Two New AZee Production Rules Refining Multiplicity in French Sign Language

Emmanuella Martinod, Claire Danet, Michael Filhol


Abstract
This paper is a contribution to sign language (SL) modeling. We focus on the hitherto imprecise notion of “Multiplicity”, assumed to express plurality in French Sign Language (LSF), using AZee approach. AZee is a linguistic and formal approach to modeling LSF. It takes into account the linguistic properties and specificities of LSF while respecting constraints linked to a modeling process. We present the methodology to extract AZee production rules. Based on the analysis of strong form-meaning associations in SL data (elicited image descriptions and short news), we identified two production rules structuring the expression of multiplicity in LSF. We explain how these newly extracted production rules are different from existing ones. Our goal is to refine the AZee approach to allow the coverage of a growing part of LSF. This work could lead to an improvement in SL synthesis and SL automatic translation.
Anthology ID:
2022.signlang-1.20
Volume:
Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Eleni Efthimiou, Stavroula-Evita Fotinea, Thomas Hanke, Julie A. Hochgesang, Jette Kristoffersen, Johanna Mesch, Marc Schulder
Venue:
SignLang
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
132–138
Language:
URL:
https://aclanthology.org/2022.signlang-1.20
DOI:
Bibkey:
Cite (ACL):
Emmanuella Martinod, Claire Danet, and Michael Filhol. 2022. Two New AZee Production Rules Refining Multiplicity in French Sign Language. In Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, pages 132–138, Marseille, France. European Language Resources Association.
Cite (Informal):
Two New AZee Production Rules Refining Multiplicity in French Sign Language (Martinod et al., SignLang 2022)
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PDF:
https://aclanthology.org/2022.signlang-1.20.pdf