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Constrained Language Models Yield Few-Shot Semantic Parsers ...
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Searching for More Efficient Dynamic Programs ...
Vieira, Tim; Cotterell, Ryan; Eisner, Jason. - : ETH Zurich, 2021
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Searching for More Efficient Dynamic Programs
In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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4
A Corpus for Large-Scale Phonetic Typology ...
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A Corpus for Large-Scale Phonetic Typology ...
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A corpus for large-scale phonetic typology
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7
A Corpus for Large-Scale Phonetic Typology
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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8
Contextualization of Morphological Inflection ...
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9
Are All Languages Equally Hard to Language-Model?
In: Proceedings of the Society for Computation in Linguistics (2019)
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10
A Generative Model for Punctuation in Dependency Trees
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 357-373 (2019) (2019)
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11
On the Complexity and Typology of Inflectional Morphological Systems
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 327-342 (2019) (2019)
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12
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model ...
Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018
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13
On the Complexity and Typology of Inflectional Morphological Systems ...
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14
Are All Languages Equally Hard to Language-Model? ...
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15
A Deep Generative Model of Vowel Formant Typology ...
Cotterell, Ryan; Eisner, Jason. - : arXiv, 2018
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16
Unsupervised Disambiguation of Syncretism in Inflected Lexicons ...
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17
Predicting Fine-Grained Syntactic Typology from Surface Features
In: Proceedings of the Society for Computation in Linguistics (2018)
Abstract: We show how to predict the basic word-order facts of a novel language given only a corpus of its part-of-speech (POS) sequences. We predict how often direct objects follow their verbs, how often adjectives follow their nouns, and in general the directionalities of all dependency relations. Although recovering syntactic structure is usually regarded as unsupervised learning, we train our predictor on languages of known structure. It outperforms the state-of-the-art unsupervised learning by a large margin, especially when we augment the training data with many synthetic languages. Full details can be found in http://www.cs.jhu.edu/~jason/papers/#wang-eisner-2017.
Keyword: Computational Linguistics; Supervised learning; Syntactic Typology; Synthetic data
URL: https://scholarworks.umass.edu/scil/vol1/iss1/39
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1018&context=scil
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18
Quantifying the Trade-off Between Two Types of Morphological Complexity
In: Proceedings of the Society for Computation in Linguistics (2018)
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19
Probabilistic Typology: Deep Generative Models of Vowel Inventories ...
Cotterell, Ryan; Eisner, Jason. - : arXiv, 2017
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20
Probabilistic Typology: Deep Generative Models of Vowel Inventories ...
Cotterell, Ryan; Eisner, Jason. - : Apollo - University of Cambridge Repository, 2017
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