Fetching the paper…
Reading the bibliography…
Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Training with exploration improves a greedy stack LSTM parser
Miguel Ballesteros, Yoav Goldberg, Chris Dyer, and Noah A. Smith. 2016 · 2010
Earlier work this paper cites.
A dynamic oracle for arc-eager dependency parsing
Yoav Goldberg and Joakim Nivre. 2012 · 2012
Earlier work this paper cites.
Smatch: an evaluation metric for semantic feature structures
Shu Cai and Kevin Knight. 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
Earlier work this paper cites.
Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing
James Goodman, Andreas Vlachos, and Jason Naradowsky. 2016 · 2016
Earlier work this paper cites.
The AMU-UEDIN submission to the WMT16 news translation task: Attention-based NMT models as feature functions in phrase-based SMT
Marcin Junczys-Dowmunt, Tomasz Dwojak, and Rico Sennrich. 2016 · 2016
Earlier work this paper cites.
Generating english from abstract meaning representations
Nima Pourdamghani, Kevin Knight, and Ulf Hermjakob. 2016 · 2016
Cited alongside, same era.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
AMR parsing using stack-LSTMs
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.
Rik van Noord and Johan Bos. 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
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.
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.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Broad-coverage semantic parsing as transduction
Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme. 2019b · 2019
Later among the works it cites.
AMR parsing via graph-sequence iterative inference
Deng Cai and Wai Lam. 2020 · 2020
Closest in time.
Transition-based parsing with stack-transformers
Ramon Fernandez Astudillo, Miguel Ballesteros, Tahira Naseem, Austin Blodget, and Radu Florian. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Synthetic QA corpora generation with roundtrip consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019 · 2019
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. 2017a
Cited in the paper.
Neural amr: Sequence-to-sequence models for parsing and generation
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 2017b
Cited in the paper.
AMR parsing as graph prediction with latent alignment
Chunchuan Lyu and Ivan Titov. 2018a
Cited in the paper.
AMR parsing as graph prediction with latent alignment
Chunchuan Lyu and Ivan Titov. 2018b
Cited in the paper.
AMR parsing as sequence-to-graph transduction
Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme. 2019a
Cited in the paper.
Closest in time.
Gpt-too: A language-model-first approach for amr-to-text generation
Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, and Salim Roukos. 2020 · 2020
Closest in time.