# Content4All Open Research Sign Language Translation Datasets

arXiv: https://arxiv.org/abs/2105.02351 · HTML: https://mlsanity.com/paper/2105.02351/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Progressive Transformers for End-to-End Sign Language Production](https://mlsanity.com/paper/2004.14874/) (2020)
- [MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language](https://mlsanity.com/paper/1812.01053/) (2018)
- [Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2002.03187/) (2020)
- [Multi-channel Transformers for Multi-articulatory Sign Language Translation](https://mlsanity.com/paper/2009.00299/) (2020)
- [Adversarial Training for Multi-Channel Sign Language Production](https://mlsanity.com/paper/2008.12405/) (2020)
- [Quantitative Survey of the State of the Art in Sign Language Recognition](https://mlsanity.com/paper/2008.09918/) (2020)
- [Single-Network Whole-Body Pose Estimation](https://mlsanity.com/paper/1909.13423/) (2019)
- [Weakly-Supervised 3D Pose Estimation from a Single Image using Multi-View Consistency](https://mlsanity.com/paper/1909.06119/) (2019)

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

- [One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer](https://mlsanity.com/paper/2303.16160/) (2023)
- [YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus](https://mlsanity.com/paper/2306.15162/) (2023)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives](https://mlsanity.com/paper/2107.11317/) (2021)
- [Sign Language Translation from Instructional Videos](https://mlsanity.com/paper/2304.06371/) (2023)
- [T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text](https://mlsanity.com/paper/2406.07119/) (2024)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [Machine Translation from Signed to Spoken Languages: State of the Art and Challenges](https://mlsanity.com/paper/2202.03086/) (2022)
- [Automatic dense annotation of large-vocabulary sign language videos](https://mlsanity.com/paper/2208.02802/) (2022)
- [JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language Processing](https://mlsanity.com/paper/2311.10174/) (2023)
- [AnonySIGN: Novel Human Appearance Synthesis for Sign Language Video Anonymisation](https://mlsanity.com/paper/2107.10685/) (2021)
- [Is context all you need? Scaling Neural Sign Language Translation to Large Domains of Discourse](https://mlsanity.com/paper/2308.09622/) (2023)
- [Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation](https://mlsanity.com/paper/2506.00129/) (2025)
- [Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production](https://mlsanity.com/paper/2112.05277/) (2021)
- [A New Dataset for End-to-End Sign Language Translation: The Greek Elementary School Dataset](https://mlsanity.com/paper/2310.04753/) (2023)
- [iSign: A Benchmark for Indian Sign Language Processing](https://mlsanity.com/paper/2407.05404/) (2024)
- [Deep Understanding of Sign Language for Sign to Subtitle Alignment](https://mlsanity.com/paper/2503.03287/) (2025)
- [Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognition](https://mlsanity.com/paper/2506.03615/) (2025)
- [Reconsidering Sentence-Level Sign Language Translation](https://mlsanity.com/paper/2406.11049/) (2024)
- [ISLTranslate: Dataset for Translating Indian Sign Language](https://mlsanity.com/paper/2307.05440/) (2023)
- [Toward American Sign Language Processing in the Real World: Data, Tasks, and Methods](https://mlsanity.com/paper/2308.12419/) (2023)
- [E-TSL: A Continuous Educational Turkish Sign Language Dataset with Baseline Methods](https://mlsanity.com/paper/2405.02984/) (2024)
- [MultimodalHugs: Enabling Sign Language Processing in Hugging Face](https://mlsanity.com/paper/2509.09729/) (2025)
- [SkelCap: Automated Generation of Descriptive Text from Skeleton Keypoint Sequences](https://mlsanity.com/paper/2405.02977/) (2024)
- [Co-creation for Sign Language Processing and Machine Translation](https://mlsanity.com/paper/2503.01553/) (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)
- [Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards](https://mlsanity.com/paper/2606.19352/) (2026)
- [OneSign: Unifying Sign Language Understanding Tasks with One Model](https://mlsanity.com/paper/2609.33090/) (2026)

## Abstract (arXiv)

Computational sign language research lacks the large-scale datasets that enables the creation of useful reallife applications. To date, most research has been limited to prototype systems on small domains of discourse, e.g. weather forecasts. To address this issue and to push the field forward, we release six datasets comprised of 190 hours of footage on the larger domain of news. From this, 20 hours of footage have been annotated by Deaf experts and interpreters and is made publicly available for research purposes. In this paper, we share the dataset collection process and tools developed to enable the alignment of sign language video and subtitles, as well as baseline translation results to underpin future research.