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The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph.
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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Loopy belief propagation for approximate inference: An empirical study
Kevin P Murphy, Yair Weiss, and Michael I Jordan. 1999 · 1999
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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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Statistical phrase-based translation
Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003 · 2003
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Online large-margin training of dependency parsers
Ryan McDonald, Koby Crammer, and Fernando Pereira. 2005 · 2005
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Semantics-based machine translation with hyperedge replacement grammars
Bevan Jones, Jacob Andreas, Daniel Bauer, Karl Moritz Hermann, and Kevin Knight. 2012 · 2012
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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
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Improving event detection with abstract meaning representation
Xiang Li, Thien Huu Nguyen, Kai Cao, and Ralph Grishman. 2015 · 2015
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Addressing a question answering challenge by combining statistical methods with inductive rule learning and reasoning
Arindam Mitra and Chitta Baral. 2015 · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015 · 2015
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A discriminative model for semantics-to-string translation
Aleš Tamchyna, Chris Quirk, and Michel Galley. 2015 · 2015
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CMU at semeval-2016 task 8: Graph-based AMR parsing with infinite ramp loss
Jeffrey Flanigan, Chris Dyer, Noah A. Smith, and Jaime Carbonell. 2016a · 2016
Cited alongside, same era.
Generation from abstract meaning representation using tree transducers
RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation
Normunds Gruzitis, Didzis Gosko, and Guntis Barzdins. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
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Neural AMR: Sequence-to-sequence models for parsing and generation
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 2017 · 2017
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Sheffield at semeval-2017 task 9: Transition-based language generation from amr
Gerasimos Lampouras and Andreas Vlachos. 2017 · 2017
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Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov. 2017 · 2017
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Jeffrey Flanigan, Chris Dyer, Noah A. Smith, and Jaime Carbonell. 2016b · 2016
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016 · 2016
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Pointing the unknown words
Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016 · 2016
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Generating English from abstract meaning representations
Nima Pourdamghani, Kevin Knight, and Ulf Hermjakob. 2016 · 2016
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Neural headline generation on abstract meaning representation
Sho Takase, Jun Suzuki, Naoaki Okazaki, Tsutomu Hirao, and Masaaki Nagata. 2016 · 2016
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Modeling coverage for neural machine translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016 · 2016
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Simon Mille, Roberto Carlini, Alicia Burga, and Leo Wanner. 2017 · 2017
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Cross-sentence n-ary relation extraction with graph LSTMs
Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
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AMR-to-text generation with synchronous node replacement grammar
Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2017 · 2017
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Leveraging context information for natural question generation
Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, and Daniel Gildea. 2018 · 2018
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Graph2seq: Graph to sequence learning with attention-based neural networks
Kun Xu, Lingfei Wu, Zhiguo Wang, and Vadim Sheinin. 2018 · 2018
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