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

arXiv: https://arxiv.org/abs/2407.11144 · HTML: https://mlsanity.com/paper/2407.11144/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [Learning Compact Metrics for MT](https://mlsanity.com/paper/2110.06341/) (2021)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Neural Sign Language Translation based on Human Keypoint Estimation](https://mlsanity.com/paper/1811.11436/) (2018)
- [A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News](https://mlsanity.com/paper/2405.00980/) (2024)
- [iSign: A Benchmark for Indian Sign Language Processing](https://mlsanity.com/paper/2407.05404/) (2024)
- [E-TSL: A Continuous Educational Turkish Sign Language Dataset with Baseline Methods](https://mlsanity.com/paper/2405.02984/) (2024)

## Built on by (most cited first, 30 of 36)

- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction](https://mlsanity.com/paper/2411.16765/) (2024)
- [SignRep: Enhancing Self-Supervised Sign Representations](https://mlsanity.com/paper/2503.08529/) (2025)
- [Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation](https://mlsanity.com/paper/2506.00129/) (2025)
- [Gloss-Free Sign Language Translation: An Unbiased Evaluation of Progress in the Field](https://mlsanity.com/paper/2603.13240/) (2026)
- [Scaling Sign Language Translation](https://mlsanity.com/paper/2407.11855/) (2024)
- [FSboard: Over 3 million characters of ASL fingerspelling collected via smartphones](https://mlsanity.com/paper/2407.15806/) (2024)
- [SignSplat: Rendering Sign Language via Gaussian Splatting](https://mlsanity.com/paper/2505.02108/) (2025)
- [Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognition](https://mlsanity.com/paper/2506.03615/) (2025)
- [Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment](https://mlsanity.com/paper/2512.08040/) (2025)
- [Exploring Pose-based Sign Language Translation: Ablation Studies and Attention Insights](https://mlsanity.com/paper/2507.01532/) (2025)
- [Segment, Embed, and Align: A Universal Recipe for Aligning Subtitles to Signing](https://mlsanity.com/paper/2512.08094/) (2025)
- [SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning](https://mlsanity.com/paper/2603.10446/) (2026)
- [CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language](https://mlsanity.com/paper/2604.22367/) (2026)
- [Sign Language Question Answering: A New Task, Benchmark, and Baseline for Sign Language Understanding](https://mlsanity.com/paper/2607.27826/) (2026)
- [Sign Spotting Disambiguation using Large Language Models](https://mlsanity.com/paper/2507.03703/) (2025)
- [SAGE: Segment-Aware Gloss-Free Encoding for Token-Efficient Sign Language Translation](https://mlsanity.com/paper/2507.09266/) (2025)
- [MultimodalHugs: Enabling Sign Language Processing in Hugging Face](https://mlsanity.com/paper/2509.09729/) (2025)
- [Saudi Sign Language Translation Using T5](https://mlsanity.com/paper/2510.11183/) (2025)
- [Bootstrapping Sign Language Annotations with Sign Language Models](https://mlsanity.com/paper/2604.07606/) (2026)
- [SIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation](https://mlsanity.com/paper/2606.28626/) (2026)
- [The TUB Sign Language Corpus Collection](https://mlsanity.com/paper/2508.05374/) (2025)
- [Diverse Signer Avatars with Manual and Non-Manual Feature Modelling for Sign Language Production](https://mlsanity.com/paper/2508.15988/) (2025)
- [SSL-SLR: Self-Supervised Representation Learning for Sign Language Recognition](https://mlsanity.com/paper/2509.05188/) (2025)
- [Seeing, Signing, and Saying: A Vision-Language Model-Assisted Pipeline for Sign Language Data Acquisition and Curation from Social Media](https://mlsanity.com/paper/2510.25413/) (2025)
- [Beyond a Single Reference: Training and Evaluation with Paraphrases in Sign Language Translation](https://mlsanity.com/paper/2601.21128/) (2026)
- [Towards Multi-View Sign Language Understanding: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2604.18184/) (2026)
- [Targeted Linguistic Analysis of Sign Language Models with Minimal Translation Pairs](https://mlsanity.com/paper/2604.27232/) (2026)
- [Towards Continuous Sign Language Conversation from Isolated Signs](https://mlsanity.com/paper/2605.14705/) (2026)
- [ODE-Based Transformer Decoders for Iterative Sign Language Translation](https://mlsanity.com/paper/2608.11352/) (2026)

## Abstract (arXiv)

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.