| ROA: | 155 |
|---|---|
| Title: | Learnability in Optimality Theory (short version) |
| Authors: | Bruce Tesar, Paul Smolensky |
| Comment: | 62 pages (double-spaced) |
| Length: | 62 |
| Abstract: | 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. |
| Type: | Paper/tech report |
| Area/Keywords: | |
| Article: | Version 1 |