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Long-Term Sign Language Data Crowdsourcing Through Collaborative Lexicons

Paper · 2026 · Pierre Poitier, Jérôme Fink, Ariel Basso Madjoukeng et al.

Summary

Sign languages such as French Belgian Sign Language (LSFB) have very little digital data. The available corpora are mostly recorded in studios, cost a lot to annotate, and do not keep up with how quickly signing vocabulary changes. Poitier et al. present Mot-Signe (MOSI), a free public French–LSFB lexicon built together with members of the French-Belgian deaf community. People can look up the LSFB equivalent of a French word, and they can also record and submit their own signs. Data collection is therefore a by-product of a tool that is useful to its users, and it does not depend on paid annotation campaigns. MOSI extends the controlled LSFB-Lab corpus with crowdsourced recordings made in real-world conditions by many different signers. The authors ran a longitudinal user study to check whether people keep contributing over time, and they analysed data from the public release. Users have contributed more than 3,000 distinct signs, and most of them are new vocabulary, including regional variants and domain-specific terms. This shows that a community dictionary can work as a long-term source of realistic sign language data for linguistic documentation and machine learning.

Key points

  • MOSI (mot-signe.be) lets users both search for LSFB signs and submit their own sign videos. The authors say this is the first SL dictionary combined with long-term crowdsourcing.
  • The tool builds on the studio-recorded LSFB-Lab corpus (Meurant, 2015; Fink et al., 2021) and adds diverse webcam- and phone-style recordings collected outside the studio.
  • After public release, users contributed more than 3,000 distinct LSFB signs. Over 80% of them were vocabulary missing from the existing public lexicon.
  • For comparison, the paper cites:
    • LSFB Dico with more than 5,900 signs, Elix (LSF) with more than 27,000 signs, and SpreadTheSign with more than 200,000 signs across 40 SLs. None of these lets users add signs.
    • The largest SL datasets, at about 200,000 sign occurrences.
    • An annotation cost of up to 100 hours of work per hour of video.
  • Volunteers contribute for altruistic reasons, as on Wikipedia, rather than for payment. A longitudinal study was used to assess user retention and the sustainability of collection.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of NamurNamur · Belgium6 works · 21 citations🔬Namur Digital InstituteNamur · Belgium4 works · 4 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksDevelopmental and Educational Psychology · 499 worksHuman-Computer Interaction · 141 works

Cite

Cite as Pierre Poitier, Jérôme Fink, Ariel Basso Madjoukeng, Adelaide Couplet, Margaux Leleu and Benoît Frénay. 2026. Long-Term Sign Language Data Crowdsourcing Through Collaborative Lexicons. Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/3t8tpmhi2om8

@inproceedings{poitier2026long,
  title = {{Long-Term Sign Language Data Crowdsourcing Through Collaborative Lexicons}},
  author = {Pierre Poitier, Jérôme Fink, Ariel Basso Madjoukeng, Adelaide Couplet, Margaux Leleu and Benoît Frénay},
  booktitle = {Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion},
  year = {2026},
  doi = {10.63317/3t8tpmhi2om8},
  url = {https://doi.org/10.63317/3t8tpmhi2om8}
}

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