# Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments

arXiv: https://arxiv.org/abs/1904.07165 · HTML: https://mlsanity.com/paper/1904.07165/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction](https://mlsanity.com/paper/2104.01558/) (2021)
- [From Speech-to-Spatial: Grounding Utterances on A Live Shared View with Augmented Reality](https://mlsanity.com/paper/2602.03059/) (2026)

## Abstract (arXiv)

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the state of the art algorithm in ambiguous environments (e.g., environments that include very similar objects with similar relationships). We show that our method generates referring expressions that people find to be more accurate (~30% better) and would prefer to use (~32% more often).