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Input Strictly Local opaque maps

Published online by Cambridge University Press:  01 June 2018

Jane Chandlee*
Affiliation:
Haverford College
Jeffrey Heinz*
Affiliation:
Stony Brook University
Adam Jardine*
Affiliation:
Rutgers University

Abstract

This paper gives a computational characterisation of opaque interactions in phonology. Specifically, a range of opaque interactions are shown to be Input Strictly Local (ISL) maps (Chandlee 2014), which are string-to-string functions that determine output based only on contiguous sequences of input symbols. Examples from Baković’s (2007) extended typology of counterfeeding, counterbleeding, self-destructive feeding, non-gratuitous feeding and cross-derivational feeding, as well as a case of fed counterfeeding from Kavitskaya & Staroverov (2010), are all given ISL analyses, which show that these interactions can be computed based on sequences of bounded length in the input. It is discussed how ISL maps are restrictive in their typological predictions, have guaranteed learning results and known methods for generation and recognition, and thus compare favourably to rule-based and constraint-based approaches to these interactions.

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Articles
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

The authors would like to thank the following people for valuable feedback and discussion related to this work: Bill Idsardi, the three anonymous reviewers and audiences at GALANA 2015, GLOW 2015, UPenn, UC Berkeley, UCSD and the University of Chicago.

References

Albright, Adam & Fullwood, Michelle A. (eds.) (2015). Proceedings of the 2014 Meeting on Phonology. http://dx.doi.org/10.3765/amp.CrossRefGoogle Scholar
Albro, Daniel M. (2005). Studies in computational Optimality Theory, with special reference to the phonological system of Malagasy. PhD dissertation, University of California, Los Angeles.Google Scholar
Baković, Eric (2000). Harmony, dominance and control. PhD dissertation, Rutgers University.Google Scholar
Baković, Eric (2005). Antigemination, assimilation and the determination of identity. Phonology 22. 279315.CrossRefGoogle Scholar
Baković, Eric (2007). A revised typology of opaque generalisations. Phonology 24. 217259.CrossRefGoogle Scholar
Baković, Eric (2011). Opacity and ordering. In Goldsmith, John, Riggle, Jason & Yu, Alan (eds.) The handbook of phonological theory. 2nd edn. Malden, Mass. & Oxford: Wiley-Blackwell. 4067.CrossRefGoogle Scholar
Baković, Eric (2013). Blocking and complementarity in phonological theory. Sheffield & Bristol, Conn.: Equinox.Google Scholar
Bane, Max, Riggle, Jason & Sonderegger, Morgan (2010). The VC dimension of constraint-based grammars. Lingua 120. 11941208.CrossRefGoogle Scholar
Beesley, Kenneth R. & Karttunen, Lauri (2003). Finite state morphology. Stanford: CSLI.Google Scholar
Benua, Laura (1997). Transderivational identity: phonological relations between words. PhD dissertation, University of Massachusetts, Amherst.Google Scholar
Bermúdez-Otero, Ricardo (2018). Stratal Phonology. In Hannahs, S. J. & Bosch, Anna R. K. (eds.) The Routledge handbook of phonological theory. Abingdon & New York: Routledge. 100134.Google Scholar
Buccola, Brian (2013). On the expressivity of Optimality Theory versus ordered rewrite rules. In Morrill, Glyn & Nederhof, Mark-Jan (eds.) Formal grammar: Proceedings of the 17th and 18th International Conferences, FG 2012/2013. Berlin & Heidelberg : Springer. 142158.CrossRefGoogle Scholar
Chandlee, Jane (2014). Strictly local phonological processes. PhD dissertation, University of Delaware.Google Scholar
Chandlee, Jane, Athanasopoulou, Angeliki & Heinz, Jeffrey (2012). Evidence for classifying metathesis patterns as subsequential. WCCFL 29. 303309.Google Scholar
Chandlee, Jane, Eyraud, Rémi & Heinz, Jeffrey (2014). Learning Strictly Local subsequential functions. Transactions of the Association for Computational Linguistics 2. 491503.Google Scholar
Chandlee, Jane, Eyraud, Rémi & Heinz, Jeffrey (2015). Output strictly local functions. In Kuhlmann, Marco, Kanazawa, Makoto & Kobele, Gregory M. (eds.) Proceedings of the 14th Meeting on the Mathematics of Language. Association for Computational Linguistics. 112125.Google Scholar
Chandlee, Jane & Heinz, Jeffrey (2012). Bounded copying is subsequential: implications for metathesis and reduplication. In Proceedings of the 12th Meeting of the ACL Special Interest Group on Computational Morphology and Phonology, pages. Stroudsburg, PA: Association for Computational Linguistics. 4251.Google Scholar
Chandlee, Jane & Heinz, Jeffrey (2016). Computational phonology. In Aronoff, Mark (ed.) Oxford research encyclopedia of linguistics. Oxford: Oxford University Press. http://dx.doi.org/10.1093/acrefore/9780199384655.013.116.Google Scholar
Chandlee, Jane & Heinz, Jeffrey (2018). Strict locality and phonological maps. LI 49. 2360.Google Scholar
Chandlee, Jane, Jardine, Adam & Heinz, Jeffrey (2015). Learning repairs for marked structures. In Albright & Fullwood (2015). http://dx.doi.org/10.3765/amp.v2i0.3760.CrossRefGoogle Scholar
Chandlee, Jane & Lindell, Steven (to appear). A logical characterization of strictly local functions. In Heinz, Jeffrey (ed.) Doing computational phonology. Oxford: Oxford University Press.Google Scholar
Chomsky, Noam & Halle, Morris (1968). The sound pattern of English. New York: Harper & Row.Google Scholar
Eisner, Jason (1997). Efficient generation in primitive Optimality Theory. In Proceedings of the 35th Annual Meeting of the ACL and 8th Conference of the European Chapter of the Association for Computational Linguistics. Morristown, NJ: Association for Computational Linguistics. 313320.Google Scholar
Finley, Sara (2008). Formal and cognitive restrictions on vowel harmony. PhD dissertation, Johns Hopkins University.Google Scholar
Frank, Robert & Satta, Giorgio (1998). Optimality Theory and the generative complexity of constraint violability. Computational Linguistics 24. 307315.Google Scholar
Gainor, Brian, Lai, Regine & Heinz, Jeffrey (2012). Computational characterizations of vowel harmony patterns and pathologies. WCCFL 29. 6371.Google Scholar
Gerdemann, Dale & Hulden, Mans (2012). Practical finite state Optimality Theory. In Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing. Association for Computational Linguistics. 1019.Google Scholar
Gerdemann, Dale & van Noord, Gertjan (2000). Approximation and exactness in finite state optimality theory. In Eisner, Jason, Karttunen, Lauri & Thériault, Alain (eds.) Finite-state phonology: proceedings of the 5th Workshop of the ACL Special Interest Group in Computational Phonology (SIGPHON). Luxemburg. 3445.Google Scholar
Gildea, Daniel & Jurafsky, Daniel (1996). Learning bias and phonological-rule induction. Computational Linguistics 22. 497530.Google Scholar
Gold, E. Mark (1967). Language identification in the limit. Information and Control 10. 447474.CrossRefGoogle Scholar
Goldrick, Matthew (2000). Turbid output representations and the unity of opacity. NELS 30. 231245.Google Scholar
Hansson, Gunnar Ólafur (2007). Blocking effects in agreement by correspondence. LI 38. 395409.Google Scholar
Hayes, Bruce, Kirchner, Robert & Steriade, Donca (eds.) (2004). Phonetically based phonology. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Heinz, Jeffrey (2010). Learning long-distance phonotactics. LI 41. 623661.Google Scholar
Heinz, Jeffrey (2011a). Computational phonology. Part I: Foundations. Language and Linguistics Compass 5. 140152.CrossRefGoogle Scholar
Heinz, Jeffrey (2011b). Computational phonology. Part II: Grammars, learning, and the future. Language and Linguistics Compass 5. 153168.CrossRefGoogle Scholar
Heinz, Jeffrey & Lai, Regine (2013). Vowel harmony and subsequentiality. In Kornai, András & Kuhlmann, Marco (eds.) Proceedings of the 13th Meeting on the Mathematics of Language. Sofia: Association for Computational Linguistics. 5263.Google Scholar
Heinz, Jeffrey, Rawal, Chetan & Tanner, Herbert G. (2011). Tier-based strictly local constraints in phonology. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Vol. 2. Association for Computational Linguistics. 5864.Google Scholar
Heinz, Jeffrey & Riggle, Jason (2011). Learnability. In van Oostendorp, Marc, Ewen, Colin J., Hume, Elizabeth & Rice, Keren (eds.) The Blackwell companion to phonology. Malden, Mass.: Wiley-Blackwell. 5478.Google Scholar
Higuera, Colin de la (1997). Characteristic sets for polynomial grammatical inference. Machine Learning 27. 125138.CrossRefGoogle Scholar
Higuera, Colin de la (2010). Grammatical inference: learning automata and grammars. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Idsardi, William J. (1998). Tiberian Hebrew spirantization and phonological derivations. LI 29. 3773.Google Scholar
Idsardi, William J. (2000). Clarifying opacity. The Linguistic Review 17. 337350.CrossRefGoogle Scholar
Jäger, Gerhard (2002). Gradient constraints in finite state OT: the unidirectional and the bidirectional case. In Kaufmann, Ingrid & Stiebels, Barbara (eds.) More than words: a Festschrift for Dieter Wunderlich. Berlin: Akademie Verlag. 299325.Google Scholar
Jain, Sanjay, Osherson, Daniel, Royer, James S. & Sharma, Arun (1999). Systems that learn: an introduction to learning theory. 2nd edn. Cambridge, Mass.: MIT Press.Google Scholar
Jardine, Adam (2016). Computationally, tone is different. Phonology 33. 247283.CrossRefGoogle Scholar
Jardine, Adam, Chandlee, Jane, Eyraud, René & Heinz, Jeffrey (2014). Very efficient learning of structured classes of subsequential functions from positive data. In Clark, Alexander, Kanazawa, Makoto & Yoshinaka, Ryo (eds.) Proceedings of the 12th International Conference on Grammatical Inference. 94108.Google Scholar
Jarosz, Gaja (2014). Serial markedness reduction. In Kingston, John, Moore-Cantwell, Claire, Pater, Joe & Staubs, Robert (eds.) Proceedings of the 2013 Meeting on Phonology. http://dx.doi.org/10.3765/amp.v1i1.40.Google Scholar
Jarosz, Gaja (2016). Learning opaque and transparent interactions in Harmonic Serialism. In Ólafur Hansson, Gunnar, Farris-Trimble, Ashley, McMullin, Kevin & Pulleyblank, Douglas (eds.) Proceedings of the 2015 Annual Meeting on Phonology. http://dx.doi.org/10.3765/amp.v3i0.3671.Google Scholar
Johnson, C. Douglas (1972). Formal aspects of phonological description. The Hague & Paris: Mouton.Google Scholar
Johnson, Mark (1984). A discovery procedure for certain phonological rules. In Proceedings of the 10th International Conference on Computational Linguistics and the 22nd Annual Meeting of the ACL. 344347.Google Scholar
Kager, René (1999). Optimality Theory. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kaplan, Ronald & Kay, Martin (1994). Regular models of phonological rule systems. Computational Linguistics 20. 331378.Google Scholar
Karttunen, Lauri (1993). Finite-state constraints. In Goldsmith, John A. (ed.) The last phonological rule: reflections on constraints and derivations. Chicago: University of Chicago Press. 173194.Google Scholar
Karttunen, Lauri (1998). The proper treatment of optimality in computational phonology. In Karttunen, Lauri & Oflazer, Kemal (eds.) Proceedings of the International Workshop on Finite State Methods in Natural Language Processing. Ankara: Bilkent University. 112.Google Scholar
Kavitskaya, Darya & Staroverov, Peter (2010). When an interaction is both opaque and transparent: the paradox of fed counterfeeding. Phonology 27. 255288.CrossRefGoogle Scholar
Kawahara, Shigeto (2015). A catalogue of phonological opacity in Japanese. Reports of the Keio Institute of Cultural and Linguistic Studies 46. 145174.Google Scholar
Kearns, Michael & Vazirani, Umesh (1994). An introduction to computational learning theory. Cambridge, Mass.: MIT Press.Google Scholar
Kiparsky, Paul (1971). Historical linguistics. In Dingwall, William Orr (ed.) A survey of linguistic science. College Park: University of Maryland Linguistics Program. 576642.Google Scholar
Kiparsky, Paul (1973). Abstractness, opacity, and global rules. In Fujimura, Osamu (ed.) Three dimensions in linguistic theory. Tokyo: TEC. 5786.Google Scholar
Kiparsky, Paul (1998). Paradigm effects and opacity. Ms, Stanford University.Google Scholar
Kiparsky, Paul (2000). Opacity and cyclicity. The Linguistic Review 17. 351365.CrossRefGoogle Scholar
Legendre, Géraldine, Miyata, Yoshiro & Smolensky, Paul (1990). Harmonic Grammar: a formal multi-level connectionist theory of linguistic well-formedness: theoretical foundations. In Proceedings of the 12th Annual Conference of the Cognitive Science Society. Hillsdale: Erlbaum. 388395.Google Scholar
Łubowicz, Anna (2003). Contrast preservation in phonological mappings. PhD dissertation, University of Massachusetts, Amherst.Google Scholar
Lundskaer-Nielsen, Tom & Holmes, Philip (2011). Danish: an essential grammar. 2nd edn. London & New York: Routledge.Google Scholar
McCarthy, John J. (1999). Sympathy and phonological opacity. Phonology 16. 331399.CrossRefGoogle Scholar
McCarthy, John J. (2000). Harmonic serialism and parallelism. NELS 30. 501524.Google Scholar
McCarthy, John J. (2003). Comparative markedness. Theoretical Linguistics 29. 151.CrossRefGoogle Scholar
McCarthy, John J. (2007). Hidden generalizations: phonological opacity in Optimality Theory. Sheffield & Bristol, Conn.: Equinox.Google Scholar
McCarthy, John J. & Prince, Alan (1999). Faithfulness and identity in Prosodic Morphology. In Kager, René, van der Hulst, Harry & Zonneveld, Wim (eds.) The prosody–morphology interface. Cambridge: Cambridge University Press. 218309.CrossRefGoogle Scholar
McMullin, Kevin (2016). Tier-based locality in long-distance phonotactics: learnability and typology. PhD dissertation, University of British Columbia.Google Scholar
McMullin, Kevin & Ólafur Hansson, Gunnar (2015). Long-distance phonotactics as Tier-Based Strictly 2-Local languages. In Albright & Fullwood (2015). http://dx.doi.org/10.3765/amp.v2i0.3750.CrossRefGoogle Scholar
Magri, Giorgio (2013). HG has no computational advantages over OT: toward a new toolkit for computational OT. LI 44. 569609.Google Scholar
Mohri, Mehryar (1997). Finite-state transducers in language and speech processing. Computational Linguistics 23. 269311.Google Scholar
Nazarov, Aleksei & Pater, Joe (2017). Learning opacity in Stratal Maximum Entropy Grammar. Phonology 34. 299324.CrossRefGoogle Scholar
Odden, David (2005). Introducing phonology. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Oncina, José, García, Pedro & Vidal, Enrique (1993). Learning subsequential transducers for pattern recognition interpretation tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 15. 448458.CrossRefGoogle Scholar
Pater, Joe (2008). Gradual learning and convergence. LI 39. 334345.Google Scholar
Pater, Joe (2012). Serial Harmonic Grammar and Berber syllabification. In Borowsky, Toni, Kawahara, Shigeto, Shinya, Takahito & Sugahara, Mariko (eds.) Prosody matters: essays in honor of Elisabeth Selkirk. London: Equinox. 4372.Google Scholar
Potts, Christopher, Pater, Joe, Jesney, Karen, Bhatt, Rajesh & Becker, Michael (2010). Harmonic Grammar with linear programming: from linear systems to linguistic typology. Phonology 27. 77117.CrossRefGoogle Scholar
Prince, Alan & Smolensky, Paul (2004). Optimality Theory: constraint interaction in generative grammar. Malden, Mass. & Oxford: Blackwell.CrossRefGoogle Scholar
Riggle, Jason (2004). Generation, recognition, and learning in finite-state Optimality Theory. PhD dissertation, University of California, Los Angeles.Google Scholar
Rogers, James, Heinz, Jeffrey, Fero, Margaret, Hurst, Jeremy, Lambert, Dakotah & Wibel, Sean (2013). Cognitive and sub-regular complexity. In Morrill, Glyn & Nederhof, Mark-Jan (eds.) Formal grammar. Berlin & Heidelberg: Springer. 90108.CrossRefGoogle Scholar
Rogers, James & Pullum, Geoffrey K. (2011). Aural pattern recognition experiments and the subregular hierarchy. Journal of Logic, Language and Information 20. 329342.CrossRefGoogle Scholar
Sanders, Nathan (2003). Opacity and sound change in the Polish lexicon. PhD dissertation, University of California, Santa Cruz.Google Scholar
Smolensky, Paul (2006). Optimality in phonology II: harmonic completeness, local constraint conjunction, and feature domain markedness. In Smolensky, Paul & Legendre, Géraldine (eds.) The harmonic mind: from neural computation to optimality-theoretic grammar. Vol. 2: Linguistic and philosophical implications. Cambridge, Mass.: MIT Press. 27160.Google Scholar
Sprouse, Ronald L. (1997). A case for enriched inputs. Handout of paper presented at TREND (Trilateral Phonology Weekend) 3. Available as ROA-193 from the Rutgers Optimality Archive.Google Scholar
Tesar, Bruce (2014). Output-driven phonology: theory and learning. Cambridge: Cambridge University Press.Google Scholar
Tesar, Bruce & Smolensky, Paul (1996). Learnability in Optimality Theory (long version). Technical Report 96:3, Department of Cognitive Science, Johns Hopkins University.Google Scholar
Tesar, Bruce & Smolensky, Paul (1998). Learnability in Optimality Theory. LI 29. 229268.Google Scholar
Weigel, William F. (2005). Yowlumne in the twentieth century. PhD thesis, University of California, Berkeley.Google Scholar
Wilson, Colin (2000). Targeted constraints: an approach to contextual neutralization in Optimality Theory. PhD dissertation, Johns Hopkins University.Google Scholar
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