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Title:An Iterative Strategy for Learning Metrical Stress in Optimality Theory
Authors:Bruce Tesar
Abstract:One of the major challenges of language acquisition is the fact

that the auditory signal received by a child can underdetermine

the structural description of the utterance. This paper presents

an approach to the problem via an idea borrowed from

statistical learning theory, an approach that depends critically

upon the optimizing structure of Optimality Theory.

The learner starts with an initial hypothesized ranking of the

constraints. It uses that ranking to make a best guess at the full

structural description of an observed overt form. The learner

then treats this full description as correct, and uses it to

refine its ranking hypothesis. The full description and the

ranking are alternately refined, each with respect to the

other, in iterative fashion. Success is achieved when, despite

starting from an incorrect hypothesis ranking, the learner is

able to iteratively converge upon the correct ranking, and thus

also assign the correct descriptions to observed overt forms.

This paper presents some results of simulations applying this

approach to an OT system for metrical stress grammars, including

quantity sensitivity. The degree of success achieved by a simple

implementation of this strategy, which is applicable to OT systems

generally, is presented as evidence that the optimizing structure

inherent in OT has an important role to play in an overall account

of language learning.

This paper is to appear in the Proceedings of the 21st Annual

Boston University Conference on Language Development.
Type:Paper/tech report
Article:Version 1