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.

翻译：语言接触具有传递词汇及其他语言特征的潜力，但这并非必然发生。本文采用迭代学习模型，以简化的方式探究语言在接触过程中对变化的抵抗性。迭代学习模型是基于智能体的语言演化模型，其研究表明：表达性强且具有组合性的语言会因语言传递瓶颈而自发形成。本文采用近期提出的一种半监督迭代学习模型来模拟语言接触。这些模拟虽未涵盖语言接触涉及的诸多复杂因素，也未对说话者群体进行建模，但模型表明：促使语言在模型中自发形成表达性与组合性的动态机制，同样会导致一种语言在与另一种语言混合后仍能保持其核心特征。