Leveraging Unannotated Sign Language Data via a Robust Data Augmentation Method for Contrastive Representation Learning

Basso Madjoukeng, Ariel | Poitier, Pierre | Kenmogne, Belise Edith | Couplet, Adelaide | Leleu, Margaux | Benoit, Frenay

Volume:: Proceedings of the LREC2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion
Venue:: Palma, Mallorca, Spain
Date:: 16 May 2026
Pages:: 10–16
Publisher:: European Language Resources Association (ELRA)
Licence:: CC BY-NC 4.0
sign-lang ID:: 26037
ISBN:: 978-2-493814-82-1

Abstract

Contrastive learning is a deep learning paradigm that allows the learning of useful representations without annotations. In many fields, including sign language recognition (SLR), contrastive approaches have proven to be very effective for developing pretrained models. To learn representations, they generate augmented variants of an instance through augmentation techniques and then maximize their similarities. The quality of the learned representations is strongly correlated with the augmentations used during training. In several fields, specialized augmentations have been developed and adopted. However, in SLR, we observed two trends: contrastive-based SLR approaches often rely on augmentations that are not realistic for the application (e.g., vertical flip, excessive rotations); specialized augmentation methods lack robustness. Hence, when they are used as a starting point for contrastive algorithms, the learned representations are often irrelevant, and sometimes sensitive. These issues considerably affect the accuracy of SLR models on downstream tasks. In response, this paper proposes a robust augmentation method specially designed for contrastive approaches applied to SLR. The results show an improvement in accuracy during linear evaluation and semi-supervised learning with only 30% of annotations.

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Citation in ACL Citation Format

Ariel Basso Madjoukeng, Pierre Poitier, Belise Edith Kenmogne, Adelaide Couplet, Margaux Leleu, Frenay Benoit. 2026. Leveraging Unannotated Sign Language Data via a Robust Data Augmentation Method for Contrastive Representation Learning. In Proceedings of the LREC2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, pages 10–16, Palma, Mallorca, Spain. European Language Resources Association (ELRA).

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@inproceedings{bassomadjoukeng:26037:sign-lang:lrec,
  author    = {Basso Madjoukeng, Ariel and Poitier, Pierre and Kenmogne, Belise Edith and Couplet, Adelaide and Leleu, Margaux and Benoit, Frenay},
  title     = {Leveraging Unannotated Sign Language Data via a Robust Data Augmentation Method for Contrastive Representation Learning},
  pages     = {10--16},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2026} 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion},
  maintitle = {15th International Conference on Language Resources and Evaluation ({LREC} 2026)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Palma, Mallorca, Spain},
  day       = {16},
  month     = may,
  year      = {2026},
  isbn      = {978-2-493814-82-1},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/26037.html}
}