A Linked Data Approach for linking and aligning Sign Language and Spoken Language Data

Thierry Declerck, Sam Bigeard, Fahad Khan, Irene Murtagh, Sussi Olsen, Mike Rosner, Ineke Schuurman, Andon Tchechmedjiev, Andy Way


Abstract
We present work dealing with a Linked Open Data (LOD)-compliant representation of Sign Language (SL) data, with the goal of supporting the cross-lingual alignment of SL data and their linking to Spoken Language (SpL) data. The proposed representation is based on activities of groups of researchers in the field of SL who have investigated the use of Open Multilingual Wordnet (OMW) datasets for (manually) cross-linking SL data or for linking SL and SpL data. Another group of researchers is proposing an XML encoding of articulatory elements of SLs and (manually) linking those to an SpL lexical resource. We propose an RDF-based representation of those various data. This unified formal representation offers a semantic repository of information on SL and SpL data that could be accessed for supporting the creation of datasets for training or evaluating NLP applications dealing with SLs, thinking for example of Machine Translation (MT) between SLs and between SLs and SpLs.
Anthology ID:
2023.at4ssl-1.2
Volume:
Proceedings of the Second International Workshop on Automatic Translation for Signed and Spoken Languages
Month:
June
Year:
2023
Address:
Tampere, Finland
Editors:
Dimitar Shterionov, Mirella De Sisto, Mathias Muller, Davy Van Landuyt, Rehana Omardeen, Shaun Oboyle, Annelies Braffort, Floris Roelofsen, Fred Blain, Bram Vanroy, Eleftherios Avramidis
Venue:
AT4SSL
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
11–21
Language:
URL:
https://aclanthology.org/2023.at4ssl-1.2
DOI:
Bibkey:
Cite (ACL):
Thierry Declerck, Sam Bigeard, Fahad Khan, Irene Murtagh, Sussi Olsen, Mike Rosner, Ineke Schuurman, Andon Tchechmedjiev, and Andy Way. 2023. A Linked Data Approach for linking and aligning Sign Language and Spoken Language Data. In Proceedings of the Second International Workshop on Automatic Translation for Signed and Spoken Languages, pages 11–21, Tampere, Finland. European Association for Machine Translation.
Cite (Informal):
A Linked Data Approach for linking and aligning Sign Language and Spoken Language Data (Declerck et al., AT4SSL 2023)
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PDF:
https://aclanthology.org/2023.at4ssl-1.2.pdf