@inproceedings{wan-etal-2025-sign2vis,
title = "{S}ign2{V}is: Automated Data Visualization from Sign Language",
author = "Wan, Yao and
Wu, Yang and
Li, Zhen and
Zhang, Guobiao and
Zhang, Hongyu and
Zhao, Zhou and
Jin, Hai and
Wang, April",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.918/",
doi = "10.18653/v1/2025.findings-acl.918",
pages = "17839--17857",
ISBN = "979-8-89176-256-5",
abstract = "Data visualizations, such as bar charts and histograms, are essential for analyzing and exploring data, enabling the effective communication of insights. While existing methods have been proposed to translate natural language descriptions into visualization queries, they focus solely on spoken languages, overlooking sign languages, which comprise about 200 variants used by 70 million Deaf and Hard-of-Hearing (DHH) individuals. To fill this gap, this paper proposes Sign2Vis, a sign language interface that enables the DHH community to engage more fully with data analysis. We first construct a paired dataset that includes sign language pose videos and their corresponding visualization queries. Using this dataset, we evaluate a variety of models, including both pipeline-based and end-to-end approaches. Extensive experiments, along with a user study involving 15 participants, demonstrate the effectiveness of Sign2Vis. Finally, we share key insights from our evaluation and highlight the need for more accessible and user-centered tools to support the DHH community in interactive data analytics."
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<abstract>Data visualizations, such as bar charts and histograms, are essential for analyzing and exploring data, enabling the effective communication of insights. While existing methods have been proposed to translate natural language descriptions into visualization queries, they focus solely on spoken languages, overlooking sign languages, which comprise about 200 variants used by 70 million Deaf and Hard-of-Hearing (DHH) individuals. To fill this gap, this paper proposes Sign2Vis, a sign language interface that enables the DHH community to engage more fully with data analysis. We first construct a paired dataset that includes sign language pose videos and their corresponding visualization queries. Using this dataset, we evaluate a variety of models, including both pipeline-based and end-to-end approaches. Extensive experiments, along with a user study involving 15 participants, demonstrate the effectiveness of Sign2Vis. Finally, we share key insights from our evaluation and highlight the need for more accessible and user-centered tools to support the DHH community in interactive data analytics.</abstract>
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%0 Conference Proceedings
%T Sign2Vis: Automated Data Visualization from Sign Language
%A Wan, Yao
%A Wu, Yang
%A Li, Zhen
%A Zhang, Guobiao
%A Zhang, Hongyu
%A Zhao, Zhou
%A Jin, Hai
%A Wang, April
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wan-etal-2025-sign2vis
%X Data visualizations, such as bar charts and histograms, are essential for analyzing and exploring data, enabling the effective communication of insights. While existing methods have been proposed to translate natural language descriptions into visualization queries, they focus solely on spoken languages, overlooking sign languages, which comprise about 200 variants used by 70 million Deaf and Hard-of-Hearing (DHH) individuals. To fill this gap, this paper proposes Sign2Vis, a sign language interface that enables the DHH community to engage more fully with data analysis. We first construct a paired dataset that includes sign language pose videos and their corresponding visualization queries. Using this dataset, we evaluate a variety of models, including both pipeline-based and end-to-end approaches. Extensive experiments, along with a user study involving 15 participants, demonstrate the effectiveness of Sign2Vis. Finally, we share key insights from our evaluation and highlight the need for more accessible and user-centered tools to support the DHH community in interactive data analytics.
%R 10.18653/v1/2025.findings-acl.918
%U https://aclanthology.org/2025.findings-acl.918/
%U https://doi.org/10.18653/v1/2025.findings-acl.918
%P 17839-17857
Markdown (Informal)
[Sign2Vis: Automated Data Visualization from Sign Language](https://aclanthology.org/2025.findings-acl.918/) (Wan et al., Findings 2025)
ACL
- Yao Wan, Yang Wu, Zhen Li, Guobiao Zhang, Hongyu Zhang, Zhou Zhao, Hai Jin, and April Wang. 2025. Sign2Vis: Automated Data Visualization from Sign Language. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17839–17857, Vienna, Austria. Association for Computational Linguistics.