@inproceedings{halbout-etal-2026-learning,
title = "Learning to Spot Signs from Named Entities. A study on {F}rench {S}ign {L}anguage.",
author = "Halbout, Julie and
Braffort, Annelies and
Gouiff{\`e}s, Mich{\`e}le and
Fabre, Diandra and
Lascar, Julie",
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-1.22/",
doi = "10.63317/26i8n4zuyzyx",
pages = "203--211",
abstract = "French Sign Language (LSF) is a low-resourced language, with few available corpora, most of which being only partially annotated. Previous work on other sign languages has explored automatic sign annotation using subtitles as weak supervision, existing signaries, or mouthing cues. This paper focuses on the corpus Matignon-LSF, by first leveraging lexical token spotting then by studying Named Entities (locations, companies, persons). Accounting for the Named entities enables the automatic detection of 30{\%} to 100{\%} more signs per class and improves the spotting of rare signs. In addition, this work provides insights into the signing of named entities and contributes resources for improving LSF-to-French translation models."
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<abstract>French Sign Language (LSF) is a low-resourced language, with few available corpora, most of which being only partially annotated. Previous work on other sign languages has explored automatic sign annotation using subtitles as weak supervision, existing signaries, or mouthing cues. This paper focuses on the corpus Matignon-LSF, by first leveraging lexical token spotting then by studying Named Entities (locations, companies, persons). Accounting for the Named entities enables the automatic detection of 30% to 100% more signs per class and improves the spotting of rare signs. In addition, this work provides insights into the signing of named entities and contributes resources for improving LSF-to-French translation models.</abstract>
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%0 Conference Proceedings
%T Learning to Spot Signs from Named Entities. A study on French Sign Language.
%A Halbout, Julie
%A Braffort, Annelies
%A Gouiffès, Michèle
%A Fabre, Diandra
%A Lascar, Julie
%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 halbout-etal-2026-learning
%X French Sign Language (LSF) is a low-resourced language, with few available corpora, most of which being only partially annotated. Previous work on other sign languages has explored automatic sign annotation using subtitles as weak supervision, existing signaries, or mouthing cues. This paper focuses on the corpus Matignon-LSF, by first leveraging lexical token spotting then by studying Named Entities (locations, companies, persons). Accounting for the Named entities enables the automatic detection of 30% to 100% more signs per class and improves the spotting of rare signs. In addition, this work provides insights into the signing of named entities and contributes resources for improving LSF-to-French translation models.
%R 10.63317/26i8n4zuyzyx
%U https://aclanthology.org/2026.signlang-1.22/
%U https://doi.org/10.63317/26i8n4zuyzyx
%P 203-211
Markdown (Informal)
[Learning to Spot Signs from Named Entities. A study on French Sign Language.](https://aclanthology.org/2026.signlang-1.22/) (Halbout et al., SignLang 2026)
ACL