Learning with Violable Constraints

This chapter reviews major results as well as areas of ongoing research on computational models of learning with violable constraints, in the sense of Optimality Theory. The review encompasses learning in classic OT as well as learning in related frameworks that formalize constraint interaction as (probabilistic) weighting or ranking. The learning problem is decomposed into several subproblems, which are considered in turn. The discussion emphasizes the challenges posed by these various subproblems and the insights and differences of the proposed solutions. The chapter first discusses the narrow grammar- learning subproblem, the problem of learning rankings or weightings given data along with their full structural descriptions. Next, the discussion turns to several distinct approaches to the broader grammar-learning subproblem, which makes the more realistic assumption that hidden structure, such as prosodic structure or underlying representations, are not available to the learner and must themselves be learned. The chapter then discusses the subset problem and various approaches that have been proposed for learning restrictive constraint grammars. Finally, the chapter briefly reviews work on acquisition modeling, work that connects the predictions of computational models and the process by which children acquire language.