Learning opacity in Stratal Maximum Entropy Grammar
This addresses the challenge of learning opaque phonological patterns for linguists and computational modelers, representing an incremental advance in grammatical theory implementation.
The paper tackles the problem of learning opaque phonological patterns by implementing a Maximum Entropy version of Stratal OT and testing it on French and Canadian English cases. It finds that the difficulty of opacity is influenced by evidence for stratal affiliation, such as easier learning in Canadian English when encountering specific vowel raising contexts.
Opaque phonological patterns are sometimes claimed to be difficult to learn; specific hypotheses have been advanced about the relative difficulty of particular kinds of opaque processes (Kiparsky 1971, 1973), and the kind of data that will be helpful in learning an opaque pattern (Kiparsky 2000). In this paper, we present a computationally implemented learning theory for one grammatical theory of opacity: a Maximum Entropy version of Stratal OT (Bermúdez-Otero 1999, Kiparsky 2000), and test it on simplified versions of opaque French tense-lax vowel alternations and the opaque interaction of diphthong raising and flapping in Canadian English. We find that the difficulty of opacity can be influenced by evidence for stratal affiliation: the Canadian English case is easier if the learner encounters application of raising outside the flapping context, or non-application of raising between words (i.e., <life> with a raised vowel; <lie for> with a non-raised vowel).