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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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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
Abstract: We show how the spellings of known words can help us deal with unknown words in open-vocabulary NLP tasks. The method we propose can be used to extend any closed-vocabulary generative model, but in this paper we specifically consider the case of neural language modeling. Our Bayesian generative story combines a standard RNN language model (generating the word tokens in each sentence) with an RNN-based spelling model (generating the letters in each word type). These two RNNs respectively capture sentence structure and word structure, and are kept separate as in linguistics. By invoking the second RNN to generate spellings for novel words in context, we obtain an open-vocabulary language model. For known words, embeddings are naturally inferred by combining evidence from type spelling and token context. Comparing to baselines (including a novel strong baseline), we beat previous work and establish state-of-the-art results on multiple datasets. ... : Accepted for publication at AAAI 2019 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1804.08205
https://dx.doi.org/10.48550/arxiv.1804.08205
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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)
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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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