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Learning opacity in Stratal Maximum Entropy Grammar*

Published online by Cambridge University Press:  14 August 2017

Aleksei Nazarov  and
Joe Pater
Aleksei Nazarov*
Affiliation:
Harvard University
Joe Pater*
Affiliation:
University of Massachusetts Amherst
*
E-mail: [email protected], [email protected].
E-mail: [email protected], [email protected].
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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 is 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 (e.g. life with [ʌɪ]; lie for with [aɪ]).

Type
Articles
Information
Phonology , Volume 34 , Issue 2 , August 2017 , pp. 299 - 324
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

*

We would like to thank Ricardo Bermúdez-Otero, Paul Boersma, Jeroen Breteler, Ivy Hauser, Jeff Heinz, Coral Hughto, Gaja Jarosz, Marc van Oostendorp, Olivier Rizzolo, Klaas Seinhorst and Robert Staubs, as well as audiences at the 21st Manchester Phonology Meeting, the University of Massachusetts Amherst and the University of Amsterdam for their insightful feedback on this paper and for stimulating discussion. We also thank the editors of this volume and two anonymous reviewers for their very helpful and useful comments. We are grateful to the National Science Foundation for supporting this work through grants BCS-0813829 and BCS-1424077. All errors are ours.

References

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