Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation

Élise Bertin-Lemée (1) , Annelies Braffort (2) , Camille Challant (2) , Claire Danet (2) , Boris Dauriac (3) , Michael Filhol , Emmanuella Martinod (2) , Jérémie Segouat

1 SYSTRAN
2 ILES - Information, Langue Ecrite et Signée
3 Mocaplab

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.

Mots clés: Sign Language; machine translation; example-based translation; synthesis

Domaines: Informatique et langage [cs.CL]; Linguistique

Élise Bertin-Lemée, Annelies Braffort, Camille Challant, Claire Danet, Boris Dauriac, et al.. Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation. 13th Conference on Language Resources and Evaluation (LREC 2022), Jun 2022, Marseille, France. ⟨hal-03720096⟩