What's the Point? Spatial Grammar & Index Resolution for Sign Language Processing

Title:What's the Point? Spatial Grammar & Index Resolution for Sign Language Processing

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 framework for training and evaluating indexing experts, establishing a baseline for index-aware sign language modeling. Our approach decomposes spatial reference resolution into index detection and discourse entity linking. The resulting mention representations enable automatic annotation and non-lexical structure modeling, and serve as an auxiliary indexing expert that augments a frozen SLR model at inference time.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2606.08056 [cs.CL]

(or arXiv:2606.08056v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.08056 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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