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Novel neural models have been proposed in recent years for learning under domain shift.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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Multitask learning: A knowledge-based source of inductive bias
Rich Caruana. 1993 · 1993
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An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani. 1994 · 1994
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Unsupervised Word Sense Disambiguation Rivaling Supervised Methods
David Yarowsky. 1995 · 1995
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Example selection for bootstrapping statistical parsers
Mark Steedman, Rebecca Hwa, Stephen Clark, Miles Osborne, Anoop Sarkar, Julia Hockenmaier, Paul Ruhlen, Steven Baker, and Jeremiah Crim. 2003 · 2003
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Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
Zhi-Hua Zhou and Ming Li. 2005 · 2005
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Semi-Supervised Learning Literature Survey
Xiaojin Zhu. 2005 · 2005
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Domain Adaptation with Structural Correspondence Learning
John Blitzer, Ryan McDonald, and Fernando Pereira. 2006 · 2006
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Semi-Supervised Learning , volume 1
Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien. 2006 · 2006
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Semisupervised learning for computational linguistics
Steven Abney. 2007 · 2007
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
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Instance weighting for domain adaptation in nlp
Jing Jiang and ChengXiang Zhai. 2007 · 2007
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Self-training for enhancement and domain adaptation of statistical parsers trained on small datasets
Roi Reichart and Ari Rappoport. 2007 · 2007
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Semi-supervised sequential labeling and segmentation using giga-word scale unlabeled data
Jun Suzuki and Hideki Isozaki. 2008 · 2008
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Introduction to semi-supervised learning
Xiaojin Zhu and Andrew B Goldberg. 2009 · 2009
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Simple semi-supervised training of part-of-speech taggers
Anders Søgaard. 2010 · 2010
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Domain adaptation for parsing
Barbara Plank. 2011 · 2011
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Overview of the 2012 shared task on parsing the web
Slav Petrov and Ryan McDonald. 2012 · 2012
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Modelling annotator bias with multi-task gaussian processes: An application to machine translation quality estimation
Trevor Cohn and Lucia Specia. 2013 · 2013
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Cited alongside, same era.
FLORS: Fast and Simple Domain Adaptation for Part-of-Speech Tagging
Tobias Schnabel and Hinrich Schütze. 2014 · 2014
Cited alongside, same era.
Open-domain name error detection using a multi-task rnn
Hao Cheng, Hao Fang, and Mari Ostendorf. 2015 · 2015
Cited alongside, same era.
The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel. 2015 · 2015
Cited alongside, same era.
Bi-transferring deep neural networks for domain adaptation
Guangyou Zhou, Zhiwen Xie, Jimmy Xiangji Huang, and Tingting He. 2016 · 2016
Later among the works it cites.
Stronger baselines for trustable results in neural machine translation
Michael Denkowski and Graham Neubig. 2017 · 2017
Later among the works it cites.
Deep Biaffine Attention for Neural Dependency Parsing
Timothy Dozat and Christopher D. Manning. 2017 · 2017
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Model transfer for tagging low-resource languages using a bilingual dictionary
Meng Fang and Trevor Cohn. 2017 · 2017
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To normalize, or not to normalize: The impact of normalization on part-of-speech tagging
Rob van der Goot, Barbara Plank, and Malvina Nissim. 2017 · 2017
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Minh-Thang Luong, Quoc V Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 2015 · 2015
Cited alongside, same era.
Computational linguistics and deep learning
Christopher D Manning. 2015 · 2015
Cited alongside, same era.
Online Updating of Word Representations for Part-of-Speech Tagging
Wenpeng Yin, Tobias Schnabel, and Hinrich Schütze. 2015 · 2015
Cited alongside, same era.
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan. 2016 · 2016
Cited alongside, same era.
Learning when to trust distant supervision: An application to low-resource pos tagging using cross-lingual projection
Meng Fang and Trevor Cohn. 2016 · 2016
Cited alongside, same era.
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Francois Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2016
Cited alongside, same era.
Simple and accurate dependency parsing using bidirectional lstm feature representations
Eliyahu Kiperwasser and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
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Deep semantic role labeling: What works and what’s next
Luheng He, Kenton Lee, Mike Lewis, and Luke Zettlemoyer. 2017 · 2017
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Snapshot Ensembles: Train 1, get M for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, and Kilian Q. Weinberger. 2017 · 2017
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Adversarial adaptation of synthetic or stale data
Young-Bum Kim, Karl Stratos, and Dongchan Kim. 2017 · 2017
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Temporal Ensembling for Semi-Supervised Learning
Samuli Laine and Timo Aila. 2017 · 2017
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Adversarial multi-task learning for text classification
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2017 · 2017
Later among the works it cites.
On the State of the Art of Evaluation in Neural Language Models
Gábor Melis, Chris Dyer, and Phil Blunsom. 2017 · 2017
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Dynet: The dynamic neural network toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, et al. 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
Later among the works it cites.
Learning what to share between loosely related tasks
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2017 · 2017
Later among the works it cites.
Asymmetric Tri-training for Unsupervised Domain Adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada. 2017 · 2017
Later among the works it cites.
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
Isabelle Augenstein, Sebastian Ruder, and Anders Søgaard. 2018 · 2018
Closest in time.
Universal Language Model Fine-tuning for Text Classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
Closest in time.