@inproceedings{de-la-garza-etal-2026-extracting,
title = "Extracting Signs from Weakly Aligned Sign Language Corpora: A Study on {LSF} and {LSM}",
author = "de la Garza, Lorena and
Halbout, Julie and
Lascar, Julie and
Martinez, Niels and
Curiel, Arturo and
Gouiff{\`e}s, Mich{\`e}le and
Braffort, Annelies",
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-1.19/",
doi = "10.63317/38kfot52b4dz",
pages = "174--183",
abstract = "This paper presents a framework for the automatic annotation of sign language data across different recording conditions, including original and interpreted content. The proposed approach integrates weak alignment, sign segmentation, and multiple instance learning with a contrastive loss. The resulting annotations are subsequently refined and filtered to enhance their reliability. Our method was applied to two historically related sign languages, French Sign Language (LSF) and Mexican Sign Language (LSM). This led to the creation of two signaries, comprising approximately 2k categories in LSF (25k occurrences) and 41 categories in LSM (1k occurrences). Both resources provide valuable support for future research in artificial intelligence and linguistics, particularly for comparative analyses between the two languages. A seminal analysis is presented as part of this paper."
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<abstract>This paper presents a framework for the automatic annotation of sign language data across different recording conditions, including original and interpreted content. The proposed approach integrates weak alignment, sign segmentation, and multiple instance learning with a contrastive loss. The resulting annotations are subsequently refined and filtered to enhance their reliability. Our method was applied to two historically related sign languages, French Sign Language (LSF) and Mexican Sign Language (LSM). This led to the creation of two signaries, comprising approximately 2k categories in LSF (25k occurrences) and 41 categories in LSM (1k occurrences). Both resources provide valuable support for future research in artificial intelligence and linguistics, particularly for comparative analyses between the two languages. A seminal analysis is presented as part of this paper.</abstract>
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%0 Conference Proceedings
%T Extracting Signs from Weakly Aligned Sign Language Corpora: A Study on LSF and LSM
%A de la Garza, Lorena
%A Halbout, Julie
%A Lascar, Julie
%A Martinez, Niels
%A Curiel, Arturo
%A Gouiffès, Michèle
%A Braffort, Annelies
%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 de-la-garza-etal-2026-extracting
%X This paper presents a framework for the automatic annotation of sign language data across different recording conditions, including original and interpreted content. The proposed approach integrates weak alignment, sign segmentation, and multiple instance learning with a contrastive loss. The resulting annotations are subsequently refined and filtered to enhance their reliability. Our method was applied to two historically related sign languages, French Sign Language (LSF) and Mexican Sign Language (LSM). This led to the creation of two signaries, comprising approximately 2k categories in LSF (25k occurrences) and 41 categories in LSM (1k occurrences). Both resources provide valuable support for future research in artificial intelligence and linguistics, particularly for comparative analyses between the two languages. A seminal analysis is presented as part of this paper.
%R 10.63317/38kfot52b4dz
%U https://aclanthology.org/2026.signlang-1.19/
%U https://doi.org/10.63317/38kfot52b4dz
%P 174-183
Markdown (Informal)
[Extracting Signs from Weakly Aligned Sign Language Corpora: A Study on LSF and LSM](https://aclanthology.org/2026.signlang-1.19/) (de la Garza et al., SignLang 2026)
ACL
- Lorena de la Garza, Julie Halbout, Julie Lascar, Niels Martinez, Arturo Curiel, Michèle Gouiffès, and Annelies Braffort. 2026. Extracting Signs from Weakly Aligned Sign Language Corpora: A Study on LSF and LSM. In Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, pages 174–183, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).