# From Spatial Relations to Spatial Configurations

arXiv: https://arxiv.org/abs/2007.09557 · HTML: https://mlsanity.com/paper/2007.09557/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning](https://mlsanity.com/paper/2104.05832/) (2021)
- [VLN-Trans: Translator for the Vision and Language Navigation Agent](https://mlsanity.com/paper/2302.09230/) (2023)
- [Towards Navigation by Reasoning over Spatial Configurations](https://mlsanity.com/paper/2105.06839/) (2021)
- [LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation](https://mlsanity.com/paper/2209.12723/) (2022)

## Abstract (arXiv)

Spatial Reasoning from language is essential for natural language understanding. Supporting it requires a representation scheme that can capture spatial phenomena encountered in language as well as in images and videos. Existing spatial representations are not sufficient for describing spatial configurations used in complex tasks. This paper extends the capabilities of existing spatial representation languages and increases coverage of the semantic aspects that are needed to ground the spatial meaning of natural language text in the world. Our spatial relation language is able to represent a large, comprehensive set of spatial concepts crucial for reasoning and is designed to support the composition of static and dynamic spatial configurations. We integrate this language with the Abstract Meaning Representation(AMR) annotation schema and present a corpus annotated by this extended AMR. To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with the fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.