Learning opacity in Stratal Maximum Entropy Grammar

TL;DR

This paper develops a computational learning model for opacity in phonology using Stratal MaxEnt Grammar, demonstrating how evidence influences the difficulty of learning opaque patterns in French and Canadian English.

Contribution

It introduces a MaxEnt-based learning theory for opacity within Stratal OT and tests how different evidence affects learning difficulty.

Findings

Canadian English opacity is easier to learn with certain evidence.

Opacity difficulty varies with evidence of stratal affiliation.

The model predicts conditions that facilitate learning opaque patterns.

Abstract

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 (Berm\'udez-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).