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Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate ...
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144 |
Unsupervised Disambiguation of Syncretism in Inflected Lexicons ...
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145 |
A Discriminative Latent-Variable Model for Bilingual Lexicon Induction ...
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146 |
A Discriminative Latent-Variable Model for Bilingual Lexicon Induction ...
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147 |
A Discriminative Latent-Variable Model for Bilingual Lexicon Induction
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148 |
A Structured Variational Autoencoder for Contextual Morphological Inflection
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149 |
Quantifying the Trade-off Between Two Types of Morphological Complexity
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In: Proceedings of the Society for Computation in Linguistics (2018)
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151 |
Probabilistic Typology: Deep Generative Models of Vowel Inventories ...
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152 |
One-Shot Neural Cross-Lingual Transfer for Paradigm Completion ...
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153 |
Cross-lingual, Character-Level Neural Morphological Tagging ...
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154 |
Probabilistic Typology: Deep Generative Models of Vowel Inventories ...
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Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles ...
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158 |
Probabilistic Typology: Deep Generative Models of Vowel Inventories
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Eisner, Jason; Cotterell, Ryan. - : Association for Computational Linguistics, 2017. : Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017
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Abstract:
Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For ex- ample, all languages have vowels, while most—but not all—languages have an [u] sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a se- ries of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages.
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URL: https://doi.org/10.17863/CAM.41585 https://www.repository.cam.ac.uk/handle/1810/294479
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159 |
Weighting Finite-State Transductions With Neural Context
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Eisner, Jason; Cotterell, Ryan; Rastogi, Pushpendre. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
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A Joint Model of Orthography and Morphological Segmentation
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Cotterell, Ryan; Vieira, Tim; Schütze, Hinrich. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
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