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We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar.
Asymptotically Subminimax Solutions of Compound Statistical Decision Problems
Herbert Robbins. 1951 · 1951
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An Empirical Bayes Approach to Statistics
Herbert Robbins. 1956 · 1956
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Indexed Grammars—An Extension of Context-Free Grammars
Alfred Aho. 1968 · 1968
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Maximum Likelihood from Incomplete Data via the EM Algorithm
Arthur P. Dempster, Nan M. Laird, and Donald B. Rubin. 1977 · 1977
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Trainable Grammars for Speech Recognition
James K. Baker. 1979 · 1979
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Statistical Decision Theory and Bayesian Analysis
James O. Berger. 1985 · 1985
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The Estimation of Stochastic Context-Free Grammars Using the Inside-Outside Algorithm
Karim Lari and Steve Young. 1990 · 1990
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Two Experiments on Learning Probabilistic Dependency Grammars from Corpora
Glenn Carroll and Eugene Charniak. 1992 · 1992
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Inside-Outside Reestimation from Partially Bracketed Corpora
Fernando Pereira and Yves Schabes. 1992 · 1992
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Statistical Language Learning
Eugene Charniak. 1993 · 1993
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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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Tagging English Text with a Probabilistic Model
Bernard Merialdo. 1994 · 1994
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Three Generative, Lexicalised Models for Statistical Parsing
Michael Collins. 1997 · 1997
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PCFG Models of Linguistic Tree Representations
Mark Johnson. 1998 · 1998
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Unsupervised Induction of Stochastic Context Free Grammars Using Distributional Clustering
Alexander Clark. 2001 · 2001
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A Generative Constituent-Context Model for Improved Grammar Induction
Dan Klein and Christopher Manning. 2002 · 2002
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Accurate Unlexicalized Parsing
Dan Klein and Christopher D. Manning. 2003 · 2003
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Optimization with EM and Expectation-Conjugate-Gradient
Ruslan Salakhutdinov, Sam Roweis, and Zoubin Ghahramani. 2003 · 2003
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Compound Decision Theory and Empirical Bayes Methods
Cun-Hui Zhang. 2003 · 2003
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Corpus-based Induction of Syntactic Structure: Models of Dependency and Constituency
Dan Klein and Christopher Manning. 2004 · 2004
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Annealing Techniques for Unsupervised Statistical Language Learning
Noah A. Smith and Jason Eisner. 2004 · 2004
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Probabilistic CFG with Latent Annotations
Takuya Matsuzaki, Yusuke Miyao, and Junichi Tsujii. 2005 · 2005
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The Penn Chinese Treebank: Phrase Structure Annotation of a Large Corpus
Naiwen Xue, Fei Xia, Fu dong Chiou, and Marta Palmer. 2005 · 2005
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An All-Subtrees Approach to Unsupervised Parsing
Rens Bod. 2006 · 2006
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Variational Bayesian Grammar Induction for Natural Language
Kenichi Kurihara and Taisuke Sato. 2006 · 2006
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Learning Accurate, Compact, and Interpretable Tree Annotation
Slav Petrov, Leon Barret, Romain Thibaux, and Dan Klein. 2006 · 2006
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Bayesian Inference for PCFGs via Markov chain Monte Carlo
Mark Johnson, Thomas L. Griffiths, and Sharon Goldwater. 2007 · 2007
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The Infinite PCFG using Hierarchical Dirichlet Processes
Percy Liang, Slav Petrov, Michael I. Jordan, and Dan Klein. 2007 · 2007
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Fast Unsupervised Incremental Parsing
Yoav Seginer. 2007 · 2007
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Curriculum Learning
Yoshua Bengio, Jerome Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
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Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction
Shay B. Cohen, Kevin Gimpel, and Noah A Smith. 2009 · 2009
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Shared Logistic Normal Distributions for Soft Parameter Tying in Unsupervised Grammar Induction
Shay B. Cohen and Noah A Smith. 2009 · 2009
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Online EM for Unsupervised models
Percy Liang and Dan Klein. 2009 · 2009
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Unsupervised Multilingual Grammar Induction
Benjamin Snyder, Tahira Naseem, and Regina Barzilay. 2009 · 2009
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Painless Unsupervised Learning with Features
Taylor Berg-Kirkpatrick, Alexandre Bouchard-Cote, John DeNero, and Dan Klein. 2010 · 2010
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Improved Fully Unsupervised Parsing with Zoomed Learning
Roi Reichart and Ari Rappoport. 2010 · 2010
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Character-Aware Neural Language Models
Yoon Kim, Yacine Jernite, David Sontag, and Alexander M. Rush. 2016 · 2016
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Improving Variational Inference with Autoregressive Flow
Diederik P. Kingma, Tim Salimans, and Max Welling. 2016 · 2016
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Using Left-corner Parsing to Encode Universal Structural Constraints in Grammar Induction
Hiroshi Noji, Yusuke Miyao, and Mark Johnson. 2016 · 2016
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Using the Output Embedding to Improve Language Models
Ofir Press and Lior Wolf. 2016 · 2016
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Unsupervised Neural Hidden Markov Models
Ke Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, and Kevin Knight. 2016 · 2016
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Graph-based Dependency Parsing with Bidirectional LSTM
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Simpled Unsupervised Grammar Induction from Raw Text with Cascaded Finite State Methods
Elis Ponvert, Jason Baldridge, and Katrin Erk. 2011 · 2011
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Spectral Learning of Latent-Variable PCFGs
Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster, and Lyle Ungar. 2012 · 2012
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A Feature-Rich Constituent Context Model for Grammar Induction
Dave Golland, John DeNero, and Jakob Uszkoreit. 2012 · 2012
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Improved Constituent Context Model with Features
Yun Huang, Min Zhang, and Chew Lim Tan. 2012 · 2012
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Three Dependency-and-Boundary Models for Grammar Induction
Valentin I. Spitkovsky, Hiyan Alshawi, and Daniel Jurafsky. 2012 · 2012
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Parsing with Compositional Vector Grammars
Richard Socher, John Bauer, Christopher D. Manning, and Andrew Y. Ng. 2013 · 2013
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Wenhui Wang and Baobao Chang. 2016 · 2016
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A Minimal Span-Based Neural Constituency Parser
Mitchell Stern, Jacob Andreas, and Dan Klein. 2017 · 2017
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On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen, and Elad Hazan. 2018 · 2018
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Unsupervised Learning of Syntactic Structure with Invertible Neural Projections
Junxian He, Graham Neubig, and Taylor Berg-Kirkpatrick. 2018 · 2018
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Grammar Induction with Neural Language Models: An Unusual Replication
Phu Mon Htut, Kyunghyun Cho, and Samuel R. Bowman. 2018 · 2018
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Unsupervised Grammar Induction with Depth-bounded PCFG
Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler, and Lane Schwartz. 2018 · 2018
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Constituency Parsing with a Self-Attentive Encoder
Nikita Kitaev and Dan Klein. 2018 · 2018
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LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Adhiguna Kuncoro, Chris Dyer, John Hale, Dani Yogatama, Stephen Clark, and Phil Blunsom. 2018 · 2018
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Structured Alignment Networks for Matching Sentences
Yang Liu, Matt Gardner, and Mirella Lapata. 2018 · 2018
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Targeted Syntactic Evaluation of Language Models
Rebecca Marvin and Tal Linzen. 2018 · 2018
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Neural Language Modeling by Jointly Learning Syntax and Lexicon
Yikang Shen, Zhouhan Lin, Chin-Wei Huang, and Aaron Courville. 2018 · 2018
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Learning Neural Templates for Text Generation
Sam Wiseman, Stuart M. Shieber, and Alexander M. Rush. 2018 · 2018
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Benefits of Over-Parameterization with EM
Ji Xu, Daniel Hsu, and Arian Maleki. 2018 · 2018
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Gaussian Mixture Latent Vector Grammars
Yanpeng Zhao, Liwen Zhang, and Kewei Tu. 2018 · 2018
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Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders
Andrew Drozdov, Patrick Verga, Mohit Yadev, Mohit Iyyer, and Andrew McCallum. 2019 · 2019
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Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Simon S. Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh. 2019 · 2019
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Unsupervised Recurrent Neural Network Grammars
Yoon Kim, Alexander M. Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, and Gábor Melis. 2019 · 2019
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Scalable Syntax-Aware Language Models Using Knowledge Distillation
Adhiguna Kuncoro, Chris Dyer, Laura Rimell, Stephen Clark, and Phil Blunsom. 2019 · 2019
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An Imitation Learning Approach to Unsupervised Parsing
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Text Generation with Exemplar-based Adaptive Decoding
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Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
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Visually Grounded Neural Syntax Acquisition
Haoyue Shi, Jiayuan Mao, Kevin Gimpel, and Karen Livescu. 2019 · 2019
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Mutual Information Maximization for Simple and Accurate Part-of-Speech Induction
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Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
Ethan Wilcox, Peng Qian, Richard Futrell, Miguel Ballesteros, and Roger Levy. 2019 · 2019
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