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In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance.
Assessing BERT’s syntactic abilities
Yoav Goldberg. 2019 · 1901
Earlier work this paper cites.
Building a large annotated corpus of English: The Penn treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993 · 1993
Earlier work this paper cites.
Incorporating BERT into neural machine translation
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu. 2020 · 2010
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.
Smatch: an evaluation metric for semantic feature structure
Shu Cai and Kevin Knight. 2013 · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
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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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
Semi-supervised sequence learning
Andrew M. Dai and Quoc V. Le. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Robust subgraph generation improves abstract meaning representation parsing
Keenon Werling, Gabor Angeli, and Christoerpher D. Manning. 2015 · 2015
Earlier work this paper cites.
Parsing as language modeling
Do Kook Choe and Eugene Charniak. 2016 · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Does string-based neural MT learn source syntax?
Xing Shi, Inkit Padhi, and Kevin Knight. 2016 · 2016
Earlier work this paper cites.
An incremental parser for abstract meaning representation
Marco Damonte, Shay B. Cohen, and Giorgio Satta. 2017 · 2017
Earlier work this paper cites.
AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, and Fernanda Viégas. 2017 · 2017
Cited alongside, same era.
OpenNMT: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 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.
Modeling source syntax for neural machine translation
Junhui Li, Deyi Xiong, Zhaopeng Tu, Muhua Zhu, Min Zhang, and Guodong Zhou. 2017 · 2017
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
Core semantic first: A top-down approach for AMR parsing
Deng Cai and Wai Lam. 2019 · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Pre-trained language model representations for language generation
Sergey Edunov, Alexei Baevski, and Michael Auli. 2019 · 2019
Later among the works it cites.
Modeling source syntax and semantics for neural AMR parsing
Donglai Ge, Junhui Li, Muhua Zhu, and Shoushan Li. 2019 · 2019
Later among the works it cites.
What does BERT learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
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Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Neural semantic parsing by character-based translation: Experiments with abstract meaning representation
Rik van Noord and Johan Bos. 2017 · 2017
Cited alongside, same era.
Addressing the data sparsity issue in neural AMR parsing
Xiaochang Peng, Chuang Wang, Daniel Gildea, and Nianwen Xue. 2017 · 2017
Cited alongside, same era.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N.Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
AMR dependency parsing with a typed semantic algebra
Jonas Groschwitz, Matthias Lindemann, Meaghan Fowlie, Mark Johnson, and Alexander Koller. 2018 · 2018
Cited alongside, same era.
Better transition-based AMR parsing with a refined search space
Zhijiang Guo and Wei Lu. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Rewarding smatch: Transition-based AMR parsing with reinforcement learning
Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, and Miguel Ballesteros. 2019 · 2019
Later among the works it cites.
MASS: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 2019
Later among the works it cites.
Denoising based sequence-to-sequence pre-training for text generation
Liang Wang, Wei Zhao, Ruoyu Jia, Sujian Li, and Jingming Liu. 2019 · 2019
Later among the works it cites.
Modeling graph structure in transformer for better amr-to-text generation
Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, and Guodong Zhou. 2019 · 2019
Later among the works it cites.
AMR parsing via graph ⇌ \rightleftharpoons sequence iterative inference
Deng Cai and Wai Lam. 2020 · 2020
Closest in time.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2020 · 2020
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