---
title: "Sign-Language Datasets at Scale — paper by Ni et al. (2026)"
description: "Automatic sign-language recognition (SLR), translation (SLT) and production (SLP) are limited less by modelling and more by… 154 references."
authors: ["Yiming Ni", "Zhi-Qi Cheng", "Jiayu Li", "Wei Cheng"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1928/"
doi: "10.18653/v1/2026.acl-long.1928"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ni-2026-sign-language-datasets-scale-comprehensive/"
---
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# Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards

**Authors:** Yiming Ni, Zhi-Qi Cheng, [Jiayu Li](https://md.research.elamur.ai/free/en/authors/jiayu-li/) and [Wei Cheng](https://md.research.elamur.ai/free/en/authors/wei-cheng/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1928/) · [PDF](https://aclanthology.org/2026.acl-long.1928.pdf) · [DOI 10.18653/v1/2026.acl-long.1928](https://doi.org/10.18653/v1/2026.acl-long.1928) · [OpenAlex](https://openalex.org/W7166809358)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Automatic sign-language recognition (SLR), translation (SLT) and production (SLP) are limited less by modelling and more by data. Resources are scattered, annotated in incompatible ways, and concentrated on a few high-resource languages such as ASL, BSL, CSL and DGS. This survey takes a data-centric approach and catalogues 120 publicly documented datasets covering 35 sign languages. Earlier surveys handled at most a few dozen resources and usually focused on a single task. The authors sort the material into fingerspelling sets, isolated-sign datasets and continuous corpora. They compare these by modality, signer demographics, vocabulary size and domain, and identify recurring weaknesses: modality imbalance, inconsistent annotation granularity, signer bias and poor cross-dataset generalization. They also collect benchmark results, offer guidance on collecting and annotating new data, and propose a 24-field Sign-Language Datasheet, released together with a public GitHub index. The paper serves as a common reference for building more inclusive, comparable and reproducible sign-language systems. Its documentation template could also be adapted for other low-resource language data.

## Key points

- **Coverage:** 120 datasets across 35 sign languages and all three tasks (SLR, SLT, SLP). The largest prior survey in the authors' comparison table covered 34 datasets.
- **Representative resources:**
  - Fingerspelling: ChicagoFSWild, ArASL, AzSLD.
  - Isolated signs: MS-ASL, WLASL, ASL Citizen, AUTSL, MM-WLAuslan (about 2,500 h and 282,900 videos).
  - Continuous corpora: RWTH-PHOENIX14T, CSL-Daily, How2Sign, YouTube-ASL, BOBSL.
- **Benchmarks:** the consolidated leaderboards cover only five flagship general-purpose datasets. Specialised domains such as medical or legal signing are not yet benchmarked.
- **Annotation guidance:** the authors recommend a layered scheme. It starts with a video ID and a sentence-level translation, then adds glosses, temporal sign boundaries, skeleton poses and Facial Action Units (extractable with OpenFace). ELAN is identified as the most widely used annotation tool.
- **Open issues:** the datasheet has not yet been reviewed by Deaf signers or communities. Metadata was taken from the original papers rather than re-annotated, and privacy and bias concerns are flagged.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) — University, Seattle, United States · 353 works · 200,043 citations

Also on the byline: [University of Washington Tacoma](https://ror.org/05n8t2628).

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🧭 Topics (1): [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · 1,220 works

## Cite

Cite as: Yiming Ni, Zhi-Qi Cheng, Jiayu Li and Wei Cheng. 2026. Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ni2026sign,
  title = {{Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards}},
  author = {Yiming Ni, Zhi-Qi Cheng, Jiayu Li and Wei Cheng},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1928},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1928}
}
```

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*Updated 1 November 2026*