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

arXiv: https://arxiv.org/abs/2606.08056 · HTML: https://mlsanity.com/paper/2606.08056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 21 of 21)

- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild](https://mlsanity.com/paper/2409.12259/) (2024)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining](https://mlsanity.com/paper/2307.14768/) (2023)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition](https://mlsanity.com/paper/2110.05382/) (2021)
- [SimulSLT: End-to-End Simultaneous Sign Language Translation](https://mlsanity.com/paper/2112.04228/) (2021)
- [C2RL: Content and Context Representation Learning for Gloss-free Sign Language Translation and Retrieval](https://mlsanity.com/paper/2408.09949/) (2024)
- [A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision](https://mlsanity.com/paper/2405.10266/) (2024)
- [SignRep: Enhancing Self-Supervised Sign Representations](https://mlsanity.com/paper/2503.08529/) (2025)
- [Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation](https://mlsanity.com/paper/2505.15438/) (2025)
- [Major Entity Identification: A Generalizable Alternative to Coreference Resolution](https://mlsanity.com/paper/2406.14654/) (2024)
- [Gloss-Free Sign Language Translation: An Unbiased Evaluation of Progress in the Field](https://mlsanity.com/paper/2603.13240/) (2026)
- [The Referential Reader: A Recurrent Entity Network for Anaphora Resolution](https://mlsanity.com/paper/1902.01541/) (2019)
- [Beyond Gloss: A Hand-Centric Framework for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2507.23575/) (2025)
- [3D-LEX v1.0: 3D Lexicons for American Sign Language and Sign Language of the Netherlands](https://mlsanity.com/paper/2409.01901/) (2024)
- [The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition](https://mlsanity.com/paper/2409.15284/) (2024)

## Built on by (most cited first, 1 of 1)

- [Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks](https://mlsanity.com/paper/2609.03734/) (2026)

## Abstract (arXiv)

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.