Proceedings of the Annual Meeting of the Cognitive Science Society

Neural language models are commonly used to study language processing in human speakers, and several studies trained such models on two languages to simulate bilingual speakers. Surprisingly, no work systematically evaluates different neural architectures on bilingual speakers’ data, despite the abundance of such studies in the monolingual domain. In this work, we take the first step in this direction. We train three neural architectures (SRN, LSTM, and Transformer) on Dutch and English data and evaluate them on two data sets from experimental studies. Our goal is to investigate which architectures can reproduce the cognate facilitation effect and grammaticality illusion observed in bilingual speakers. While all three architectures can correctly predict the cognate effect, only the SRN succeeds at the grammaticality illusion. We additionally show how the observed patterns change as a function of the models’ hidden layer size, a hyperparameter that we argue may be more important in bilingual models.