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1
Learning Stress Patterns with a Sequence-to-Sequence Neural Network
In: Proceedings of the Society for Computation in Linguistics (2022)
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2
Learning Repetition, but not Syllable Reversal
In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2020 Annual Meeting on Phonology ; 2377-3324 (2021)
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3
Learning syntacti parameter settings without triggers by assigning credit and blame
In: CLS 55, 2019 : proceedings of the fifty-fifth annual meeting of the Chicago Linguistic Society (2020), S. 337-350
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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4
French schwa and gradient cumulativity
In: Glossa: a journal of general linguistics; Vol 5, No 1 (2020); 24 ; 2397-1835 (2020)
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5
Assimilation triggers metathesis in Balantak: Implications for theories of possible repair in Optimality Theory
In: University of Massachusetts Occasional Papers in Linguistics (2020)
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6
*NC
In: North East Linguistics Society (2020)
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7
French schwa and gradient cumulativity
In: Joe Pater (2019)
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8
Learning Reduplication with a Neural Network without Explicit Variables
In: Joe Pater (2019)
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9
Phonological typology in Optimality Theory and Formal Language Theory: Goals and future directions
In: Joe Pater (2019)
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10
Learning syntactic parameters without triggers by assigning credit and blame
In: Joe Pater (2019)
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11
Generative linguistics and neural networks at 60: foundation, friction, and fusion
In: Joe Pater (2019)
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12
Preface: SCiL 2019 Editors’ Note
In: Proceedings of the Society for Computation in Linguistics (2019)
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13
Substance matters: A reply to Jardine 2016
In: Joe Pater (2018)
Abstract: Jardine (2016) claims that tonal phonology is more formally complex than the phonology of other segmental features, in that only tonal phonology goes beyond the class of Weakly Deterministic maps. He then goes on to argue that this formal distinction is superior to any available treatment in Optimality Theory. This reply points out that Jardine’s formal distinction conflates attested and unattested tonal patterns, which can be distinguished in Optimality Theory given a substantively defined constraint set.
Keyword: Computational Linguistics; Linguistics; Phonetics and Phonology; Typological Linguistics and Linguistic Diversity
URL: https://works.bepress.com/joe_pater/34
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14
Seq2Seq Models with Dropout can Learn Generalizable Reduplication
In: Joe Pater (2018)
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15
Preface: SCiL 2018 Editors’ Note
In: Proceedings of the Society for Computation in Linguistics (2018)
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16
Learning opacity in Stratal Maximum Entropy Grammar ...
Nazarov, Aleksei; Pater, Joe. - : arXiv, 2017
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17
The Oxford handbook of developmental linguistics
Lidz, Jeffrey (Herausgeber); Snyder, William (Herausgeber); Pater, Joe (Herausgeber). - Oxford : Oxford University Press, 2016
UB Frankfurt Linguistik
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18
The Oxford handbook of developmental linguistics
Lidz, Jeffrey; Snyder, William; Pater, Joe. - Oxford : Oxford University Press, 2016
MPI für Psycholinguistik
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19
Harmonic grammar and harmonic serialism
McCarthy, John J. (Herausgeber); Pater, Joe (Herausgeber). - Bristol, CT : Equinox Publishing, 2016
BLLDB
UB Frankfurt Linguistik
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20
Gradient Exceptionality in Maximum Entropy Grammar with Lexically Specific Constraints
BASE
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