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This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser.
Feature-rich part-of-speech tagging with a cyclic dependency network
Kristina Toutanova, Dan Klein, Christopher D Manning, and Yoram Singer · 2003
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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A fast and accurate dependency parser using neural networks
Danqi Chen and Christopher D Manning · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Dependency-based word embeddings
Omer Levy and Yoav Goldberg · 2014
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Leveraging linguistic structure for open domain information extraction
Gabor Angeli, Melvin Johnson Premkumar, and Christopher D Manning · 2015
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Transition-based dependency parsing with stack long short-term memory
Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A Smith · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2015
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Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Grounded semantic parsing for complex knowledge extraction
Ankur P Parikh, Hoifung Poon, and Kristina Toutanova · 2015
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Structured training for neural network transition-based parsing
David Weiss, Chris Alberti, Michael Collins, and Slav Petrov · 2015
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A fast unified model for parsing and sentence understanding
Samuel R Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D Manning, and Christopher Potts · 2016
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Bi-directional attention with agreement for dependency parsing
Hao Cheng, Hao Fang, Xiaodong He, Jianfeng Gao, and Li Deng · 2016
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A joint many-task model: Growing a neural network for multiple nlp tasks
Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher · 2016
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Simple and accurate dependency parsing using bidirectional LSTM feature representations
Eliyahu Kiperwasser and Yoav Goldberg · 2016
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What do recurrent neural network grammars learn about syntax?
Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, Graham Neubig, and Noah A. Smith · 2016
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Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins · 2016
Cited alongside, same era.
Training with exploration improves a greedy stack-LSTM parser
Miguel Ballesteros, Yoav Goldberg, Chris Dyer, and Noah A Smith · 2016
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Compositional learning of embeddings for relation paths in knowledge bases and text
Kristina Toutanova, Xi Victoria Lin, and Wen-tau Yih · 2016
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