# Modeling language contact with the Iterated Learning Model

arXiv: https://arxiv.org/abs/2406.06878 · HTML: https://mlsanity.com/paper/2406.06878/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Spatial community structure impedes language amalgamation in a population-based iterated learning model](https://mlsanity.com/paper/2305.11962/) (2023)
- [An iterated learning model of language change that mixes supervised and unsupervised learning](https://mlsanity.com/paper/2405.20818/) (2024)

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

- [Image, Word and Thought: A More Challenging Language Task for the Iterated Learning Model](https://mlsanity.com/paper/2601.02911/) (2026)
- [Evolving language compositionality in a frequency-structured meaning space](https://mlsanity.com/paper/2607.29642/) (2026)

## Abstract (arXiv)

Contact between languages has the potential to transmit vocabulary and other language features; however, this does not always happen. Here, an iterated learning model is used to examine, in a simple way, the resistance of languages to change during language contact. Iterated learning models are agent-based models of language change, they demonstrate that languages that are expressive and compositional arise spontaneously as a consequence of a language transmission bottleneck. A recently introduced type of iterated learning model, the Semi-Supervised ILM is used to simulate language contact. These simulations do not include many of the complex factors involved in language contact and do not model a population of speakers; nonetheless the model demonstrates that the dynamics which lead languages in the model to spontaneously become expressive and compositional, also cause a language to maintain its core traits even after mixing with another language.