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YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus

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arxiv 2407.11144 v1 pith:H3J7WCCM submitted 2024-07-15 cs.CL

YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus

Garrett Tanzer , Biao Zhang This is my paper
classification cs.CL
keywords signlanguageslanguagemultilingualyoutube-sl-25parallelacrosscorpus
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Sign Language Question Answering: A New Task, Benchmark, and Baseline for Sign Language Understanding

    cs.AI 2026-07 conditional novelty 6.0

    Sign Language QA benchmarks are introduced from PHOENIX14T and CSL-Daily via template-generated questions, and a question-conditioned baseline outperforms video-language and cascaded baselines.

  2. Towards Continuous Sign Language Conversation from Isolated Signs

    cs.CV 2026-05 unverdicted novelty 6.0

    Constructs continuous sign conversation data from isolated signs using retrieval and diffusion models to train a direct sign-to-sign conversational AI.

  3. CanonSLR: Canonical-View Guided Multi-View Continuous Sign Language Recognition

    cs.CV 2026-04 unverdicted novelty 6.0

    CanonSLR uses frontal-view teacher-student distillation and temporal motion enhancement to boost multi-view continuous sign language recognition, backed by new seven-view benchmarks PT14-MV and CSL-MV created from exi...

  4. MultimodalHugs: Enabling Sign Language Processing in Hugging Face

    cs.CL 2025-09 conditional novelty 5.0

    MultimodalHugs provides a standardized TSV-based dataset format, modular processors, and Hugging Face integration to enable reproducible sign language and multimodal translation experiments.