Bruce Tesar

The Center for Cognitive Science / Linguistics Department
 Rutgers University



and



Paul Smolensky

Cognitive Science Department, Johns Hopkins University
 



A central claim of Optimality Theory is that grammars may differ only in
 how conflicts among universal well-formedness constraints are resolved:
 a grammar is precisely a means of resolving such conflicts via a strict
 priority ranking of constraints.  It is shown here how this theory of
 Universal Grammar 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. The learning problem is decomposed and formal results are
 presented for a central subproblem, deducing the constraint ranking
 particular to a target language, given structural descriptions of
 positive examples and knowledge of universal grammatical elements.
 Despite the potentially large size of the space of possible grammars,
 the structure imposed on this space by Optimality Theory allows
 efficient convergence to a correct grammar.  Implications are discussed
 for learning from overt data only, learnability of partially-ranked
 constraint hierarchies, and the initial state.  It is argued that
 Optimality Theory promotes a goal which, while generally desired, has
 been surprising elusive: confluence of the demands of more effective
 learnability and deeper linguistic explanation.