When phonology guides learning

Learning English Metrical Phonology: When Probability Distributions Are Not Enough

Language involves knowledge of multiple complex linguistic systems, such as phonology, morphology, and syntax. For each system, the process of acquisition requires children to discover the underlying components of that system, which native speakers use to generate the observable data. Theoretical research traditionally provides a description of the knowledge to be acquired, identifying what chi...

Learning and learnability in phonology

A central scientific problem in phonology is how children rapidly and accurately acquire the intricate structures and patterns seen in the phonology of their native language. The solution to this problem lies in part in the discovery of the right formal theory of phonology, but another crucial element is the development of theories of learning, often in the form of machineimplemented models tha...

Expectation Driven Learning of Phonology

Expectation Driven Learning of Phonology Gaja Jarosz Abstract This paper develops a novel approach to learning phonological hidden structure relying on underexplored expectation driven learning strategies rooted in the machine learning literature. A novel probabilistic grammatical representation that enables estimation of expectation driven learning updates, and two learning algorithms utilizin...

When Unbiased Probabilistic Learning Is Not Enough: Acquiring a Parametric System of Metrical Phonology

Structure and substance in artificial-phonology learning

2 Formal complexity 3 2.1 Number of relevant features . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Relations between features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Featural agreement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.2 Contiguity-similarity tradeoff . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Summary: Complexity . . . ....