JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language Processing
This addresses the lack of diversity in sign language datasets, which has hindered progress in recognition and translation tasks, particularly for underrepresented sign languages, though it is incremental as it builds on existing translation methods.
The paper tackles the problem of insufficient and skewed data in sign language processing by introducing JWSign, a large multilingual dataset of 2,530 hours of Bible translations in 98 sign languages, and reports that multilingual systems outperform bilingual baselines, with related language clustering improving translation quality in higher-resource scenarios.
Advancements in sign language processing have been hindered by a lack of sufficient data, impeding progress in recognition, translation, and production tasks. The absence of comprehensive sign language datasets across the world's sign languages has widened the gap in this field, resulting in a few sign languages being studied more than others, making this research area extremely skewed mostly towards sign languages from high-income countries. In this work we introduce a new large and highly multilingual dataset for sign language translation: JWSign. The dataset consists of 2,530 hours of Bible translations in 98 sign languages, featuring more than 1,500 individual signers. On this dataset, we report neural machine translation experiments. Apart from bilingual baseline systems, we also train multilingual systems, including some that take into account the typological relatedness of signed or spoken languages. Our experiments highlight that multilingual systems are superior to bilingual baselines, and that in higher-resource scenarios, clustering language pairs that are related improves translation quality.