NeurIPS 2023poster52 citations
YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus
David Uthus, Garrett Tanzer, Manfred Georg
Abstract
Machine learning for sign languages is bottlenecked by data. In this paper, we present YouTube-ASL, a large-scale, open-domain corpus of American Sign Language (ASL) videos and accompanying English captions drawn from YouTube. With ~1000 hours of videos and >2500 unique signers, YouTube-ASL is ~3x as large and has ~10x as many unique signers as the largest prior ASL dataset. We train baseline models for ASL to English translation on YouTube-ASL and evaluate them on How2Sign, where we achieve a new fine-tuned state of the art of 12.397 BLEU and, for the first time, nontrivial zero-shot results.
Sign language translationdataset
BibTeX
@inproceedings{
uthus2023youtubeasl,
title={YouTube-{ASL}: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus},
author={David Uthus and Garrett Tanzer and Manfred Georg},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=QEDjXv9OyY}
}