@inproceedings{brown-etal-2026-signgpt,
title = "{S}ign{GPT} and the Visual Language Toolkit",
author = "Brown, Matt and
Ranum, Oline and
Fish, Edward and
Proctor, Heidi and
Woll, Bencie and
Bowden, Richard and
Cormier, Kearsy",
editor = "Efthimiou, Eleni and
Fotinea, Stavroula-Evita and
Hanke, Thomas and
Hochgesang, Julie A. and
Mesch, Johanna and
Schulder, Marc",
booktitle = "Proceedings of the {LREC} 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.signlang-1.6/",
doi = "10.63317/5ez54d2x8yos",
pages = "51--60",
abstract = "SignGPT{'}s Visual Language Toolkit (VLTK) aims to remove fundamental barriers to large scale sign language modelling by developing data-driven, linguistically grounded methods for continuous sign language recognition. We first identify fundamental issues around the ecological validity of potential data sources (e.g. broadcast media with interpreted signing or captions, scraping of social media). We contrast these with the currently highly resource-intensive development of curated sign language corpora based on linguistic principles. The VLTK addresses this scarcity of high quality sign language data by providing semi-automated glossing and other recognition tools, driving large scale corpus expansion without sacrificing linguistic principles. Unlike prior systems that rely on sparse glossing, the project integrates dense temporal annotation, non-manual and non-lexical feature tracking, and transformer-based architectures to capture the multimodal and spatial structure of signing. By aligning machine vision innovation with linguistic insights and community-embedded evaluation, SignGPT establishes a foundation for robust and extensible sign language models."
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<abstract>SignGPT’s Visual Language Toolkit (VLTK) aims to remove fundamental barriers to large scale sign language modelling by developing data-driven, linguistically grounded methods for continuous sign language recognition. We first identify fundamental issues around the ecological validity of potential data sources (e.g. broadcast media with interpreted signing or captions, scraping of social media). We contrast these with the currently highly resource-intensive development of curated sign language corpora based on linguistic principles. The VLTK addresses this scarcity of high quality sign language data by providing semi-automated glossing and other recognition tools, driving large scale corpus expansion without sacrificing linguistic principles. Unlike prior systems that rely on sparse glossing, the project integrates dense temporal annotation, non-manual and non-lexical feature tracking, and transformer-based architectures to capture the multimodal and spatial structure of signing. By aligning machine vision innovation with linguistic insights and community-embedded evaluation, SignGPT establishes a foundation for robust and extensible sign language models.</abstract>
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%0 Conference Proceedings
%T SignGPT and the Visual Language Toolkit
%A Brown, Matt
%A Ranum, Oline
%A Fish, Edward
%A Proctor, Heidi
%A Woll, Bencie
%A Bowden, Richard
%A Cormier, Kearsy
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F brown-etal-2026-signgpt
%X SignGPT’s Visual Language Toolkit (VLTK) aims to remove fundamental barriers to large scale sign language modelling by developing data-driven, linguistically grounded methods for continuous sign language recognition. We first identify fundamental issues around the ecological validity of potential data sources (e.g. broadcast media with interpreted signing or captions, scraping of social media). We contrast these with the currently highly resource-intensive development of curated sign language corpora based on linguistic principles. The VLTK addresses this scarcity of high quality sign language data by providing semi-automated glossing and other recognition tools, driving large scale corpus expansion without sacrificing linguistic principles. Unlike prior systems that rely on sparse glossing, the project integrates dense temporal annotation, non-manual and non-lexical feature tracking, and transformer-based architectures to capture the multimodal and spatial structure of signing. By aligning machine vision innovation with linguistic insights and community-embedded evaluation, SignGPT establishes a foundation for robust and extensible sign language models.
%R 10.63317/5ez54d2x8yos
%U https://aclanthology.org/2026.signlang-1.6/
%U https://doi.org/10.63317/5ez54d2x8yos
%P 51-60
Markdown (Informal)
[SignGPT and the Visual Language Toolkit](https://aclanthology.org/2026.signlang-1.6/) (Brown et al., SignLang 2026)
ACL
- Matt Brown, Oline Ranum, Edward Fish, Heidi Proctor, Bencie Woll, Richard Bowden, and Kearsy Cormier. 2026. SignGPT and the Visual Language Toolkit. In Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, pages 51–60, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).