P 351-358

Markdown (Informal)

[KnowComp Submission for WMT23 Sign Language Translation Task](https://aclanthology.org/2023.wmt-1.36) (Xu et al., WMT 2023)

• KnowComp Submission for WMT23 Sign Language Translation Task (Xu et al., WMT 2023)

ACL

• Baixuan Xu, Haochen Shi, Tianshi Zheng, Qing Zong, Weiqi Wang, Zhaowei Wang, and Yangqiu Song. 2023. KnowComp Submission for WMT23 Sign Language Translation Task. In Proceedings of the Eighth Conference on Machine Translation, pages 351–358, Singapore. Association for Computational Linguistics.