Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching
Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and enabling communication between signing and non-signing communities. While large weakly-aligned datasets have enabled pre-training at scale and gloss-free methods have reduced reliance on expert annotation, high-quality parallel sign video-text pairs for fine-tuning remain scarce, limiting generalisation on long-tail vocabulary and unseen constructions. We propose a corpus augmentation approach that requires no additional human annotation, external sign-language video corpora, or generative video models, relying only on the existing gloss-anno...
| Authors | Zsolt Robotka, Ádám Rák, Jalal Al-Afandi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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