In early stages of language acquisition, children often make inflectional errors on regular verbs, e.g., Spanish-speaking children produce –a (present-tense 3rd person singular) when other inflections are expected. Most previous models of morphology learning have focused on later stages of learning relating to the production of irregular verbs. We propose a computational model of Spanish inflection learning to examine the earlier stages of learning and present a novel data set of gold-standard inflectional annotations for Spanish verbs. Our model replicates data from Spanish-learning children, capturing the acquisition order of different inflections and correctly predicting the substitution errors they make. Analyses show that the learning trajectory can be explained as a result of the gradual acquisition of inflection-meaning associations. Ours is the first computational model to provide an explanation for this acquisition trajectory in Spanish, and represents a theoretical advance more generally in explaining substitution errors in early morphology learning.

Hundreds of millions of people learn a second language (L2).1When learning a specific L2, there are common errors for na-tive speakers of a given L1 language, suggesting specific ef-fects of L1 on L2 learning. Nevertheless, language instruc-tion materials are designed based only on L2. We developa computational model that mimics the behavior of a non-native speaker of a specific language to provide a deeper un-derstanding of the problem of learning a second language. Weuse a Naive Bayes to model prepositional choices in English(L2) by native Mandarin (L1) speakers. Our results show thatboth correct and incorrect responses can be explained by thelearner’s L1 information. Moreover, our model predicts incor-rect choices with no explicit training data of non-native mis-takes. Our results thus provide a new medium to analyze anddevelop tools for L2 teaching.

Children with developmental language disorder (DLD) regularly use the base form of verbs (e.g., dance) instead of inflected forms (e.g., danced). We propose an account of this behavior in which children with DLD have difficulty processing novel inflected verbs in their input. This leads the inflected form to face stronger competition from alternatives. Competition is resolved by the production of a more accessible alternative with high semantic overlap with the inflected form: in English, the bare form. We test our account computationally by training a nonparametric Bayesian model that infers the productivity of the inflectional suffix (-ed). We systematically vary the number of novel types of inflected verbs in the input to simulate the input as processed by children with and without DLD. Modeling results are consistent with our hypothesis, suggesting that children’s inconsistent use of inflectional morphemes could stem from inferences they make on the basis of impoverished data.