This paper deals with depiction in (French) Sign Language, the formal account AZee can provide, and how it compares, validates or simplifies the linguistic notions of classifiers and iconic structures. It reports on a partial encoding work on “Mocap1”, a corpus with a high density of depicting structures, following the same method that led to the first AZee reference corpus “40 brèves”. The approach does not postulate classifiers or iconic structures as entities separate from lexical signs, and nonetheless manages to model the corpus data. We discuss the entailed possibility to rediscover some of the useful categories, and if so define them from AZee’s premises. We also specify how a formal metric can be specified to measure iconicity in signed data. While this paper is of linguistic interest as it compares to existing theories, it also provides a concrete step to covering depicting discourse with AZee, therefore enable automatic SL animation of depiction.

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.

This article presents a new French Sign Language (LSF) corpus called “Rosetta-LSF”. It was created to support future studies on the automatic translation of written French into LSF, rendered through the animation of a virtual signer. An overview of the field highlights the importance of a quality representation of LSF. In order to obtain quality animations understandable by signers, it must surpass the simple “gloss transcription” of the LSF lexical units to use in the discourse. To achieve this, we designed a corpus composed of four types of aligned data, and evaluated its usability. These are: news headlines in French, translations of these headlines into LSF in the form of videos showing animations of a virtual signer, gloss annotations of the “traditional” type—although including additional information on the context in which each gestural unit is performed as well as their potential for adaptation to another context—and AZee representations of the videos, i.e. formal expressions capturing the necessary and sufficient linguistic information. This article describes this data, exhibiting an example from the corpus. It is available online for public research.