Jarosz, Gaja. To appear / 2019. Computational Modeling of Phonological Learning. To appear in Annual Review of Linguistics (5).
Recent advances in computational modeling have led to significant discoveries about the representation and acquisition of phonological knowledge and the limits on language learning and variation. These discoveries are the result of applying computational learning models to increasingly rich and complex natural language data while making increasingly realistic assumptions about the learning task. This article reviews the recent developments in computational modeling that have made the connections between fully explicit theories of learning, naturally occurring corpus data, and the richness of psycholinguistic and typological data possible. These advances fall into two broad research threads 1) the development of models capable of learning the quantitative, noisy, and inconsistent patterns that are characteristic of naturalistic data, and 2) the development of models with the capacity to learn hidden phonological structure from unlabeled data. After reviewing these advances, the article summarizes some of the most significant discoveries they have led to.