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Noise Contrastive Estimation (NCE) is a powerful parameter estimation method for log-linear models, which avoids calculation of the partition function or its derivatives at each training step, a computationally demanding step in many cases.
Building a large annotated corpus of english: The penn treebank
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993 · 1993
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A maximum entropy approach to natural language processing
Adam L. Berger, Vincent J. Della Pietra, and Stephen A. Della Pietra. 1996 · 1996
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A course in large sample theory , volume 49
Thomas Shelburne Ferguson. 1996 · 1996
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001 · 2001
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Adaptive importance sampling to accelerate training of a neural probabilistic language model
Yoshua Bengio and Jean-Sébastien Senécal. 2008 · 2008
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen. 2012 · 2012
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A fast and simple algorithm for training neural probabilistic language models
Andriy Mnih and Yee W Teh. 2012 · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg. 2014 · 2014
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Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014 · 2014
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Exploring the limits of language modeling
Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. 2016 · 2016
Later among the works it cites.
Breaking the softmax bottleneck: A high-rank RNN language model
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, and William W. Cohen. 2018 · 2018
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