# Compositional properties of emergent languages in deep learning

arXiv: https://arxiv.org/abs/2001.08618 · HTML: https://mlsanity.com/paper/2001.08618/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

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

- [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024)
- [PatchGame: Learning to Signal Mid-level Patches in Referential Games](https://mlsanity.com/paper/2111.01785/) (2021)
- [ELCC: the Emergent Language Corpus Collection](https://mlsanity.com/paper/2407.04158/) (2024)
- [Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling](https://mlsanity.com/paper/2103.06198/) (2021)

## Abstract (arXiv)

Recent findings in multi-agent deep learning systems point towards the emergence of compositional languages. These claims are often made without exact analysis or testing of the language. In this work, we analyze the emergent language resulting from two different cooperative multi-agent game with more exact measures for compositionality. Our findings suggest that solutions found by deep learning models are often lacking the ability to reason on an abstract level therefore failing to generalize the learned knowledge to out of the training distribution examples. Strategies for testing compositional capacities and emergence of human-level concepts are discussed.