Simulating Code-switching Using a Neural Network Model of Bilingual Sentence Production

Abstract

Code-switching is the alternation from one language to the other during bilingual speech. We present a novel method of researching this phenomenon using computational cognitive modeling. We trained a neural network of bilingual sentence production to simulate early balanced Spanish–English bilinguals, late speakers of English who have Spanish as a dominant native language, and late speakers of Spanish who have English as a dominant native language. The model produced code-switches even though it was not exposed to code-switched input. The simulations predicted how code-switching patterns differ between early balanced and late non-balanced bilinguals; the balanced bilingual simulation code-switches considerably more frequently, which is in line with what has been observed in human speech production. Additionally, we compared the patterns produced by the simulations with two corpora of spontaneous bilingual speech and identified noticeable commonalities and differences. To our knowledge, this is the first computational cognitive model simulating the code-switched production of non-balanced bilinguals and comparing the simulated production of balanced and non-balanced bilinguals with that of human bilinguals.