We introduce a new sign language production (SLP) and sign language translation (SLT) dataset, NIASL2021, consisting of 201,026 Korean-KSL data pairs. KSL translations of Korean source texts are represented in three formats: video recordings, keypoint position data, and time-aligned gloss annotations for each hand (using a 7,989 sign vocabulary) and for eight different non-manual signals (NMS). We evaluated our sign language elicitation methodology and found that text-based prompting had a negative effect on translation quality in terms of naturalness and comprehension. We recommend distilling text into a visual medium before translating into sign language or adding a prompt-blind review step to text-based translation methodologies.
@inproceedings{huertaenochian:70012:sltat:lrec,
author = {Huerta-Enochian, Mathew and Lee, Du Hui and Myung, Hye Jin and Byun, Kang Suk and Lee, Jun Woo},
title = {{KoSign} Sign Language Translation Project: Introducing The {NIASL2021} Dataset},
pages = {59--66},
editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee},
booktitle = {Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives},
maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
publisher = {{European Language Resources Association (ELRA)}},
address = {Marseille, France},
day = {24},
month = jun,
year = {2022},
isbn = {979-10-95546-82-5},
language = {english},
url = {http://www.lrec-conf.org/proceedings/lrec2022/workshops/sltat/pdf/2022.sltat-1.9}
}