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Learnability in Optimality Theory (1995)
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| Citations: | 522 - 34 self |
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@TECHREPORT{Tesar95learnabilityin,
author = {Bruce Tesar and Paul Smolensky},
title = {Learnability in Optimality Theory},
institution = {},
year = {1995}
}
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Abstract
In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given grammatical module. We decompose the learning problem and present formal results for a central subproblem, deducing the constraint ranking particular to a target language, given structural descriptions of positive examples. The structure imposed on the space of possible grammars by Optimality Theory allows efficient convergence to a correct grammar. We discuss implications for learning from overt data only, as well as other learning issues. We argue that Optimality Theory promotes confluence of the demands of more effective learnability and deeper linguistic explanation.
Keyphrases
optimality theory target language overt data possible grammar effective learnability linguistic explanation substantive constraint general constraint demotion principle grammar learning learning procedure positive example central subproblem grammatical structure correct grammar learning problem grammatical module efficient convergence present formal result structural description
