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Computer Science > Computation and Language

arXiv:2306.15162 (cs)
[Submitted on 27 Jun 2023 (v1), last revised 26 Oct 2023 (this version, v2)]

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

Authors:David Uthus, Garrett Tanzer, Manfred Georg
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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.
Subjects: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2306.15162 [cs.CL]
  (or arXiv:2306.15162v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2306.15162

Submission history

From: David Uthus [view email]
[v1] Tue, 27 Jun 2023 02:44:07 UTC (31 KB)
[v2] Thu, 26 Oct 2023 22:57:49 UTC (36 KB)
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