Universal Dependency Parsing from Scratch

On the shortest arborescence of a directed graph

Yoeng-Jin Chu and Tseng-Hong Liu. 1965 · 1965

Earlier work this paper cites.

Optimum branchings

Jack Edmonds. 1967 · 1967

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.

Neural machine translation by jointly learning to align and translate

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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.

Highway networks

Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015 · 2015

Earlier work this paper cites.

Dropout as a Bayesian approximation: Representing model uncertainty in deep learning

Yarin Gal and Zoubin Ghahramani. 2016 · 2016

Earlier work this paper cites.

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016

Cited alongside, same era.

Enriching word vectors with subword information

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017

Cited alongside, same era.

Improved neural machine translation with a syntax-aware encoder and decoder

Huadong Chen, Shujian Huang, David Chiang, and Jiajun Chen. 2017 · 2017

Cited alongside, same era.

Stanford’s graph-based neural dependency parser at the CoNLL 2017 Shared Task

Timothy Dozat, Peng Qi, and Christopher D. Manning. 2017 · 2017

Cited alongside, same era.

Encoding sentences with graph convolutional networks for semantic role labeling

Diego Marcheggiani and Ivan Titov. 2017 · 2017

Cited alongside, same era.

Multiresolution recurrent neural networks: An application to dialogue response generation

Towards better UD parsing: Deep contextualized word embeddings, ensemble, and treebank concatenation

Wanxiang Che, Yijia Liu, Yuxuan Zheng Bo Wang, and Ting Liu. 2018 · 2018

Later among the works it cites.

Universal language model fine-tuning for text classification

Jeremy Howard and Sebastian Ruder. 2018 · 2018

Later among the works it cites.

Deep contextualized word representations

Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018

Later among the works it cites.

On the convergence of Adam and beyond

Sashank J Reddi, Satyen Kale, and Sanjiv Kumar. 2018 · 2018

Later among the works it cites.

CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

Daniel Zeman, Jan Hajič, Martin Popel, Martin Potthast, Milan Straka, Filip Ginter, Joakim Nivre, and Slav Petrov. 2018 · 2018

Later among the works it cites.

Graph convolution over pruned dependency trees improves relation extraction