@inproceedings{bertin-lemee-etal-2022-rosetta,
title = "Rosetta-{LSF}: an Aligned Corpus of {F}rench {S}ign {L}anguage and {F}rench for Text-to-Sign Translation",
author = "Bertin-Lem{\'e}e, Elise and
Braffort, Annelies and
Challant, Camille and
Danet, Claire and
Dauriac, Boris and
Filhol, Michael and
Martinod, Emmanuella and
Segouat, J{\'e}r{\'e}mie",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.529/",
pages = "4955--4962",
abstract = "This article presents a new French Sign Language (LSF) corpus called {\textquotedblleft}Rosetta-LSF{\textquotedblright}. 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 {\textquotedblleft}gloss transcription{\textquotedblright} 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 {\textquotedblleft}traditional{\textquotedblright} 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."
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%0 Conference Proceedings
%T Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation
%A Bertin-Lemée, Elise
%A Braffort, Annelies
%A Challant, Camille
%A Danet, Claire
%A Dauriac, Boris
%A Filhol, Michael
%A Martinod, Emmanuella
%A Segouat, Jérémie
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F bertin-lemee-etal-2022-rosetta
%X 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.
%U https://aclanthology.org/2022.lrec-1.529/
%P 4955-4962
Markdown (Informal)
[Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation](https://aclanthology.org/2022.lrec-1.529/) (Bertin-Lemée et al., LREC 2022)
ACL
- Elise Bertin-Lemée, Annelies Braffort, Camille Challant, Claire Danet, Boris Dauriac, Michael Filhol, Emmanuella Martinod, and Jérémie Segouat. 2022. Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4955–4962, Marseille, France. European Language Resources Association.