title: YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language
    Parallel Corpus
summary: 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.
paper_authors._id: 66972f14e1dc637c097d2f46
paper_authors.name: Garrett Tanzer
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-12-19T20:22:02.611Z
paper_authors._id: 6764805be4e0f5d5dcb6715e
paper_authors.avatarUrl: /avatars/ff12a0ed1380519a79bcd3fb20a640da.svg
paper_authors.fullname: Garrett Tanzer
paper_authors.type: user
paper_authors.user: gtanzer
paper_authors._id: 66972f14e1dc637c097d2f47
paper_authors.name: Biao Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-19T11:23:51.326Z
paper_authors._id: 6346a600bcb5e67902ac6f44
paper_authors.avatarUrl: /avatars/cf42043c19990bb388223a61c7a1e292.svg
paper_authors.fullname: Biao Zhang
paper_authors.type: user
paper_authors.user: bzhanggo
paper_title: YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language
    Parallel Corpus
paper_summary: 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.
paper_ai_keywords: neuralstudied open-domain multilingual sign language videos well-aligned captions YouTube-SL-25 YouTube-SL YouTube-ASL parallel sign language dataset sign-to-text tasks unified multilingual multitask model T5 multilingual transfer higher-resource sign languages lower-resource sign languages