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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

Garrett Tanzer , Biao Zhang This is my paper · ORCID
classification cs.CL
keywords signlanguageslanguagemultilingualyoutube-sl-25parallelacrosscorpus
verification ladder T0 review T1 audit T2 compute T3 formal
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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. Full citation record

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

    cs.AI 2026-07 conditional novelty 6.0 of 10

    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. MultimodalHugs: Enabling Sign Language Processing in Hugging Face

    cs.CL 2025-09 conditional novelty 5.0 of 10

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

  3. SAGE: Segment-Aware Gloss-Free Encoding for Token-Efficient Sign Language Translation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    SAGE uses a frozen sign-segmentation model to turn sign videos into about half as many visual tokens as prior methods, then aligns those tokens with a language model to reach BLEU-4 of 24.10 on PHOENIX14T.

  4. Sign Spotting Disambiguation using Large Language Models

    cs.CV 2025-07 conditional novelty 5.0 of 10

    LLM-based beam search disambiguation improves dictionary sign spotting WER from 47.2% to 44.4% on an internal BSL dataset.

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