SONAR-SLT: Multilingual Sign Language Translation via Language-Agnostic Sentence Embedding Supervision

• URL: http://arxiv.org/abs/2510.19398v1
• Date: Wed, 22 Oct 2025 09:17:31 GMT
• Title: SONAR-SLT: Multilingual Sign Language Translation via Language-Agnostic Sentence Embedding Supervision
• Abstract summary: Sign language translation (SLT) is typically trained with text in a single spoken language.<n>We employ language-agnostic, multimodal embeddings trained on text and speech from multiple languages to supervise SLT.<n>Our results demonstrate that language-agnostic embedding supervision, combined with coupled augmentation, provides a scalable and semantically robust alternative to traditional SLT training.
• Score: 14.416218321809824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sign language translation (SLT) is typically trained with text in a single spoken language, which limits scalability and cross-language generalization. Earlier approaches have replaced gloss supervision with text-based sentence embeddings, but up to now, these remain tied to a specific language and modality. In contrast, here we employ language-agnostic, multimodal embeddings trained on text and speech from multiple languages to supervise SLT, enabling direct multilingual translation. To address data scarcity, we propose a coupled augmentation method that combines multilingual target augmentations (i.e. translations into many languages) with video-level perturbations, improving model robustness. Experiments show consistent BLEURT gains over text-only sentence embedding supervision, with larger improvements in low-resource settings. Our results demonstrate that language-agnostic embedding supervision, combined with coupled augmentation, provides a scalable and semantically robust alternative to traditional SLT training.

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