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

Researchers present a framework for improving sign language recognition models by addressing spatial indexing—pointing gestures that assign discourse entities to spatial locations. Despite comprising 10-15% of signing content, current models trained on gloss-sequences poorly capture this non-lexical feature, and the new approach decomposes spatial reference resolution into detection and entity linking tasks to create index-aware models.

This research addresses a critical gap in sign language processing technology. Traditional sign language recognition models rely heavily on gloss-sequence or text supervision, which emphasizes lexical items while underrepresenting grammatical structures unique to signed languages. Spatial indexing represents a fundamental linguistic feature where signers assign discourse entities to specific spatial locations, then reference them through pointing—a productive construction that current architectures fail to model effectively.

The broader context reflects growing recognition that computational linguistics must move beyond word-centric approaches to capture non-lexical grammar. Sign language processing has lagged behind spoken language NLP partly because existing evaluation metrics and training objectives don't account for spatial grammar phenomena. This work establishes quantitative baselines showing indexing comprises 10-15% of signing content, yet remains poorly recovered by state-of-the-art models.

For AI developers building sign language technology, this framework provides concrete methodology for training indexing experts that can augment frozen sign language recognition models. The decomposition approach—separating index detection from discourse entity linking—offers a modular path toward better semantic understanding. Researchers and accessibility technology developers gain evaluation metrics and mention representations enabling automatic annotation of non-lexical structures.

Looking ahead, this research signals movement toward more linguistically sophisticated sign language models. Success in capturing spatial grammar could improve downstream applications including real-time translation, video understanding, and accessibility tools. The auxiliary expert approach suggests future models might similarly incorporate specialized components for other structural phenomena currently underrepresented in training objectives.

• →Spatial indexing comprises 10-15% of signing content but remains poorly captured by current sign language recognition models.
• →The framework decomposes spatial reference resolution into index detection and discourse entity linking for better non-lexical structure modeling.
• →Mention representations enable automatic annotation and can augment frozen models at inference time without retraining.
• →Current gloss-sequence and text supervision approaches inadequately capture productive, non-lexical sign language constructions.
• →Index-aware modeling advances accessibility technology and sign language computational linguistics beyond lexicon-centric approaches.