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• W2520615295 abstract "In the study of acquisition and learnability in Optimality Theory (OT; Prince and Smolensky 1993/2004) learning is characterized in terms of changes in constraint ranking. Learners begin with a ranking of Markedness constraints above Faithfulness constraints, and rerank them on the basis of evidence from the target language. A theory of learnability that accounts for the human acquisition process should both converge on the correct final grammar, and model the path that learners take to get there. The Gradual Learning Algorithm (GLA; Boersma 1998, Boersma and Hayes 2001) can model some, but not all aspects of the learning path, and as we will show, is non-convergent. The Constraint Demotion Algorithm (Tesar and Smolensky 1998) is convergent, but non-gradual. In this paper we argue that the search for a gradual convergent learner may be aided by replacing Optimality Theory’s constraint ranking with numerical weighting, returning in this respect to OT's predecessor Harmonic Grammar (HG; Legendre et al. 1990, Smolensky and Legendre 2006). We demonstrate the advantages of weighting by using a minimally modified version of the GLA implemented by Boersma and Weenink (2006) that learns Harmonic Grammars, rather than OT grammars. In the next section, we provide examples of two types of gradualness in the first language acquisition of phonology, before moving on to the question of how this gradualness can be modeled in learnability theory." @default.
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• W2520615295 date "2007-01-01" @default.
• W2520615295 modified "2023-09-26" @default.
• W2520615295 title "Phonological Acquisition as WeightedConstraint Interaction" @default.
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