| ROA: | 746 |
| Title: | Comparing two Optimality-Theoretic Learning Algorithms for Latin stress |
| Authors: | Diana Apoussidou, Paul Boersma |
| Comment: | |
| Length: | 14 |
| Abstract: | This paper compares the performance of two formal Optimality-Theoretic learning algorithms in modelling the acquisition of Latin stress from overt language data: Error-Driven Constraint Demotion (EDCD; Tesar 1995) and the Gradual Learning Algorithm (GLA; Boersma 1997). We present computer simulations of learners who are trained on several kinds of overt Latin stress patterns: a case with main stress only, three cases with overtly available secondary stress, and a case in which the learners are free to invent their own secondary stress patterns. Several of these cases turn out to be learnable with the GLA, none with EDCD. |
| Type: | Paper/tech report |
| Area/Keywords: | Learnability, Language Acquisition |
| Article: | Version 1
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