|Title:||An Iterative Strategy for Learning Metrical Stress in Optimality Theory|
|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.