Learnability in Optimality Theory

Abstract

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. 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. 1 It 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 (Prince and 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 formal We are greatly indebted to Alan Prince, whose challenges, insights, and suggestions have improved nearly every

Statistical learning of language: Theory, validity, and predictions of a statistical learning account of language acquisition

“…This is in contrast to many models of word segmentation that suggest that tracking of sequential probabilities arises from a completely different set of processes than learning of phonological regularities such as predominant lexical stress, phonology, or phonotactics. For example, in the StaGE model (Adriaans & Kager, 2010), learners identify words via calculation of transitional probabilities and then deduce phonological and phonotactic constraints via a hierarchical ranking of constraints similar to that of Optimality Theory (Tesar & Smolensky, 2000). Similarly, Mersad and Nazzi (2011) proposed that learners use a hierarchical ranking of phonological cues to parse the speech stream, relying on sequential statistics only in cases where these cues are uninformative.…”

Adding generalization to statistical learning: The induction of phonotactics from continuous speech

“…The constraint ranking mechanism in our model is also fundamentally different from mechanisms employed in earlier OT learners (in particular, the Constraint Demotion Algorithm; Tesar & Smolensky, 2000, and the Gradual Learning Algorithm; Boersma & Hayes, 2001). Rather than providing the learner with feedback about optimal forms, i.e.…”

Learning to reason about speakers’ alternatives in sentence comprehension: A computational account

“…If grammars are rankings of universal constraints, as is commonly assumed in OT, then acquiring a grammar must involve learning the adult constraint ranking (Boersma and Hayes, 2001;Tesar and Smolensky, 1998). In children who still entertain a non-adult constraint ranking, with one or more markedness constraints being ranked too high, the inherent asymmetry of the grammar may give rise to errors in production but at the same time result in adult-like performance in comprehension (Smolensky, 1996).…”