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.
Keywords
Connecting sign language resources to language resources for spoken languages
Machine / Deep Learning – How to get along with the size of sign language resources actually existing
Language and the Brain – Methods aiming at new multimodal experimentations
Use of (parallel) corpora and lexicons in translation studies and machine translation
@inproceedings{tamer:20032:sign-lang:lrec,
author = {Tamer, Nazif Can and Sara{\c c}lar, Murat},
title = {Cross-Lingual Keyword Search for Sign Language},
pages = {217--223},
editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
booktitle = {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},
maintitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)},
publisher = {{European Language Resources Association (ELRA)}},
address = {Marseille, France},
day = {16},
month = may,
year = {2020},
isbn = {979-10-95546-54-2},
language = {english},
url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/20032.pdf}
}