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

## TL;DR

This paper introduces a deep learning-based two-stage method for generating natural and unambiguous spatial referring expressions in real-world environments, improving accuracy and user preference over existing algorithms.

## Contribution

It presents a novel two-stage deep learning approach for generating spatial referring expressions, addressing the gap in generation methods compared to comprehension-focused research.

## Key findings

- Generated expressions are ~30% more accurate according to user evaluation.
- Users prefer the generated expressions ~32% more often.
- Method outperforms state-of-the-art in ambiguous environments.

## Abstract

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 ($\sim$30% better) and would prefer to use ($\sim$32% more often).