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The Learnability of Metrical Phonology

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The Learnability of Metrical Phonology The Learnability of Metrical Phonology Published by LOT phone: +31 30 253 6006 Janskerkhof 13 fax: +31 30 253 6406 3512 BL Utrecht e-mail: [email protected] The Netherlands http://wwwlot.let.uu.nl/ Cover illustration by Diana Apoussidou ISBN 978-90-78328-18-6 NUR 632 Copyright © 2007: Diana Apoussidou. All rights reserved. THE LEARNABILITY OF METRICAL PHONOLOGY ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof.mr. P.F. van der Heijden ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Aula der Universiteit op dinsdag 9 januari 2007, te 14.00 uur door Diana Apoussidou geboren te Mönchengladbach, Duitsland Promotiecommissie Promotor: prof. dr. P.P.G. Boersma Overige commissieleden: prof. dr. H.M.G.M. Jacobs prof. dr. R.W.J. Kager dr. W. Kehrein dr. N.S.H. Smith prof. dr. P. Smolensky Faculteit der Geesteswetenschappen Acknowledgements I consider myself very lucky to have had the support when writing this thesis, whether directly or indirectly, from the following people: Essential to starting and finishing this work is Paul Boersma. I thank him for his support as much as for challenging me, and for enabling me to push the limits. I thank my reading committee Haike Jacobs, René Kager, Wolfgang Kehrein, Norval Smith, and Paul Smolensky for their valuable comments; especially Wolfgang and Norval for enduring tedious questions about phonology. My paranymphs Maren Pannemann and Petra Jongmans never failed in supporting me; they prevented me from going nuts in quite some moments of panic. I wouldn’t have known what to do without them, and my Dutch would be much worse if it weren’t for them. Furthermore, I was very happy to be part of the phonological reading groups in Utrecht and Leiden. It was always fun and helpful to discuss anything phono with Dafna Graf, Paula Fikkert, Nancy Kula, Claartje Levelt, Jeroen van der Weijer, Marc van Oostendorp, and all the other people who attended. Being part of the Sound Circle organizing committee with Ivana Brasileiro, Ania Kijak, and Shakuntala Mahanta was, and still is, great. The core of the IFA as I got to know it made me always feel welcome. Florien van Beinum, Jan van Dijk, Cecilia Odé, Louis Pols, Jeannette van der Stelt, Rob van Zon, David Weenink, Ton Wempe en Dirk Jaasma took more than good care of me, not to forget my fellow gratiën Irene Jacobi and Wieneke Wesseling. Being a phonologist among phoneticians is not so bad at all. In the bigger picture, I thank everyone at the ACLC for contributing to a stimulating work environment, and all AiOs for being an enjoyable group open for discussion. Being responsible for my beginning interest in science, I like to thank Janet Grijzenhout and Martin Krämer, who have always been there with good advise for me beyond Düsseldorf. There are many non-phonologist linguists I need to thank for a lot inside and outside linguistics: Martin Salzmann for having the first fun-nights out in Amsterdam; Berit Gehrke for many more fun-nights out and in; Mika Poss for being there in Boston; Silke Hamann for being a role model, and Robert Cloutier for his patience and help with my English. My fellow mappreviators need to be mentioned, too: Roland Pfau for being my other role model; Rachel Selbach and Karina Hof for brightening my day with their eloquence and quick cheer-ups; and Rafael Fischer for being a fun office mate and utterly cheek. Completely outside of linguistics, there are many more people who contributed in mastering my time as a PhD student. I thank my homies from Germany Yvonne Salcewics, Anja Ammenhäuser, Marie Reuter, and Wolfram Bernhardt for keeping the faith. Last but definitely not least, I thank Georgios, Annelie and Aliki for being there. The list is not exhaustive; many others have contributed in one way or the other to my work. However, I would like to leave it at that and let the interested reader go on to the main part of this book. Contents i 1 Introduction 1 2 Grammatical vs. lexical stress 2.1 Introduction 9 2.2 Typological grass roots 10 2.3 Grammatical stress systems 11 2.4 Lexical stress systems 13 2.5 Stress in Optimality Theory 14 2.6 Constraints for stress 18 2.6.1 Structural constraints for metrical phonology 19 2.6.2 Faithfulness constraints for lexical stresss 21 2.6.3 Lexical constraints for underlying stress 23 2.7 The form of overt forms 24 2.8 Summary 24 3 The learnability of hidden structure and the grammar 3.1 Introduction 25 3.2 Perception 27 3.3 Recognition and comprehension 29 3.4 Virtual production 31 3.4.1 Virtual production from the underlying form 32 3.4.2 Virtual production from meaning 33 3.5 Error detection 34 3.5.1 Error detection in virtual production from the underlying 34 form 3.5.2 Error detection in virtual production from meaning 36 3.6 The reranking strategies 37 3.6.1 Constraint Demotion 38 3.6.2 The Gradual Learning Algorithm 40 3.7 Learning from full information 44 3.8 Summary 46 4 The learnability of grammatical stress in Latin 4.1 Introduction 47 4.2 Latin main stress 49 4.2.1 Linguistic analyses of Latin stress 52 i ii Contents 4.2.2 Latin Stress in OT 53 4.2.2.1 Underlying forms 54 4.2.2.2 The candidate generator 54 4.2.2.3 The constraints 55 4.2.3 Assessment of Jacobs’ OT analysis of Latin stress 56 4.2.4 Assessment of the Tesar & Smolensky constraint set for 63 Latin stress 4.2.5 Assessment of a moraic-trochee analysis of Latin stress 64 4.3 The constraint sets 70 4.4 The training data 70 4.5 The acquisition processes 74 4.6 Results 76 4.6.1 Informed learning of primary stress in Latin 77 4.6.2 Learning hidden structure and primary stress in Latin 85 4.6.3 Learning hidden structure including secondary stress in 90 Latin 4.6.4 Conclusions 95 4.7 More learners, different results? 96 4.8 More learners, the same results 97 4.9 Secondary Stress in Latin? 100 4.9.1 Very weight-sensitive secondary stress 101 4.9.2 Weight-insensitive secondary stress in Latin? 102 4.9.3 Freely assignable secondary stress 103 4.9.4 Summary 104 4.10 Discussion 105 4.11 Conclusions 108 5 The learnability of grammatical stress and weight in Pintupi 5.1 Introduction 111 5.2 Pintupi stress 112 5.2.1 Pintupi stress: two possible OT accounts 113 5.2.2 Maintaining an analysis with TROCHAIC 119 5.2.3 The weight of CVC-syllables in Pintupi 120 5.3 Modelling Pintupi stress 123 5.3.1 Perceiving Pintupi stress 125 5.3.2 Virtually producing Pintupi stress 127 5.3.3 Learning with Constraint Demotion 128 5.3.4 Learning with the Gradual Learning Algorithm 130 ii Contents iii 5.4 Simulating the acquisition of Pintupi stress 131 5.4.1 The training data 132 5.4.2 The candidate generator 133 5.4.3 The constraints 133 5.5 Results for Pintupi stress 135 5.5.1 The linguist’s analysis 137 5.5.1.1 FTNONFIN learners 137 5.5.1.2 TROCHAIC learners 141 5.5.1.3 Summary 143 5.5.2 Moraic coda consonants 143 5.5.3 Moraic coda consonants in stressed syllables only 145 5.5.4 Final syllable extrametricality 146 5.5.5 Moraic codas and final syllable extrametricality 148 5.5.6 Summary 150 5.6 Generalizations to unheard forms 150 5.6.1 Generalizations to unattested forms 151 5.6.2 Generalizations to longer forms 152 5.6.3 Generalizations to other stress patterns 154 5.7 The control group: learning from polysyllabic forms 155 5.8 Discussion 156 5.9 Conclusions 158 6 The learnability of grammatical and lexical stress in Modern Greek 6.1 Introduction 159 6.2 Grammatical and lexical stress in Modern Greek 162 6.2.1 Analyzing grammatical stress in Modern Greek 165 6.2.2 Analyzing lexical stress in Modern Greek 166 6.3 The model for the learning of underlying forms 167 6.3.1 Lexical constraints 169 6.3.2 Recognition 171 6.3.3 Virtual production 172 6.4 Testing a two-way contrast 174 6.4.1 The training data 174 6.4.2 The pool of underlying forms to choose from: GEN 175 6.4.3 The constraint set 175 6.4.4 Results: the chosen underlying forms 176 6.5 Testing a three-way contrast: learning pre- and post-stressing 179 morphemes iii iv Contents 6.5.1 The training data 181 6.5.2 The pool of underlying forms to choose from: GEN 181 6.5.3 The constraint set 182 6.5.4 Results: the chosen underlying forms 184 6.6 Modelling comprehension: two levels of hidden structures 187 6.6.1 The training data 187 6.6.2 The pool of underlying forms to choose from: GEN 188 6.6.3 The constraint set 189 6.6.4 Results: the chosen underlying forms 189 6.7 Alternative approaches to the learning of underlying forms 193 6.7.1 Learning underlying forms with Constraint Demotion 193 6.7.2 Lexicon Optimization 194 6.7.3 Comparison to inconsistency detection and surgery 196 6.7.4 Considering multiple grammars 196 6.7.5 A note on Richness of the Base 197 6.8 Discussion 198 7 Conclusions 7.1 Learning hidden structures 201 7.2 Why there can be different grammars for the speakers of the 203 same language 7.3 The innateness of constraints 204 7.4 Constraint Demotion vs.
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