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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 19 of 19 1 Searching for More Efficient Dynamic Programs ... Vieira, Tim; Cotterell, Ryan; Eisner, Jason. - : ETH Zurich, 2021 BASE Show details 2 Searching for More Efficient Dynamic Programs Cotterell, Ryan; Eisner, Jason; Vieira, Tim In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021) BASE Show details 3 A Corpus for Large-Scale Phonetic Typology ... Salesky, Elizabeth; Chodroff, Eleanor; Pimentel, Tiago. - : arXiv, 2020 BASE Show details 4 A Corpus for Large-Scale Phonetic Typology ... Salesky, Elizabeth; Chodroff, Eleanor; Pimentel, Tiago. - : ETH Zurich, 2020 BASE Show details 5 A Corpus for Large-Scale Phonetic Typology Chodroff, Eleanor; Cotterell, Ryan; Wiesner, Matthew... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 6 Contextualization of Morphological Inflection ... Vylomova, Ekaterina; Cotterell, Ryan; Baldwin, Timothy. - : arXiv, 2019 BASE Show details 7 Are All Languages Equally Hard to Language-Model? Cotterell, Ryan; Mielke, Sebastian J; Eisner, Jason... In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 8 On the Complexity and Typology of Inflectional Morphological Systems Cotterell, Ryan; Kirov, Christo; Hulden, Mans... In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 327-342 (2019) (2019) BASE Show details 9 On the Complexity and Typology of Inflectional Morphological Systems ... Cotterell, Ryan; Kirov, Christo; Hulden, Mans. - : arXiv, 2018 BASE Show details 10 Are All Languages Equally Hard to Language-Model? ... Cotterell, Ryan; Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018 BASE Show details 11 A Deep Generative Model of Vowel Formant Typology ... Cotterell, Ryan; Eisner, Jason. - : arXiv, 2018 BASE Show details 12 Unsupervised Disambiguation of Syncretism in Inflected Lexicons ... Cotterell, Ryan; Kirov, Christo; Mielke, Sabrina J.. - : arXiv, 2018 BASE Show details 13 Quantifying the Trade-off Between Two Types of Morphological Complexity Cotterell, Ryan; Kirov, Christo; Hulden, Mans... In: Proceedings of the Society for Computation in Linguistics (2018) BASE Show details 14 Probabilistic Typology: Deep Generative Models of Vowel Inventories ... Cotterell, Ryan; Eisner, Jason. - : arXiv, 2017 BASE Show details 15 Probabilistic Typology: Deep Generative Models of Vowel Inventories ... Cotterell, Ryan; Eisner, Jason. - : Apollo - University of Cambridge Repository, 2017 BASE Show details 16 Probabilistic Typology: Deep Generative Models of Vowel Inventories 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 BASE Show details 17 Weighting Finite-State Transductions With Neural Context 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 Abstract: How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the out- put string, using recurrent neural networks. In contrast, we propose to keep the traditional architecture, which uses a finite-state trans- ducer to score all possible output strings, but to augment the scoring function with the help of recurrent networks. A stack of bidirec- tional LSTMs reads the input string from left- to-right and right-to-left, in order to summa- rize the input context in which a transducer arc is applied. We combine these learned fea- tures with the transducer to define a probabil- ity distribution over aligned output strings, in the form of a weighted finite-state automaton. This reduces hand-engineering of features, al- lows learned features to examine unbounded context in the input string, and still permits ex- act inference through dynamic programming. We illustrate our method on the tasks of mor- phological reinflection and lemmatization. URL: https://www.repository.cam.ac.uk/handle/1810/294482 https://doi.org/10.17863/CAM.41588 BASE Hide details 18 Modeling Word Forms Using Latent Underlying Morphs and Phonology ... Cotterell, Ryan; Peng, Nanyun; Eisner, Jason. - : Apollo - University of Cambridge Repository, 2015 BASE Show details 19 Modeling Word Forms Using Latent Underlying Morphs and Phonology Eisner, Jason; Cotterell, Ryan; Peng, Nanyun. - : MIT Press, 2015. : Transactions of the Association for Computational Linguistics (TACL) 2015, 2015 BASE Show details

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