| ROA: | 688 |
|---|---|
| Title: | Contenders and Learning |
| Authors: | Jason Riggle |
| Comment: | This paper appears in the Proceedings of the WCCFL XXIII. |
| Length: | 14 |
| Abstract: | In Optimality Theory (Prince and Smolensky, 1993), the effect of running an input form through the phonological grammar is typically illustrated with a tableau showing that a particular output is more harmonic than a set of alternative candidates. But could a finite set of losing candidates ever suffice to prove that one candidate was optimal among the infinite range of possible competitors? The answer is yes, if the candidates under consideration are the set of contenders: those that could win under some permutation of the constraints in the grammar. This is so because, as Samek-Lodovici and Prince (1999) have shown, if a candidate is more harmonic than all non-harmonically-bounded competitors (the contenders) it is more harmonic than all possible competitors. I present here an algorithm that takes an input and a set of constraints and generates the set of contenders. This algorithm brings together Ellison’s (1994) finite state approach to optimization and the inconsistency detecting capability of Tesar’s (1995) recursive constraint demotion algorithm thereby making it possible to generate the entire set of contenders in a single derivation without needing to perform factorially many checks of the range of possible constraint rankings. The ability to generate the contenders has important ramification s for learning in OT, for they are exactly the candidates whose failure the learner must account for. Access to the set of contenders allows significant improvements over error driven learning algorithms because the learner is able to compare an observed datum with all of the alternative contenders rather than just the single hypothetical candidate generated by the learner’s current ranking. In this sense, access to the contenders makes each datum maximally informative. Moreover, because the set of contenders does not depend on the learner’s current ranking hypothesis, the learner cannot get stuck in a loop and return to an incorrect hypothesis after it has been eliminated. With the contenders, learning can proceed by simply taking the union of the ranking entailments for observed data rather than by the piece by piece reranking of constraints. This makes it unnecessary to call on an independent mechanism to identify more ‘restrictive’ rankings in cases where multiple rerankings would derive the observed datum. |
| Type: | Paper/tech report |
| Area/Keywords: | Computation,Phonology,Learnability |
| Article: | Version 1 |