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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

REFERENCES

Bermúdez-Otero, Ricardo (1999). Constraint interaction in language change: quantity in English and Germanic. PhD dissertation, University of Manchester & University of Santiago de Compostela.Google Scholar
Bermúdez-Otero, Ricardo (2003). The acquisition of phonological opacity. In Spenader et al. (2003). 25–36.Google Scholar
Boersma, Paul (1998). Functional phonology: formalizing the interactions between articulatory and perceptual drives. PhD dissertation, University of Amsterdam.Google Scholar
Boersma, Paul & van Leussen, Jan-Willem (to appear). Efficient evaluation and learning in multilevel parallel constraint grammars. LI 48.Google Scholar
Byrd, Richard H., Lu, Peihuang, Nocedal, Jorge & Zhu, Ciyou (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing 16. 1190–1208.Google Scholar
Coetzee, Andries W. & Pater, Joe (2011). The place of variation in phonological theory. In Goldsmith et al. (2011). 401–434.Google Scholar
De Jong, Kenneth J. (2011). Flapping in American English. In van Oostendorp, Marc, Ewen, Colin J., Hume, Elizabeth & Rice, Keren (eds.) The Blackwell companion to phonology. Malden, Mass.: Wiley-Blackwell. 2711–2729.Google Scholar
Dinnsen, Daniel A. & Farris-Trimble, Ashley W. (2008). An opacity-tolerant conspiracy in phonological acquisition. Indiana University Working Papers in Linguistics 6. 99–118.Google Scholar
Eisenstat, Sarah (2009). Learning underlying forms with MaxEnt. MA thesis, Brown University.Google Scholar
Eychenne, Lucien (2014). Schwa and the loi de position in Southern French. Journal of French Language Studies 24. 223–253.Google Scholar
Goldsmith, John A., Riggle, Jason & Yu, Alan C. L. (eds.) (2011). The handbook of phonological theory. 2nd edn. Malden, Mass.: Wiley-Blackwell.Google Scholar
Goldwater, Sharon & Johnson, Mark (2003). Learning OT constraint rankings using a Maximum Entropy model. In Spenader et al. (2003). 111–120.Google Scholar
Hayes, Bruce & Wilson, Colin (2008). A maximum entropy model of phonotactics and phonotactic learning. LI 39. 379–440.Google Scholar
Hoerl, Arthur E. & Kennard, Robert W. (1970). Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12. 55–67.CrossRefGoogle Scholar
Idsardi, William J. (2000). Clarifying opacity. The Linguistic Review 17. 337–350.Google Scholar
Jarosz, Gaja (2006). Rich lexicons and restrictive grammars: maximum likelihood learning in Optimality Theory. PhD dissertation, Johns Hopkins University.Google Scholar
Jarosz, Gaja (2014). Serial markedness reduction. In John Kingston, Claire Moore-Cantwell, Joe Pater & Robert Staubs (eds.) Proceedings of the 2013 Meeting on Phonology. Available (May 2017) at http://journals.linguisticsociety.org/proceedings/index.php/amphonology/article/view/40.Google Scholar
Jarosz, Gaja (2015). Expectation driven learning of phonology. Ms, University of Massachusetts Amherst.Google Scholar
Jarosz, Gaja (2016). Learning opaque and transparent interactions in Harmonic Serialism. In Gunnar Ólafur Hansson, Ashley Farris-Trimble, Kevin McMullin & Douglas Pulleyblank (eds.) Proceedings of the 2015 Annual Meeting on Phonology. Available (May 2017) at http://journals.linguisticsociety.org/proceedings/index.php/amphonology/article/view/3671.Google Scholar
Johnson, Mark (2013). A gentle introduction to maximum entropy, log-linear, exponential, logistic, harmonic, Boltzmann, Markov Random Fields, Conditional Random Fields, etc., models. Slides of paper presented to the Macquarie University Machine Learning Reading Group. Available (May 2017) at http://web.science.mq.edu.au/~mjohnson/papers/Johnson12IntroMaxEnt.pdf.Google Scholar
Johnson, Mark, Pater, Joe, Staubs, Robert & Dupoux, Emmanuel (2015). Sign constraints on feature weights improve a joint model of word segmentation and phonology. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. 303–313.Google Scholar
Joos, Martin (1942). A phonological dilemma in Canadian English. Lg 18. 141–144.Google Scholar
Kaye, Jonathan (1990). What ever happened to Dialect B? In Mascaró, Joan & Nespor, Marina (eds.) Grammar in progress: GLOW essays for Henk van Riemsdijk. Dordrecht: Foris. 259–263.Google Scholar
Kim, Yun Jung (2012). Do learners prefer transparent rule ordering? An artificial language learning study. CLS 48:1. 375–386.Google Scholar
Kiparsky, Paul (1971). Historical linguistics. In Dingwall, William Orr (ed.) A survey of linguistic science. College Park: University of Maryland Linguistics Program. 576–642.Google Scholar
Kiparsky, Paul (1973). Abstractness, opacity, and global rules. In Fujimura, Osamu (ed.) Three dimensions in linguistic theory. Tokyo: TEC. 57–86.Google Scholar
Kiparsky, Paul (2000). Opacity and cyclicity. The Linguistic Review 17. 351–365.Google Scholar
Kullback, S. & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics 22. 79–86.Google Scholar
McCarthy, John J. (2007). Hidden generalizations: phonological opacity in Optimality Theory. Sheffield & Bristol, Conn.: Equinox.Google Scholar
McCarthy, John J. & Pater, Joe (eds.) (2016). Harmonic Grammar and Harmonic Serialism. London: Equinox.Google Scholar
Moreux, Bernard (1985). La ‘Loi de Position’ en français du Midi. I: Synchronie (Béarn). Cahiers de Grammaire 9. 45–138.Google Scholar
Moreux, Bernard (2006). Les voyelles moyennes en français du Midi: une tentative de synthèse en 1985. Cahiers de Grammaire 30. 307–317.Google Scholar
Odden, David (2011). Rules v. constraints. In Goldsmith et al. (2011). 1–39.Google Scholar
Pater, Joe (2014). Canadian raising with language-specific weighted constraints. Lg 90. 230–240.Google Scholar
Pater, Joe (2016). Universal Grammar with weighted constraints. In McCarthy & Pater (2016). 1–46.Google Scholar
Pater, Joe, Staubs, Robert, Jesney, Karen & Smith, Brian (2012). Learning probabilities over underlying representations. In Proceedings of the 12th Meeting of the Special Interest Group on Computational Morphology and Phonology. Montreal: Association for Computational Linguistics. 62–71. Available (May 2017) at www.aclweb.org/anthology/W12-2308.Google Scholar
R Core Team (2013). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org/.Google Scholar
Rizzolo, Olivier (2002). Du leurre phonétique des voyelles moyennes en français et du divorce entre licenciement et licenciement pour gouverner. PhD dissertation, University of Nice-Sophia Antipolis.Google Scholar
Selkirk, Elisabeth (1978). The French foot: on the status of ‘mute’ e. Studies in French Linguistics 1:2. 141–150.Google Scholar
Smolensky, Paul (1986). Information processing in dynamical systems: foundations of Harmony Theory. In Rumelhart, D. E., McClelland, J. L. & the PDP Research Group (eds.) Parallel Distributed Processing: explorations in the micro-structure of cognition. Vol. 1: Foundations. Cambridge, Mass.: MIT Press. 194–281.Google Scholar
Smolensky, Paul & Legendre, Géraldine (eds.) (2006). The harmonic mind: from neural computation to optimality-theoretic grammar. 2 vols. Cambridge, Mass.: MIT Press.Google Scholar
Spenader, Jennifer, Eriksson, Anders & Dahl, Östen (eds.) (2003). Variation within Optimality Theory: Proceedings of the Stockholm Workshop on ‘Variation within Optimality Theory’. Stockholm: Department of Linguistics, Stockholm University.Google Scholar
Staubs, Robert (2014a). Computational modeling of learning biases in stress typology. PhD dissertation, University of Massachusetts Amherst.Google Scholar
Staubs, Robert (2014b). Stratal MaxEnt Solver. Software package. Available (July 2017) at http://www.linguist.robertstaubs.org/HGR/serialMaxEnt.zip.Google Scholar
Staubs, Robert & Pater, Joe (2016). Learning serial constraint-based grammars. In McCarthy & Pater (2016). 369–388.Google Scholar
Tesar, Bruce & Smolensky, Paul (2000). Learnability in Optimality Theory. Cambridge, Mass.: MIT Press.Google Scholar