Modeling language contact with the Iterated Learning Model

TL;DR

This paper uses an iterated learning model to explore how languages resist change during contact, showing that core traits can be maintained even when languages mix, despite simplified assumptions.

Contribution

It introduces the Semi-Supervised ILM to simulate language contact and demonstrates that expressive and compositional traits are resilient during language mixing.

Findings

Languages tend to maintain core traits after contact.

Expressive and compositional features arise spontaneously.

Resilience of language features despite simplified contact models.

Abstract

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.