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.Keywords: Optimality Theory, learning, acquisition, computational linguistics How exactly does a theory of grammar bear on questions of learnability? Restrictions on what counts as a possible human language can restrict the learner's search space. But this is a coarse observation: alone it says nothing about how data may be brought to bear on the problem, and further, the number of possible languages predicted by most linguistic theories is extremely large.
1It would clearly be a desirable result if the nature of the restrictions imposed by a theory of grammar could contribute further to language learnability.The central claim of this article is that the character of the restrictions imposed by Optimality Theory Smolensky 1991, 1993) have demonstrable and significant consequences for central questions of learnability. Optimality Theory explains linguistic phenomena through the complex interaction of violable constraints. The main results of this article demonstrate that those constraint interactions are nevertheless restricted in a way that permits the correct grammar to be inferred from grammatical structural descriptions. These results are theorems, based on a formalWe are greatly indebted to Alan Prince, whose challenges, insights, and suggestions have improved nearly every