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We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited.
Learning and evaluating general linguistic intelligence
Dani Yogatama, Cyprien de Masson d’Autume, Jerome Connor, Tomás Kociský, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, and Phil Blunsom. 2019 · 1901
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Unsupervised data augmentation
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Statistical parsing with a context-free grammar and word statistics
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Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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David Mimno, Hanna M. Wallach, Edmund Talley, Miriam Leenders, and Andrew McCallum. 2011 · 2011
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Active Learning
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013 · 2013
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Linguistic models for analyzing and detecting biased language
Marta Recasens, Cristian Danescu-Niculescu-Mizil, and Dan Jurafsky. 2013 · 2013
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Diederik P. Kingma and Jimmy Ba. 2014 · 2014
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Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. 2014 · 2014
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Neural variational inference for text processing
Yishu Miao, Lei Yu, and Phil Blunsom. 2016 · 2016
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Virtual adversarial training for semi-supervised text classification
Takeru Miyato, Andrew M. Dai, and Ian J. Goodfellow. 2016 · 2016
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Autoencoding variational inference for topic models
Akash Srivastava and Charles A. Sutton. 2017 · 2017
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Variational autoencoder for semi-supervised text classification
Weidi Xu, Haoze Sun, Chao Deng, and Ying Tan. 2017 · 2017
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Improved variational autoencoders for text modeling using dilated convolutions
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick. 2017 · 2017
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Neural models for documents with metadata
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