A prompting method that links signs to short text descriptions lets large language models translate English and Italian into sign language glosses, beating prior models in low-data settings.
SignBank+: Preparing a Multilingual Sign Language Dataset for Machine Translation Using Large Language Models
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We introduce SignBank+, a clean version of the SignBank dataset, optimized for machine translation between spoken language text and SignWriting, a phonetic sign language writing system. In addition to previous work that employs complex factorization techniques to enable translation between text and SignWriting, we show that a traditional text-to-text translation approach performs equally effectively on the cleaned SignBank+ dataset. Our evaluation results indicate that models trained on SignBank+ surpass those on the original dataset, establishing a new benchmark for SignWriting-based sign language translation and providing an open resource for future research.
fields
cs.CL 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios
A prompting method that links signs to short text descriptions lets large language models translate English and Italian into sign language glosses, beating prior models in low-data settings.