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This work presents a systematic theoretical and empirical comparison of themajor algorithms that have been proposed for learning Harmonic and OptimalityTheory grammars (HG and OT, respectively). By comparing learning algorithms, weare also able to compare the closely related OT and HG frameworks themselves.Experimental results show that the additional expressivity of the HG frameworkover OT affords performance gains in the task of predicting the surface wordorder of Czech sentences. We compare the perceptron with the classic GradualLearning Algorithm (GLA), which learns OT grammars, as well as the popularMaximum Entropy model. In addition to showing that the perceptron istheoretically appealing, our work shows that the performance of the HG model itlearns approaches that of the upper bound in prediction accuracy on a held outtest set and that it is capable of accurately modeling observed variation.

Harmonic Grammar, Optimality Theory, and Syntax Learnability: An Empirical Exploration of Czech Word Order