Modeling Second Language Preposition Learning

Hundreds of millions of people learn a second language (L2). When learning a specific L2, there are common errors for native speakers of a given L1 language, suggesting specific effects of L1 on L2 learning. Nevertheless, language instruction materials are designed based only on L2. We develop a computational model that mimics the behavior of a nonnative speaker of a specific language to provide a deeper understanding of the problem of learning a second language. We use a Naive Bayes to model prepositional choices in English (L2) by native Mandarin (L1) speakers. Our results show that both correct and incorrect responses can be explained by the learner's L1 information. Moreover, our model predicts incorrect choices with no explicit training data of non-native mistakes. Our results thus provide a new medium to analyze and develop tools for L2 teaching.