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We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP).
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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Cognitive Grammar: A Basic Introduction
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Transition-based parsing of the chinese treebank using a global discriminative model
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Abstract meaning representation for sembanking
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Smatch: an evaluation metric for semantic feature structures
Shu Cai and Kevin Knight. 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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A discriminative graph-based parser for the abstract meaning representation
Jeffrey Flanigan, Sam Thomson, Jaime Carbonell, Chris Dyer, and Noah A Smith. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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The stanford corenlp natural language processing toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014 · 2014
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Aligning english strings with abstract meaning representation graphs
Nima Pourdamghani, Yang Gao, Ulf Hermjakob, and Kevin Knight. 2014 · 2014
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Broad-coverage ccg semantic parsing with amr
Yoav Artzi, Kenton Lee, and Luke Zettlemoyer. 2015 · 2015
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Toward abstractive summarization using semantic representations
Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, and Noah A. Smith. 2015 · 2015
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
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A synchronous hyperedge replacement grammar based approach for amr parsing
Xiaochang Peng, Linfeng Song, and Daniel Gildea. 2015 · 2015
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Parsing english into abstract meaning representation using syntax-based machine translation
Michael Pust, Ulf Hermjakob, Kevin Knight, Daniel Marcu, and Jonathan May. 2015 · 2015
Cited alongside, same era.
Robust subgraph generation improves abstract meaning representation parsing
Keenon Werling, Gabor Angeli, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
Cited alongside, same era.
RIGA at SemEval-2016 task 8: Impact of Smatch extensions and character-level neural translation on AMR parsing accuracy
Guntis Barzdins and Didzis Gosko. 2016 · 2016
Cited alongside, same era.
Deep biaffine attention for neural dependency parsing
Timothy Dozat and Christopher D Manning. 2016 · 2016
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Rik van Noord and Johan Bos. 2017 · 2017
Later among the works it cites.
Addressing the data sparsity issue in neural AMR parsing
Xiaochang Peng, Chuan Wang, Daniel Gildea, and Nianwen Xue. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Getting the most out of amr parsing
Chuan Wang and Nianwen Xue. 2017 · 2017
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AMR parsing with an incremental joint model
Junsheng Zhou, Feiyu Xu, Hans Uszkoreit, Weiguang Qu, Ran Li, and Yanhui Gu. 2016 · 2017
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AMR dependency parsing with a typed semantic algebra
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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. 2016 · 2016
Cited alongside, same era.
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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Addressing a question answering challenge by combining statistical methods with inductive rule learning and reasoning
Arindam Mitra and Chitta Baral. 2016 · 2016
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Camr at semeval-2016 task 8: An extended transition-based amr parser
Chuan Wang, Sameer Pradhan, Xiaoman Pan, Heng Ji, and Nianwen Xue. 2016 · 2016
Cited alongside, same era.
AMR parsing using stack-LSTMs
Miguel Ballesteros and Yaser Al-Onaizan. 2017 · 2017
Cited alongside, same era.
Oxford at semeval-2017 task 9: Neural amr parsing with pointer-augmented attention
Jan Buys and Phil Blunsom. 2017 · 2017
Cited alongside, same era.
An incremental parser for abstract meaning representation
Marco Damonte, Shay B. Cohen, and Giorgio Satta. 2017 · 2017
Cited alongside, same era.
Jonas Groschwitz, Matthias Lindemann, Meaghan Fowlie, Mark Johnson, and Alexander Koller. 2018 · 2018
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Better transition-based amr parsing with refined search space
Zhijiang Guo and Wei Lu. 2018 · 2018
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Guided neural language generation for abstractive summarization using abstract meaning representation
Hardy Hardy and Andreas Vlachos. 2018 · 2018
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An AMR aligner tuned by transition-based parser
Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, and Ting Liu. 2018 · 2018
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AMR parsing as graph prediction with latent alignment
Chunchuan Lyu and Ivan Titov. 2018 · 2018
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Amr parsing with cache transition systems
Xiaochang Peng, Daniel Gildea, and Giorgio Satta. 2018 · 2018
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Linguistically-informed self-attention for semantic role labeling
Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum. 2018 · 2018
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Compositional semantic parsing across graphbanks
Matthias Lindemann, Jonas Groschwitz, and Alexander Koller. 2019 · 2019
Closest in time.
Rewarding Smatch: Transition-based AMR parsing with reinforcement learning
Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, and Miguel Ballesteros. 2019 · 2019
Closest in time.
AMR parsing as sequence-to-graph transduction
Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme. 2019 · 2019
Closest in time.