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.

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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Introduction Rene Kager's textbook is one of the first to cover Optimality Theory (OT), a declarative grammar framework that swiftly took over phonology after it was introduced by Prince, Smolensky, and McCarthy in 1993. OT reclaims traditional grammar's ability to express surface generalizations ("syllables have onsets," "no nasal+voiceless obstruent clusters"). Empirically, some surface generalizations are robust within a language, or---perhaps for functionalist reasons--- widespread across languages. Derivational theories were forced to posit diverse rules that rescued these robust generalizations from other phonological processes. An OT grammar avoids such "conspiracies" by stating the generalizations directly, as in TwoLevel Morphology (Koskenniemi, 1983) or Declarative Phonology (Bird, 1995). In OT, the processes that try but fail to disrupt a robust generalization are described not as rules (cf. Paradis (1988)), but as lower-ranked generalizations. Suc

In a series of papers, Petra Hendriks, Helen de Hoop and Henritte de Swart have applied optimality theory (OT) to semantics. These authors argue that there is a fundamental difference between the form of OT as used in phonology, morphology and syntax on the one hand and its form as used in semantics on the other hand. Whereas in the first case OT takes the point of view of the speaker, in the second case the point of view of the hearer is taken. The aim of this paper is to argue that the proper treatment of OT in natural language interpretation has to take both perspectives at the same time. A conceptual framework is established that realizes the integration of both perspectives. It will be argued that this framework captures the essence of the Gricean maxims and gives a precise explication of Atlas & Levinson`s (1981) idea of balancing between informativeness and efficiency in natural language processing. The ideas are then applied to resolve some puzzles in natural language interpret...

Overt or covert reference to the edges of constituents is a commonplace throughout phonology and morphology. Some examples include: •In English, Garawa, Indonesian and a number of other languages, the normal right-to-left

. Variation is controlled by the grammar, though indirectly: it follows automatically from the robustness requirement of learning. If every constraint in an Optimality-Theoretic grammar has a ranking value along a continuous scale, and the disharmony of a constraint at evaluation time is randomly distributed about this ranking value, the phenomenon of optionality in determining the winning candidate follows automatically from the finiteness of the difference between the ranking values of the relevant constraints. The degree of optionality is a descending function of this ranking difference. In the production grammar, the symmetrized Minimal Gradual Learning Algorithm will automatically cause the learner to copy the degrees of optionality from the language environment. In the perception grammar, even the slightest degree of randomness in constraint evaluation will automatically cause the learner to become a probability-matching listener, whose categorization distributions match the p...

T he distinction between marked and unmarked structures has played a role throughout this century in the development of phonology and of linguistics generally. Optimality Theory (Prince and Smolensky 1993) offers an approach to linguistic theory that aims to combine an empirically adequate theory of

This paper introduces primitive Optimality Theory (OTP), a linguistically motivated formalization of OT. OTP specifies the class of autosegmental representations, the universal generator Gen, and the two simple families of permissible constraints.

It has been argued that rule-based phonological descriptions can uniformly be expressed as map-pings carried out by finite-state transducers, and therefore fall within the class of rational relations. If this property of generative capacity is an empirically correct characterization of phonological mappings, it should hold of any sufficiently restrictive theory of phonology, whether it utilizes con-straints or rewrite rules. In this paper, we investigate the conditions under which the phonological descriptions that are possible within the view of constraint interaction embodied in Optimality Theory (Prince and Smolensky 1993) remain within the class of rational relations. We show that this is true when GEN is itself a rational relation, and each of the constraints distinguishes among finitely many regular sets of candidates. 1.