YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929421897695232 |
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| author | Tanzer, Garrett Zhang, Biao |
| author_facet | Tanzer, Garrett Zhang, Biao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_11144 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus Tanzer, Garrett Zhang, Biao Computation and Language 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. |
| title | YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.11144 |