arXiv:2606.08056v1 Announce Type: cross Abstract: Sign language models are predominantly trained with gloss-sequence or text supervision, thereby under-modeling non-lexical and productive constructions. One comparatively tractable instance is spatial indexing: pointing gestures that assign discourse entities to spatial loci for subsequent co-reference, which lexicon-centric objectives largely fail to capture. We present a targeted evaluation of indexing in Sign Language Recognition, showing that despite comprising 10-15% of signing content, indexing is poorly recovered. We introduce a framewor
The increasing sophistication of AI models and the focus on multimodal understanding allows for more nuanced approaches to historically challenging linguistic problems like sign language processing.
This research addresses a fundamental limitation in current sign language AI, hindering true conversational AI for deaf communities and limiting accessibility.
Current AI models, which primarily rely on gloss-sequence or text, will need to evolve to better incorporate and understand spatial grammar inherent in sign languages.
- · AI developers focused on multimodal understanding
- · Deaf and hard-of-hearing communities
- · Accessibility technology companies
- · Researchers in linguistic AI
- · AI models reliant solely on lexical representations of sign language
Improved accuracy and fluency in sign language recognition and generation AI systems.
Broader integration of sign language AI into daily applications, enhancing communication and inclusion.
Potential for new forms of human-computer interaction based on spatial and gestural input, extending beyond sign language.
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Read at arXiv cs.AI