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 this paper shows that the GLA is non-convergent. The Constraint Demotion Algorithm (Tesar and Smolensky 1998) is convergent, but non-gradual. This paper argues 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). The advantages of weighting are demonstrated by using a minimally modified version of the GLA implemented by Boersma and Weenink (2006) that learns Harmonic Grammars, rather than OT grammars.
Proceedings of the 2nd Conference on Generative Approaches to Language Acquisition North America (GALANA)
edited by Alyona Belikova, Luisa Meroni, and Mari Umeda Table of contents
ISBN 978-1-57473-419-5 library binding
vii + 490 pages
publication date: 2007
published by Cascadilla Proceedings Project, Somerville, MA, USA