LLM-generated pseudo glosses, reordered via weak video supervision, enable sign language translation that rivals gloss-supervised models while needing only 30 gloss examples.
2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We introduce the first highly multilingual speech and American Sign Language (ASL) comprehension dataset by extending BELEBELE. Our dataset covers 74 spoken languages at the intersection of BELEBELE and FLEURS, and one sign language (ASL). We evaluate 2M-BELEBELE dataset for both 5-shot and zero-shot settings and across languages, the speech comprehension accuracy is ~ 2-3% average lower compared to reading comprehension.
citation-role summary
dataset 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation
LLM-generated pseudo glosses, reordered via weak video supervision, enable sign language translation that rivals gloss-supervised models while needing only 30 gloss examples.