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1
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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2
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset ...
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3
UniMorph 3.0: Universal Morphology
In: Proceedings of the 12th Language Resources and Evaluation Conference (2020)
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4
UniMorph 3.0: Universal Morphology ...
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5
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ...
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6
On the Complexity and Typology of Inflectional Morphological Systems
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 327-342 (2019) (2019)
Abstract: We quantify the linguistic complexity of different languages’ morphological systems. We verify that there is a statistically significant empirical trade-off between paradigm size and irregularity: A language’s inflectional paradigms may be either large in size or highly irregular, but never both. We define a new measure of paradigm irregularity based on the conditional entropy of the surface realization of a paradigm— how hard it is to jointly predict all the word forms in a paradigm from the lemma. We estimate irregularity by training a predictive model. Our measurements are taken on large morphological paradigms from 36 typologically diverse languages.
Keyword: Computational linguistics. Natural language processing; P98-98.5
URL: https://doaj.org/article/0fb649718b164ce0bb10d522426035cb
https://doi.org/10.1162/tacl_a_00271
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7
On the Complexity and Typology of Inflectional Morphological Systems ...
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8
Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate ...
Kirov, Christo; Cotterell, Ryan. - : arXiv, 2018
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9
Unsupervised Disambiguation of Syncretism in Inflected Lexicons ...
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10
Quantifying the Trade-off Between Two Types of Morphological Complexity
In: Proceedings of the Society for Computation in Linguistics (2018)
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11
Paradigm Completion for Derivational Morphology ...
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12
Grammatical Influences in a Bayesian Speech Production Framework
In: Kirov, Christo. (2014). Grammatical Influences in a Bayesian Speech Production Framework. Proceedings of the Cognitive Science Society, 36(36). Retrieved from: http://www.escholarship.org/uc/item/28w3h0jx (2014)
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13
A Bayesian Approach to Speech Production
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14
Bayesian Speech Production: Evidence from Latency and Hyperarticulation
In: Kirov, Christo; & Wilson, Colin. (2013). Bayesian Speech Production: Evidence from Latency and Hyperarticulation. Proceedings of the Cognitive Science Society, 35(35). Retrieved from: http://www.escholarship.org/uc/item/5296p4d1 (2013)
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15
Processing of nested and cross-serial dependencies: an automaton perspective on SRN behaviour
In: Connection science. - Abingdon, Oxfordshire : Taylor & Francis 24 (2012) 1, 1-24
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16
The Specificity of Online Variation in Speech Production
In: Kirov, Christo; & Wilson, Colin. (2012). The Specificity of Online Variation in Speech Production. Proceedings of the Cognitive Science Society, 34(34). Retrieved from: http://www.escholarship.org/uc/item/9mz1d1tx (2012)
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