YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus
<|reference_start|>YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus: Even for better-studied sign languages like American Sign Language (ASL), data is the bottleneck for machine learning research. The situation is worse yet for the many other sign languages used by Deaf/Hard of Hearing communities around the world. In this paper, we present YouTube-SL-25, a large-scale, open-domain multilingual corpus of sign language videos with seemingly well-aligned captions drawn from YouTube. With >3000 hours of videos across >25 sign languages, YouTube-SL-25 is a) >3x the size of YouTube-ASL, b) the largest parallel sign language dataset to date, and c) the first or largest parallel dataset for many of its component languages. We provide baselines for sign-to-text tasks using a unified multilingual multitask model based on T5 and report scores on benchmarks across 4 sign languages. The results demonstrate that multilingual transfer benefits both higher- and lower-resource sign languages within YouTube-SL-25.<|reference_end|>
@article{tanzer2024youtube-sl-25:,
    title={YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language
  Parallel Corpus},
    author={Garrett Tanzer, Biao Zhang},
    journal={arXiv preprint arXiv:2407.11144},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11144},
    primaryClass={cs.CL}
}