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Title:Learning with Hidden Structure in Optimality Theory and Harmonic Grammar: Beyond Robust Interpretive Parsing
Authors:Gaja Jarosz
Comment:Slightly revised version published 2013, Phonology 30(1), 27-71.
Length:36 pages
Abstract:This paper explores the relative merits of constraint ranking versus weighting in the context of a major outstanding learnability problem in phonology: learning in the face of hidden structure. Specifically, the paper examines a well-known approach to the structural ambiguity problem, Robust Interpretive Parsing (RIP; Tesar and Smolensky 1998), focusing on its stochastic extension as first described by Boersma (2003). Two related problems with the stochastic formulation of RIP are revealed, rooted in a failure to take full advantage of probabilistic information available in the learner’s grammar. To address these problems, two novel parsing strategies are introduced and applied to learning algorithms for both probabilistic ranking and weighting. The novel parsing strategies yield significant improvements in performance, asymmetrically improving performance of OT learners. Once RIP is replaced with the proposed modifications, the apparent advantage of HG over OT learners reported in previous work disappears (Boersma and Pater to appear).
Type:Paper/tech report
Area/Keywords:phonology, learnability, computation, hidden structure, metrical stress, optimality theory, harmonic grammar,
Article:Version 1