| ROA: | 156 |
|---|---|
| Title: | Learnability in Optimality Theory (long version) |
| Authors: | Bruce Tesar, Paul Smolensky |
| Comment: | 68 pages (single spaced) |
| Length: | 68 |
| Abstract: | Bruce Tesar The Center for Cognitive Science / Linguistics Department Rutgers University and Paul Smolensky Cognitive Science Department, Johns Hopkins University Abstract for the short version: 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. This longer version includes two types of additional material. The Recursive Constraint Demotion learning algorithm and a general family of Constraint Demotion algorithms is developed; analytical results are given concerning convergence, correctness, and efficiency. The longer version also provides some additional discussion of a number of general issues associated with extending the analysis to primary learning data consisting solely of overt elements. Topics include a general class of iterative solutions to simultaneously learning a grammar and learning to assign hidden structure to primary data, a simple illustration of the operation of our proposed iterative solution to this problem in the OT case, the initial state and universal constraint rankings, and acquisition of underlying forms. This work illustrates how the constraint ranking defining an OT grammar can, in principle, be used in optimization in multiple ways: for relating underlying forms to surface forms, for relating overt forms to their structural descriptions, and for deriving new underlying forms from overt data. |
| Type: | Paper/tech report |
| Area/Keywords: | |
| Article: | Version 1 |