# Speaking Your Language: Spatial Relationships in Interpretable Emergent Communication

arXiv: https://arxiv.org/abs/2406.07277 · HTML: https://mlsanity.com/paper/2406.07277/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [Emergent Multi-Agent Communication in the Deep Learning Era](https://mlsanity.com/paper/2006.02419/) (2020)
- [Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols](https://mlsanity.com/paper/1705.11192/) (2017)
- [Compositionality and Generalization in Emergent Languages](https://mlsanity.com/paper/2004.09124/) (2020)
- [Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input](https://mlsanity.com/paper/1804.03984/) (2018)
- [Anti-efficient encoding in emergent communication](https://mlsanity.com/paper/1905.12561/) (2019)
- [EGG: a toolkit for research on Emergence of lanGuage in Games](https://mlsanity.com/paper/1907.00852/) (2019)
- [Emergent Communication of Generalizations](https://mlsanity.com/paper/2106.02668/) (2021)
- [Interpretable agent communication from scratch (with a generic visual processor emerging on the side)](https://mlsanity.com/paper/2106.04258/) (2021)
- ["LazImpa": Lazy and Impatient neural agents learn to communicate efficiently](https://mlsanity.com/paper/2010.01878/) (2020)
- [Measuring non-trivial compositionality in emergent communication](https://mlsanity.com/paper/2010.15058/) (2020)
- [Rat big, cat eaten! Ideas for a useful deep-agent protolanguage](https://mlsanity.com/paper/2003.11922/) (2020)
- [Neural networks can understand compositional functions that humans do not, in the context of emergent communication](https://mlsanity.com/paper/2103.04180/) (2021)

## Built on by (most cited first, 3 of 3)

- [From Grunts to Lexicons: Emergent Language from Cooperative Foraging](https://mlsanity.com/paper/2505.12872/) (2025)
- [Morpheme Induction for Emergent Language](https://mlsanity.com/paper/2510.03439/) (2025)
- [Emergent Language as an Approach to Conscious AI](https://mlsanity.com/paper/2606.06380/) (2026)

## Abstract (arXiv)

Effective communication requires the ability to refer to specific parts of an observation in relation to others. While emergent communication literature shows success in developing various language properties, no research has shown the emergence of such positional references. This paper demonstrates how agents can communicate about spatial relationships within their observations. The results indicate that agents can develop a language capable of expressing the relationships between parts of their observation, achieving over 90% accuracy when trained in a referential game which requires such communication. Using a collocation measure, we demonstrate how the agents create such references. This analysis suggests that agents use a mixture of non-compositional and compositional messages to convey spatial relationships. We also show that the emergent language is interpretable by humans. The translation accuracy is tested by communicating with the receiver agent, where the receiver achieves over 78% accuracy using parts of this lexicon, confirming that the interpretation of the emergent language was successful.