Fetching the paper…
Reading the bibliography…
Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Manual of information to accompany a standard corpus of present-day edited american english, for use with digital computers
W. N. Francis and H. Kučera. 1964 · 1964
Earlier work this paper cites.
A method of solving a convex programming problem with convergence rate o ( 1 / k 2 ) o(1/k^{2})
Yurii Nesterov. 1983 · 1983
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks
R. J. Williams and D. Zipser. 1989 · 1989
Earlier work this paper cites.
Multitask learning: a knowledge-based source of inductive bias
Rich Caruana. 1993 · 1993
Earlier work this paper cites.
English verb classes and alternations: A preliminary investigation
Beth Levin. 1993 · 1993
Earlier work this paper cites.
Building a large annotated corpus of English: The Penn TreeBank
Mitchell P. Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993 · 1993
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi. 1994 · 1994
Earlier work this paper cites.
Feature-rich part-of-speech tagging with a cyclic dependency network
Kristina Toutanova, Dan Klein, Christopher D Manning, and Yoram Singer. 2003 · 2003
Earlier work this paper cites.
Introduction to the conll-2005 shared task: Semantic role labeling
Xavier Carreras and Lluís Màrquez. 2005 · 2005
Earlier work this paper cites.
The proposition bank: An annotated corpus of semantic roles
Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005 · 2005
Earlier work this paper cites.
Semantic role labeling using different syntactic views
Sameer Pradhan, Wayne Ward, Kadri Hacioglu, James Martin, and Dan Jurafsky. 2005 · 2005
Earlier work this paper cites.
Joint parsing and semantic role labeling
Charles Sutton and Andrew McCallum. 2005 · 2005
Earlier work this paper cites.
Semi-supervised learning for spoken language understanding using semantic role labeling
Gokhan Tur, Dilek Hakkani-Tür, and Ananlada Chotimongkol. 2005 · 2005
Earlier work this paper cites.
Combination strategies for semantic role labeling
Mihai Surdeanu, Lluís Màrquez, Xavier Carreras, and Pere R. Comas. 2007 · 2007
Earlier work this paper cites.
Dependency-based semantic role labeling of propbank
Richard Johansson and Pierre Nugues. 2008 · 2008
Earlier work this paper cites.
The stanford typed dependencies representation
Marie-Catherine de Marneffe and Christopher D. Manning. 2008 · 2008
Earlier work this paper cites.
The importance of syntactic parsing and inference in semantic role labeling
Vasin Punyakanok, Dan Roth, and Wen-Tau Yih. 2008 · 2008
Earlier work this paper cites.
A global joint model for semantic role labeling
Kristina Toutanova, Aria Haghighi, and Christopher D. Manning. 2008 · 2008
Earlier work this paper cites.
Search-based structured prediction
Hal Daumé III, John Langford, and Daniel Marcu. 2009 · 2009
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.
Semantic role features for machine translation
Ding Liu and Daniel Gildea. 2010 · 2010
Earlier work this paper cites.
Getting the most out of transition-based dependency parsing
Jinho D. Choi and Martha Palmer. 2011 · 2011
Cited alongside, same era.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
Cited alongside, same era.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and J. Andrew Bagnell. 2011 · 2011
Cited alongside, same era.
A dynamic oracle for arc-eager dependency parsing
Yoav Goldberg and Joakim Nivre. 2012 · 2012
Cited alongside, same era.
Semantic roles for string to tree machine translation
Marzieh Bazrafshan and Daniel Gildea. 2013 · 2013
Cited alongside, same era.
Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing
Yun-Nung Chen, William Yang Wang, and Alexander I Rudnicky. 2013 · 2013
Simple and accurate dependency parsing using bidirectional LSTM feature representations
Eliyahu Kiperwasser and Yoav Goldberg. 2016 · 2016
Later among the works it cites.
Neural semantic role labeling with dependency path embeddings
Michael Roth and Mirella Lapata. 2016 · 2016
Later among the works it cites.
Deep multi-task learning with low level tasks supervised at lower layers
Anders Søgaard and Yoav Goldberg. 2016 · 2016
Later among the works it cites.
Stack-propagation: Improved representation learning for syntax
Yuan Zhang and David Weiss. 2016 · 2016
Later among the works it cites.
When is multitask learning effective? semantic sequence prediction under varying data conditions
Héctor Martínez Alonso and Barbara Plank. 2017 · 2017
Later among the works it cites.
Identifying beneficial task relations for multi-task learning in deep neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Rectifier nonlinearities improve neural network acoustic models
Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. 2013 · 2013
Cited alongside, same era.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013 · 2013
Cited alongside, same era.
Towards robust linguistic analysis using OntoNotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013 · 2013
Cited alongside, same era.
Modeling biological processes for reading comprehension
Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Brad Huang, Christopher D. Manning, Abby Vander Linden, Brittany Harding, and Peter Clark. 2014 · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous systems, 2015
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. 2015 · 2015
Cited alongside, same era.
Joachim Bingel and Anders Søgaard. 2017 · 2017
Later among the works it cites.
Deep biaffine attention for neural dependency parsing
Timothy Dozat and Christopher D. Manning. 2017 · 2017
Later among the works it cites.
A joint many-task model: Growing a neural network for multiple nlp tasks
Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. 2017 · 2017
Later among the works it cites.
Deep semantic role labeling: What works and what’s next
Luheng He, Kenton Lee, Mike Lewis, and Luke Zettlemoyer. 2017 · 2017
Later among the works it cites.
Semisupervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
Later among the works it cites.
End-to-end neural coreference resolution
Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017 · 2017
Later among the works it cites.
A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling
Diego Marcheggiani, Anton Frolov, and Ivan Titov. 2017 · 2017
Later among the works it cites.
Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov. 2017 · 2017
Later among the works it cites.
Deep multitask learning for semantic dependency parsing
Hao Peng, Sam Thomson, and Noah A. Smith. 2017 · 2017
Later among the works it cites.
Frame-semantic parsing with softmax-margin segmental rnns and a syntactic scaffold
Swabha Swayamdipta, Sam Thomson, Chris Dyer, and Noah A. Smith. 2017 · 2017
Later among the works it cites.
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
Later among the works it cites.
Conll 2017 shared task: Multilingual parsing from raw text to universal dependencies
Daniel Zeman, Martin Popel, Milan Straka, Jan Hajic, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, et al. 2017 · 2017
Later among the works it cites.
Jointly predicting predicates and arguments in neural semantic role labeling
Luheng He, Kenton Lee, Omer Levy, and Luke Zettlemoyer. 2018 · 2018
Closest in time.
Learning structured text representations
Yang Liu and Mirella Lapata. 2018 · 2018
Closest in time.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Closest in time.
Deep semantic role labeling with self-attention
Zhixing Tan, Mingxuan Wang, Jun Xie, Yidong Chen, and Xiaodong Shi. 2018 · 2018
Closest in time.