| ROA: | 177 |
|---|---|
| Title: | An Iterative Strategy for Learning Metrical Stress in Optimality Theory |
| Authors: | Bruce Tesar |
| Comment: | |
| Length: | 13 |
| 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 |
| Area/Keywords: | |
| Article: | Version 1 |