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Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic representations.
The mathematics of statistical machine translation: Parameter estimation
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Jacob Andreas, Andreas Vlachos, and Stephen Clark. 2013 · 2013
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Abstract Meaning Representation for Sembanking
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A Discriminative Graph-Based Parser for the Abstract Meaning Representation
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Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
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Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 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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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Thang Luong, Ilya Sutskever, Quoc Le, Oriol Vinyals, and Wojciech Zaremba. 2015 · 2015
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Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
Keenon Werling, Gabor Angeli, and Christopher D. Manning. 2015 · 2015
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End-to-end learning of semantic role labeling using recurrent neural networks
Deep Biaffine Attention for Neural Dependency Parsing
Timothy Dozat and Christopher D. Manning. 2017 · 2017
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Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks
William Foland and James H. Martin. 2017 · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 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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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh. 2017 · 2017
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Jie Zhou and Wei Xu. 2015 · 2015
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Language to logical form with neural attention
Li Dong and Mirella Lapata. 2016 · 2016
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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
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Learning executable semantic parsers for natural language understanding
Percy Liang. 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
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Online Segment to Segment Neural Transduction
Lei Yu, Jan Buys, and Phil Blunsom. 2016 · 2016
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A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling
Diego Marcheggiani, Anton Frolov, and Ivan Titov. 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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Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
Rik van Noord and Johan Bos. 2017 · 2017
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Addressing the Data Sparsity Issue in Neural AMR Parsing
Xiaochang Peng, Chuan Wang, Daniel Gildea, and Nianwen Xue. 2017 · 2017
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Getting the Most out of AMR Parsing
Chuan Wang and Nianwen Xue. 2017 · 2017
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The Neural Noisy Channel
Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Tomas Kocisky. 2017 · 2017
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Learning Latent Permutations with Gumbel-Sinkhorn Networks
Gonzalo Mena, David Belanger, Scott Linderman, and Jasper Snoek. 2018 · 2018
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