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We propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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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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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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Smatch: an evaluation metric for semantic feature structures
Shu Cai and Kevin Knight. 2013 · 2013
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Improving efficiency and accuracy in multilingual entity extraction
Joachim Daiber, Max Jakob, Chris Hokamp, and Pablo N Mendes. 2013 · 2013
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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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Broad-coverage ccg semantic parsing with amr
Yoav Artzi, Kenton Lee, and Luke Zettlemoyer. 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
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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
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Deep biaffine attention for neural dependency parsing
Timothy Dozat and Christopher D Manning. 2016 · 2016
Cited alongside, same era.
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
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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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Character-aware neural language models
Yoon Kim, Yacine Jernite, David Sontag, and Alexander M Rush. 2016 · 2016
Cited alongside, same era.
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
Getting the most out of amr parsing
Chuan Wang and Nianwen Xue. 2017 · 2017
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AMR dependency parsing with a typed semantic algebra
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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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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Sequence-to-sequence models for cache transition systems
Xiaochang Peng, Linfeng Song, Daniel Gildea, and Giorgio Satta. 2018 · 2018
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Miguel Ballesteros and Yaser Al-Onaizan. 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.
Neural AMR: Sequence-to-sequence models for parsing and generation
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Rik van Noord and Johan Bos. 2017 · 2017
Cited alongside, same era.
Addressing the data sparsity issue in neural AMR parsing
Xiaochang Peng, Chuan Wang, Daniel Gildea, and Nianwen Xue. 2017 · 2017
Cited alongside, same era.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
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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Core semantic first: A top-down approach for AMR parsing
Deng Cai and Wai Lam. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Compositional semantic parsing across graphbanks
Matthias Lindemann, Jonas Groschwitz, and Alexander Koller. 2019 · 2019
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Rewarding Smatch: Transition-based AMR parsing with reinforcement learning
Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, and Miguel Ballesteros. 2019 · 2019
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Broad-coverage semantic parsing as transduction
Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme. 2019b · 2019
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