Department of Linguistics
Generalizing Phonological Hidden Structure
Language acquisition proceeds on the basis of incomplete, ambiguous linguistic input, and one source of this ambiguity is hidden phonological structure. Due to recent developments in computational modeling of phonological learning, there now exist numerous approaches for learning of various kinds of hidden phonological structure from incomplete, unlabeled, and noisy data. These computational models make it possible to connect the full representational richness of phonological theory with noisy, ambiguous corpus data representative of language learners’ linguistic experience to make detailed and experimentally testable predictions about language learning. In this talk, I first review these computational developments and then discuss a couple of ongoing projects that utilize these mutually-informing connections between computation, phonological theory, and experimental data to test hypotheses about the abstract representations that underlie phonological knowledge. These projects seek to bring new sources of evidence to bear on long-standing theoretical debates by differentiating and testing the predictions for learning of distinct theoretical proposals. One case study focuses on phonological exceptionality, and the other on learning of opaque (and transparent) phonological generalizations.
Snacks will be provided.