Biases in Harmonic Grammar: the road to restrictive learning

In the Optimality-Theoretic learnability and acquisition literature it has been proposed that certain classes of constraints must be biased toward particular rankings (e.g., Markedness >> IO-Faithfulness; Specific IO-Faithfulness >> General IO-Faithfulness). While sometimes difficult to implement efficiently or comprehensively, these biases are necessary to explain how learners acquire the most restrictive grammar consistent with positive evidence from the target language, and how innovative patterns emerge during the course of child phonological development. This paper demonstrates that altering the mode of constraint interaction from strict ranking as in Optimality Theory to additive weighting as in Harmonic Grammar (HG) reduces the number of classes of constraints that must be distinguished by such biases. Using weighted constraints and a version of the Gradual Learning Algorithm (GLA), the only distinction needed is between Output-based constraints, which must be biased toward high weights, and InputOutput-based constraints, which must be biased toward the lowest weights possible. We implement this distinction within the HG-GLA model by assigning different initial weights and plasticity values to the two classes of constraints. This implementation suffices to ensure that restrictive grammars are learned, and also predicts the emergence of a variety of attested intermediate stages during the course of acquisition.