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The literature on structured prediction for NLP describes a rich collection of distributions and algorithms over sequences, segmentations, alignments, and trees; however, these algorithms are difficult to utilize in deep learning frameworks.
Cooperative learning of disjoint syntax and semantics
Serhii Havrylov, Germán Kruszewski, and Armand Joulin. 2019 · 1902
Earlier work this paper cites.
Temporal parallelization of bayesian filters and smoothers
Simo Särkkä and Ángel F García-Fernández. 2019 · 1905
Earlier work this paper cites.
Statistical inference for probabilistic functions of finite state markov chains
Leonard E Baum and Ted Petrie. 1966 · 1966
Earlier work this paper cites.
An efficient recognition and syntax-analysis algorithm for context-free languages
Tadao Kasami. 1966 · 1966
Earlier work this paper cites.
A general method applicable to the search for similarities in the amino acid sequence of two proteins
Saul B Needleman and Christian D Wunsch. 1970 · 1970
Earlier work this paper cites.
Factorial hidden markov models
Zoubin Ghahramani and Michael I Jordan. 1996 · 1996
Earlier work this paper cites.
Hmm-based word alignment in statistical translation
Stephan Vogel, Hermann Ney, and Christoph Tillmann. 1996 · 1996
Earlier work this paper cites.
Semiring parsing
Joshua Goodman. 1999 · 1999
Earlier work this paper cites.
Bilexical grammars and their cubic-time parsing algorithms
Jason Eisner. 2000 · 2000
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001 · 2001
Earlier work this paper cites.
Word reordering and a dynamic programming beam search algorithm for statistical machine translation
Christoph Tillmann and Hermann Ney. 2003 · 2003
Earlier work this paper cites.
Dyna: A declarative language for implementing dynamic programs
Jason Eisner, Eric Goldlust, and Noah A Smith. 2004 · 2004
Earlier work this paper cites.
Crf++: Yet another crf toolkit
Taku Kudo. 2005 · 2005
Earlier work this paper cites.
Non-projective dependency parsing using spanning tree algorithms
Ryan McDonald, Fernando Pereira, Kiril Ribarov, and Jan Hajič. 2005 · 2005
Cited alongside, same era.
Semi-markov conditional random fields for information extraction
Sunita Sarawagi and William W Cohen. 2005 · 2005
Cited alongside, same era.
A fully bayesian approach to unsupervised part-of-speech tagging
Sharon Goldwater and Tom Griffiths. 2007 · 2007
Cited alongside, same era.
Structured prediction models via the matrix-tree theorem
Terry Koo, Amir Globerson, Xavier Carreras Pérez, and Michael Collins. 2007 · 2007
Cited alongside, same era.
Crfsuite: a fast implementation of conditional random fields (crfs)
Naoaki Okazaki. 2007 · 2007
Cited alongside, same era.
Efficient, feature-based, conditional random field parsing
Jenny Rose Finkel, Alex Kleeman, and Christopher D Manning. 2008 · 2008
Inside-outside and forward-backward algorithms are just backprop (tutorial paper)
Jason Eisner. 2016 · 2016
Later among the works it cites.
Composing graphical models with neural networks for structured representations and fast inference
Matthew J Johnson, David K Duvenaud, Alex Wiltschko, Ryan P Adams, and Sandeep R Datta. 2016 · 2016
Later among the works it cites.
Learning to compose words into sentences with reinforcement learning
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Wang Ling. 2016 · 2016
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Joshua V Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, and Rif A Saurous. 2017 · 2017
Later among the works it cites.
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Cited alongside, same era.
Svmstruct: Support vector machine for complex outputs
Thorsten Joachims. 2008 · 2008
Cited alongside, same era.
First-and second-order expectation semirings with applications to minimum-risk training on translation forests
Zhifei Li and Jason Eisner. 2009 · 2009
Cited alongside, same era.
Turbo parsers: Dependency parsing by approximate variational inference
André FT Martins, Noah A Smith, Eric P Xing, Pedro MQ Aguiar, and Mário AT Figueiredo. 2010 · 2010
Cited alongside, same era.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
Cited alongside, same era.
Speech and language processing. vol. 3
Dan Jurafsky and James H Martin. 2014 · 2014
Cited alongside, same era.
Greg Durrett and Dan Klein. 2015 · 2015
Cited alongside, same era.
Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush. 2017 · 2017
Later among the works it cites.
Self-critical sequence training for image captioning
Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. 2017 · 2017
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Tvm: end-to-end optimization stack for deep learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018 · 2018
Later among the works it cites.
Differentiable dynamic programming for structured prediction and attention
Arthur Mensch and Mathieu Blondel. 2018 · 2018
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Listops: A diagnostic dataset for latent tree learning
Nikita Nangia and Samuel R Bowman. 2018 · 2018
Later among the works it cites.
Learning neural templates for text generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2018 · 2018
Later among the works it cites.
Pyro: Deep universal probabilistic programming
Eli Bingham, Jonathan P Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D Goodman. 2019 · 2019
Later among the works it cites.
Pystruct: learning structured prediction in python
Andreas C Müller and Sven Behnke. 2014 · 2060
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