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Academic Paper


Title: Convergence of error-driven ranking algorithms
Author: Giorgio Magri
Institution: Université Paris Diderot - Paris 7
Linguistic Field: Language Acquisition; Phonology
Abstract: According to the OT error-driven ranking model of language acquisition, the learner performs a sequence of slight re-rankings triggered by mistakes on the incoming stream of data, until it converges to a ranking that makes no more mistakes. Two classical examples are Tesar & Smolensky's (1998) Error-Driven Constraint Demotion (EDCD) and Boersma's (1998) Gradual Learning Algorithm (GLA). Yet EDCD only performs constraint demotion, and is thus shown to predict a ranking dynamics which is too simple from a modelling perspective. The GLA performs constraint promotion too, but has been shown not to converge. This paper develops a complete theory of convergence of error-driven ranking algorithms that perform both constraint demotion and promotion. In particular, it shows that convergent constraint promotion can be achieved (with an error-bound that compares well to that of EDCD) through a proper calibration of the amount by which constraints are promoted.

CUP at LINGUIST

This article appears in Phonology Vol. 29, Issue 2, which you can read on Cambridge's site or on LINGUIST .



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