Cross-Lingual Keyword Search for Sign Language

Nazif Can Tamer, Murat Saraçlar


Abstract
Sign language research most often relies on exhaustively annotated and segmented data, which is scarce even for the most studied sign languages. However, parallel corpora consisting of sign language interpreting are rarely explored. By utilizing such data for the task of keyword search, this work aims to enable information retrieval from sign language with the queries from the translated written language. With the written language translations as labels, we train a weakly supervised keyword search model for sign language and further improve the retrieval performance with two context modeling strategies. In our experiments, we compare the gloss retrieval and cross language retrieval performance on RWTH-PHOENIX-Weather 2014T dataset.
Anthology ID:
2020.signlang-1.35
Volume:
Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Eleni Efthimiou, Stavroula-Evita Fotinea, Thomas Hanke, Julie A. Hochgesang, Jette Kristoffersen, Johanna Mesch
Venue:
SignLang
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
217–223
Language:
English
URL:
https://aclanthology.org/2020.signlang-1.35
DOI:
Bibkey:
Cite (ACL):
Nazif Can Tamer and Murat Saraçlar. 2020. Cross-Lingual Keyword Search for Sign Language. In Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives, pages 217–223, Marseille, France. European Language Resources Association (ELRA).
Cite (Informal):
Cross-Lingual Keyword Search for Sign Language (Tamer & Saraçlar, SignLang 2020)
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PDF:
https://aclanthology.org/2020.signlang-1.35.pdf