YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus

David C. Uthus, Garrett Tanzer, Manfred Georg
6/1/2023

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Abstract

Machine learning for sign languages is bottlenecked by data. In this paper, we present YouTube-ASL, a large-scale, open-domain corpus of American Sign Language (ASL) videos and accompanying English captions drawn from YouTube. With ~1000 hours of videos and>2500 unique signers, YouTube-ASL is ~3x as large and has ~10x as many unique signers as the largest prior ASL dataset. We train baseline models for ASL to English translation on YouTube-ASL and evaluate them on How2Sign, where we achieve a new finetuned state of the art of 12.39 BLEU and, for the first time, report zero-shot results.

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Cite this paper

@article{uthus2023youtubeasl,
  title  = {YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus},
  author = {David C. Uthus and Garrett Tanzer and Manfred Georg},
  year   = {2023},
  eprint = {2306.15162},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/pdf/2306.15162},
  journal = {NEURIPS 2023 2023}
}

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