Select and Reorder: A Novel Approach for Neural Sign Language Production

Harry Walsh, Ben Saunders, Richard Bowden

Abstract
Sign languages, often categorised as low-resource languages, face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets. This paper introduces Select and Reorder (S&R), a novel approach that addresses data scarcity by breaking down the translation process into two distinct steps: Gloss Selection (GS) and Gloss Reordering (GR). Our method leverages large spoken language models and the substantial lexical overlap between source spoken languages and target sign languages to establish an initial alignment. Both steps make use of Non-AutoRegressive (NAR) decoding for reduced computation and faster inference speeds. Through this disentanglement of tasks, we achieve state-of-the-art BLEU and Rouge scores on the Meine DGS Annotated (mDGS) dataset, demonstrating a substantial BLUE-1 improvement of 37.88% in Text to Gloss (T2G) Translation. This innovative approach paves the way for more effective translation models for sign languages, even in resource-constrained settings.
Anthology ID:
2024.lrec-main.1266
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
14531–14542
Language:
URL:
https://aclanthology.org/2024.lrec-main.1266
DOI:
Bibkey:
Cite (ACL):
Harry Walsh, Ben Saunders, and Richard Bowden. 2024. Select and Reorder: A Novel Approach for Neural Sign Language Production. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14531–14542, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Select and Reorder: A Novel Approach for Neural Sign Language Production (Walsh et al., LREC-COLING 2024)
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https://preview.aclanthology.org/ingest-2024-clasp/2024.lrec-main.1266.pdf
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