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Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Graph convolutional encoders for syntax-aware neural machine translation
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Simaan. 2017 · 1967
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Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. 2001 · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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The description logic handbook: Theory, implementation and applications
Franz Baader. 2003 · 2003
Earlier work this paper cites.
Annotating noun argument structure for nombank
Adam Meyers, Ruth Reeves, Catherine Macleod, Rachel Szekely, Veronika Zielinska, Brian Young, and Ralph Grishman. 2004 · 2004
Earlier work this paper cites.
The proposition bank: An annotated corpus of semantic roles
Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005 · 2005
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A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009 · 2009
Earlier work this paper cites.
The first surface realisation shared task: Overview and evaluation results
Anja Belz, Michael White, Dominic Espinosa, Eric Kow, Deirdre Hogan, and Amanda Stent. 2011 · 2011
Earlier work this paper cites.
Stumaba : From deep representation to surface
Bernd Bohnet, Simon Mille, Benoît Favre, and Leo Wanner. 2011 · 2011
Earlier work this paper cites.
Abstract meaning representation for sembanking
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013 · 2013
Earlier work this paper cites.
Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
Earlier work this paper cites.
The Stanford CoreNLP natural language processing toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014 · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Cited alongside, same era.
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
The WebNLG challenge: Generating text from rdf data
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017b · 2017
Later among the works it cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. 2017 · 2017
Later among the works it cites.
Opennmt: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017 · 2017
Later among the works it cites.
Neural amr: Sequence-to-sequence models for parsing and generation
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 2017 · 2017
Later among the works it cites.
Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov. 2017 · 2017
Later among the works it cites.
Shared task proposal: Multilingual surface realization using universal dependency trees
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 2016 · 2016
Cited alongside, same era.
What to talk about and how? selective generation using lstms with coarse-to-fine alignment
Hongyuan Mei, Mohit Bansal, and Matthew R. Walter. 2016 · 2016
Cited alongside, same era.
Building RDF Content for Data-to-Text Generation
Laura Perez-Beltrachini, Rania SAYED, and Claire Gardent. 2016 · 2016
Cited alongside, same era.
Linguistic realisation as machine translation: Comparing different mt models for amr-to-text generation
Thiago Castro Ferreira, Iacer Calixto, Sander Wubben, and Emiel Krahmer. 2017 · 2017
Cited alongside, same era.
Simon Mille, Bernd Bohnet, Leo Wanner, and Anja Belz. 2017 · 2017
Later among the works it cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Later among the works it cites.
Challenges in data-to-document generation
Sam Wiseman, Stuart Shieber, and Alexander Rush. 2017 · 2017
Later among the works it cites.
Transition-based deep input linearization
Yue Zhang, Manish Shrivastava, and Ratish Puduppully. 2017 · 2017
Later among the works it cites.
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu. 2018 · 2018
Closest in time.
Graph-to-sequence learning using gated graph neural networks
Daniel Beck, Gholamreza Haffari, and Trevor Cohn. 2018 · 2018
Closest in time.
Exploiting semantics in neural machine translation with graph convolutional networks
Diego Marcheggiani, Joost Bastings, and Ivan Titov. 2018 · 2018
Closest in time.
Evaluating scoped meaning representations
Rik Van Noord, Lasha Abzianidze, Hessel Haagsma, and Johan Bos. 2018 · 2018
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