@inproceedings{poitier-etal-2026-long,
title = "Long-Term Sign Language Data Crowdsourcing Through Collaborative Lexicons",
author = "Poitier, Pierre and
Fink, J{\'e}r{\^o}me and
Basso Madjoukeng, Ariel and
Couplet, Adelaide and
Leleu, Margaux and
Fr{\'e}nay, Beno{\^i}t",
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-1.43/",
doi = "10.63317/3t8tpmhi2om8",
pages = "419--428",
abstract = "While there exists a multitude of different sign languages (SLs) across the world, Deaf communities often lack the digital tools required to document and process their languages. In this work, we introduce Mot-Signe (MOSI), an application designed in close collaboration with actors from the French Belgian Deaf community. Our tool enables users to search for French Belgian Sign Language (LSFB) translations or to propose new ones by recording signs themselves. This crowdsourcing approach facilitates the collection of SL data in the wild, enriching the available documentation on LSFB and proposing an innovative response to the data scarcity issue inherent to sign language processing. To evaluate the sustainability of this community-driven data collection, a longitudinal user study was conducted. Following its public release, MOSI demonstrated significant real-world adoption, enabling the collection of over 3,000 distinct LSFB signs. Notably, MOSI captures highly valuable linguistic variations and specialized vocabulary often absent from traditional corpora."
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<abstract>While there exists a multitude of different sign languages (SLs) across the world, Deaf communities often lack the digital tools required to document and process their languages. In this work, we introduce Mot-Signe (MOSI), an application designed in close collaboration with actors from the French Belgian Deaf community. Our tool enables users to search for French Belgian Sign Language (LSFB) translations or to propose new ones by recording signs themselves. This crowdsourcing approach facilitates the collection of SL data in the wild, enriching the available documentation on LSFB and proposing an innovative response to the data scarcity issue inherent to sign language processing. To evaluate the sustainability of this community-driven data collection, a longitudinal user study was conducted. Following its public release, MOSI demonstrated significant real-world adoption, enabling the collection of over 3,000 distinct LSFB signs. Notably, MOSI captures highly valuable linguistic variations and specialized vocabulary often absent from traditional corpora.</abstract>
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%0 Conference Proceedings
%T Long-Term Sign Language Data Crowdsourcing Through Collaborative Lexicons
%A Poitier, Pierre
%A Fink, Jérôme
%A Basso Madjoukeng, Ariel
%A Couplet, Adelaide
%A Leleu, Margaux
%A Frénay, Benoît
%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 poitier-etal-2026-long
%X While there exists a multitude of different sign languages (SLs) across the world, Deaf communities often lack the digital tools required to document and process their languages. In this work, we introduce Mot-Signe (MOSI), an application designed in close collaboration with actors from the French Belgian Deaf community. Our tool enables users to search for French Belgian Sign Language (LSFB) translations or to propose new ones by recording signs themselves. This crowdsourcing approach facilitates the collection of SL data in the wild, enriching the available documentation on LSFB and proposing an innovative response to the data scarcity issue inherent to sign language processing. To evaluate the sustainability of this community-driven data collection, a longitudinal user study was conducted. Following its public release, MOSI demonstrated significant real-world adoption, enabling the collection of over 3,000 distinct LSFB signs. Notably, MOSI captures highly valuable linguistic variations and specialized vocabulary often absent from traditional corpora.
%R 10.63317/3t8tpmhi2om8
%U https://aclanthology.org/2026.signlang-1.43/
%U https://doi.org/10.63317/3t8tpmhi2om8
%P 419-428
Markdown (Informal)
[Long-Term Sign Language Data Crowdsourcing Through Collaborative Lexicons](https://aclanthology.org/2026.signlang-1.43/) (Poitier et al., SignLang 2026)
ACL
- Pierre Poitier, Jérôme Fink, Ariel Basso Madjoukeng, Adelaide Couplet, Margaux Leleu, and Benoît Frénay. 2026. Long-Term Sign Language Data Crowdsourcing Through Collaborative Lexicons. In Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion, pages 419–428, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).