Articles with public access mandates - Jason EisnerLearn more
Available somewhere: 28
Weighting finite-state transductions with neural context
P Rastogi, R Cotterell, J Eisner
Proceedings of the 2016 conference of the North American chapter of the …, 2016
Mandates: US National Science Foundation
Morphological smoothing and extrapolation of word embeddings
R Cotterell, H Schütze, J Eisner
Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016
Mandates: US National Science Foundation, German Research Foundation
The galactic dependencies treebanks: Getting more data by synthesizing new languages
D Wang, J Eisner
Transactions of the Association for Computational Linguistics 4, 491-505, 2016
Mandates: US National Science Foundation
On the complexity and typology of inflectional morphological systems
R Cotterell, C Kirov, M Hulden, J Eisner
Transactions of the Association for Computational Linguistics 7, 327-342, 2019
Mandates: US National Science Foundation
Imputing missing events in continuous-time event streams
H Mei, G Qin, J Eisner
International Conference on Machine Learning, 4475-4485, 2019
Mandates: US National Science Foundation
Transformer embeddings of irregularly spaced events and their participants
H Mei, C Yang, J Eisner
International conference on learning representations, 2021
Mandates: US National Science Foundation
Synthetic data made to order: The case of parsing
D Wang, J Eisner
Proceedings of the Conference on Empirical Methods in Natural Language …, 2018
Mandates: US National Science Foundation
Spell once, summon anywhere: A two-level open-vocabulary language model
SJ Mielke, J Eisner
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 6843-6850, 2019
Mandates: US National Science Foundation
Explaining and generalizing skip-gram through exponential family principal component analysis
R Cotterell, A Poliak, B Van Durme, J Eisner
Proceedings of the 15th conference of the European chapter of the …, 2017
Mandates: US National Science Foundation, US Department of Defense
Noise-contrastive estimation for multivariate point processes
H Mei, T Wan, J Eisner
Advances in neural information processing systems 33, 5204-5214, 2020
Mandates: US National Science Foundation
Neural finite-state transducers: Beyond rational relations
CC Lin, H Zhu, MR Gormley, J Eisner
Proceedings of the 2019 Conference of the North American Chapter of the …, 2019
Mandates: US National Science Foundation
Fine-Grained Prediction of Syntactic Typology: Discovering Latent Structure with Supervised Learning
D Wang, J Eisner
Transactions of the Association for Computational Linguistics 5, 147-161, 2017
Mandates: US National Science Foundation
Neural Datalog through time: Informed temporal modeling via logical specification
H Mei, G Qin, M Xu, J Eisner
International Conference on Machine Learning, 6808-6819, 2020
Mandates: US National Science Foundation
Surface statistics of an unknown language indicate how to parse it
D Wang, J Eisner
Transactions of the Association for Computational Linguistics 6, 667-685, 2018
Mandates: US National Science Foundation
Learning to prune: Exploring the frontier of fast and accurate parsing
T Vieira, J Eisner
Transactions of the Association for Computational Linguistics 5, 263-278, 2017
Mandates: US National Science Foundation, US Department of Defense
Dyna: Toward a self-optimizing declarative language for machine learning applications
T Vieira, M Francis-Landau, NW Filardo, F Khorasani, J Eisner
Proceedings of the 1st ACM SIGPLAN International Workshop on Machine …, 2017
Mandates: US National Science Foundation
Creating interactive macaronic interfaces for language learning
A Renduchintala, R Knowles, P Koehn, J Eisner
54th Annual Meeting of the Association for Computational Linguistics—System …, 2016
Mandates: US National Science Foundation
Speed-accuracy tradeoffs in tagging with variable-order CRFs and structured sparsity
T Vieira, R Cotterell, J Eisner
Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 2016
Mandates: US National Science Foundation
Analyzing learner understanding of novel L2 vocabulary
R Knowles, A Renduchintala, P Koehn, J Eisner
20th SIGNLL Conference on Computational Natural Language Learning, 126-135, 2016
Mandates: US National Science Foundation
Bayesian modeling of lexical resources for low-resource settings
N Andrews, M Dredze, B Van Durme, J Eisner
Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017
Mandates: US National Science Foundation, US Department of Defense
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