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Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text.
Some methods of speeding up the convergence of iteration methods
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Probability of error of some adaptive pattern-recognition machines
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Unsupervised word sense disambiguation rivaling supervised methods
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Multitask learning
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
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Framewise phoneme classification with bidirectional LSTM and other neural network architectures
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A co-regularization approach to semi-supervised learning with multiple views
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Tri-training: Exploiting unlabeled data using three classifiers
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Ontonotes: the 90% solution
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Effective self-training for parsing
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CCGbank: a corpus of CCG derivations and dependency structures extracted from the Penn treebank
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A unified architecture for natural language processing: deep neural networks with multitask learning
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What is the best multi-stage architecture for object recognition?
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Learning multiple layers of features from tiny images
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Convolutional networks and applications in vision
Yann LeCun, Koray Kavukcuoglu, and Clément Farabet. 2010 · 2010
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
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Improving neural networks by preventing co-adaptation of feature detectors
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. 2013 · 2013
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A survey on multi-view learning
Chang Xu, Dacheng Tao, and Chao Xu. 2013 · 2013
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Learning with pseudo-ensembles
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Sequence to sequence learning with neural networks
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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The IWSLT 2015 evaluation campaign
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le. 2015 · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Skip-thought vectors
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Stanford neural machine translation systems for spoken language domains
Minh-Thang Luong and Christopher D. Manning. 2015 · 2015
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
What do recurrent neural network grammars learn about syntax?
Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, Graham Neubig, and Noah A. Smith. 2017 · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila. 2017 · 2017
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In-order transition-based constituent parsing
Jiangming Liu and Yue Zhang. 2017 · 2017
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Empower sequence labeling with task-aware neural language model
Liyuan Liu, Jingbo Shang, Frank Xu, Xiang Ren, Huan Gui, Jian Peng, and Jiawei Han. 2017 · 2017
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Neural machine translation (seq2seq) tutorial
Minh-Thang Luong, Eugene Brevdo, and Rui Zhao. 2017 · 2017
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Neural probabilistic model for non-projective mst parsing
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Named entity recognition with bidirectional LSTM-CNNs
Jason PC Chiu and Eric Nichols. 2016 · 2016
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Parsing as language modeling
Do Kook Choe and Eugene Charniak. 2016 · 2016
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Learning distributed representations of sentences from unlabelled data
Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016 · 2016
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Sequence-level knowledge distillation
Yoon Kim and Alexander M. Rush. 2016 · 2016
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
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LSTM CCG parsing
Mike Lewis, Kenton Lee, and Luke Zettlemoyer. 2016 · 2016
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Xuezhe Ma and Eduard Hovy. 2017 · 2017
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Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017 · 2017
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Adversarial dropout for supervised and semi-supervised learning
Sungrae Park, Jun-Keon Park, Su-Jin Shin, and Il-Chul Moon. 2017 · 2017
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Deep multitask learning for semantic dependency parsing
Hao Peng, Sam Thomson, and Noah A. Smith. 2017 · 2017
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Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton. 2017 · 2017
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Semi-supervised sequence tagging with bidirectional language models
Matthew E Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 2017 · 2017
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Unsupervised pretraining for sequence to sequence learning
Prajit Ramachandran, Peter J Liu, and Quoc V Le. 2017 · 2017
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Semi-supervised multitask learning for sequence labeling
Marek Rei. 2017 · 2017
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Reporting score distributions makes a difference: Performance study of LSTM-networks for sequence tagging
Nils Reimers and Iryna Gurevych. 2017 · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder. 2017 · 2017
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Fast and accurate sequence labeling with iterated dilated convolutions
Emma Strubell, Patrick Verga, David Belanger, and Andrew McCallum. 2017 · 2017
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Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola. 2017 · 2017
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Huijia Wu, Jiajun Zhang, and Chengqing Zong. 2017 · 2017
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Born again neural networks
Tommaso Furlanello, Zachary C Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar. 2018 · 2018
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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Stack-pointer networks for dependency parsing
Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig, and Eduard Hovy. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Strong baselines for neural semi-supervised learning under domain shift
Sebastian Ruder and Barbara Plank. 2018 · 2018
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Learning general purpose distributed sentence representations via large scale multi-task learning
Sandeep Subramanian, Adam Trischler, Yoshua Bengio, and Christopher J Pal. 2018 · 2018
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Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer
Vikas Verma, Alex Lamb, Christopher Beckham, Aaron Courville, Ioannis Mitliagkis, and Yoshua Bengio. 2018 · 2018
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Improving the improved training of Wasserstein GANs
Xiang Wei, Zixia Liu, Liqiang Wang, and Boqing Gong. 2018 · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2018 · 2018
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