title: Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.
abstract: We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Lnlearning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Lnacquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Lnlearning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.
authors.affiliation: Duolingo, Inc.
authors.forename: Bozena
authors.initials: B
authors.lastname: Pajak
authors.affiliation: Hebrew University of Jerusalem.
authors.forename: Alex B
authors.initials: AB
authors.lastname: Fine
authors.affiliation: University of Rochester.
authors.forename: Dave F
authors.initials: DF
authors.lastname: Kleinschmidt
authors.affiliation: University of Rochester.
authors.forename: T Florian
authors.initials: TF
authors.lastname: Jaeger
keywords: hierarchical probabilistic inference; second language acquisition; speech adaptation; statistical learning