The Gradual Learning Algorithm (Boersma 1997) is a constraint ranking algorithm for learning Optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion algorithm of Tesar and Smolensky (1993, 1996, 1998, 2000), which initiated the learnability research program for Optimality Theory. We argue that the Gradual Learning Algorithm has a number of special advantages: it can learn free variation, deal effectively with noisy learning data, and account for gradient wellformedness judgments. The case studies we examine involve Ilokano reduplication and metathesis, Finnish genitive plurals, and the distribution of English light and dark /l/.

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The proposition that the mental lexicon is a `dual route' system, advanced by Pinker and others to account for regular and irregular morphology, overlooks the important fact that morphological regularity correlates inversely with phonological regularity --`regular' past-tense beeped being phonologically irregular (exceptional syllable), while `irregular' past-tense kept is phonologically just regular. I argue that the correlation, which is general, can only be captured under a single-rather than `dual'- architecture, and an associational-rather than rule based- theory of morphology. Where morphological associations are strong, morphology looks regular and phonological alternations are inhibited, making phonology look irregular. In a system in which regularities are attributed to `rules', rules should be able to coexist with other rules, and morphological and phonological regularities should correlate directly, rather than inversely. 1.

Phonological opacity and paradigmatic effects (“synchronic analogy”) have long been of interest in relation to change, naturalness, and the phonology/morphology interface. Their investigation has now acquired a new urgency, because they call into question OT’s postulate that constraints are evaluated in parallel. Conceptually, parallelism is one of the basic and most interesting tenets of OT, and so there are good methodological reasons to try hard to save it in the face of such recalcitrant data. The price to be paid for it is the introduction of otherwise unneeded powerful new types of Faithfulness constraints, such as Output/Output (O/O) constraints, Paradigm Uniformity constraints, and Sympathy constraints, which have turned out to compromise the OT program very severely. The alternative to this approach is to abandon full parallelism in favor of stratified constraint systems. This has the compensating advantage of maintaining a restrictive and well-defined constraint inventory, as originally envisaged in OT. More importantly, it achieves some genuine explanations by relating the stratification motivated by opacity and cyclicity to the intrinsic morphological and

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We develop an extensible description logic for stating the content of optimalitytheoretic constraints in phonology, and specify a class of structures for interpreting it. The aim is a transparent formalisation of OT. We show how to state a wide range of constraints, including markedness, input–output faithfulness and base–reduplicant faithfulness. However, output–output correspondence and ‘intercandidate’ sympathy are revealed to be problematic: it is unclear that any reasonable class of structures can reconstruct their proponents’ intentions. But our contribution is positive. Proponents of both output–output correspondence and sympathy have offered alternatives that fit into the general OT picture. We show how to state these in a reasonable extension of our formalism. The problematic constraint types were developed to deal with opaque phenomena. We hope to shed new light on the debate about how to handle opacity, by subjecting some common responses to it within OT to critical investigation.

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One of the many challenges to be faced in explaining language learning is the interdependence of the phonological mapping and the phonological underlying forms for morphemes (Albright and Hayes 2002; Hale and Reiss 1997; Tesar and Smolensky 2000; Tesar et al. 2003). The learner must attempt to infer both simultaneously, based on the surface forms of a language.