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DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation
Framework enables discourse-aware text to sign language gloss translation with spatial consistency
Apple researchers introduce DiscoSign, a computational framework for discourse-aware text to sign language gloss translation that addresses spatial coreference resolution, question-answer clauses, and concept-gloss consistency using a modular large language model approach.
The work establishes the first systematic framework for discourse-level text to sign language gloss translation with novel evaluation metrics designed to assess discourse coherence.
From the source
We introduce DiscoSign, a computational approach for discourse-aware text to sign language gloss translation grounded in linguistic research. We address three key phenomena within our modular Large Language Model (LLM)-based translation framework: (i) spatial coreference resolution, where entities maintain consistent spatial locations throughout discourse; (ii) Question-Answer Clauses (QACs), pseudocleft structures serving specific discourse functions; and (iii) concept-gloss consistency, ensuring stable mappings between English concepts and American Sign Language (ASL) signs.
machinelearning.apple.com
