Generalizing Phonological (Hidden) Structure (USC Talk & Minicourse)
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 and generalization. In this talk, I briefly review these computational developments and then discuss some 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. The ongoing projects include learning of phonological exceptionality, metrical structure, abstract lexical representations, and opaque (and transparent) phonological generalizations.