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.
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Garrett Tanzer
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2024-12-19T20:22:02.611Z
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Garrett Tanzer
user
gtanzer
66972f14e1dc637c097d2f47
Biao Zhang
claimed_verified
2024-07-19T11:23:51.326Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6346a600bcb5e67902ac6f44/Kw-Ucmxx5S9WdlYDxQQ-z.jpeg
Biao Zhang
user
bzhanggo
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.
YouTube-SL-25 is a large, multilingual sign language dataset that improves sign-to-text tasks through multilingual transfer learning.
T5 multilingual multitask model sign-to-text tasks multilingual transfer