@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://preview.aclanthology.org/ingestion-acl-25/2025.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."
}