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Self-training is a useful strategy for semi-supervised learning, leveraging raw texts for enhancing model performances.
Introduction to the conll-2002 shared task: Language-independent named entity recognition
Erik F Tjong Kim Sang · 2002
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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Europarl: A parallel corpus for statistical machine translation
Philipp Koehn. 2005 · 2005
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Self-training and co-training applied to spanish named entity recognition
Zornitsa Kozareva, Boyan Bonev, and Andres Montoyo. 2005 · 2005
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Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman. 2005 · 2005
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Effective self-training for parsing
David McClosky, Eugene Charniak, and Mark Johnson. 2006 · 2006
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Weakly supervised learning for hedge classification in scientific literature
Ben Medlock and Ted Briscoe. 2007 · 2007
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Semi-supervised learning for part-of-speech tagging of mandarin transcribed speech
Wen Wang, Zhongqiang Huang, and Mary Harper. 2007 · 2007
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Cross-task knowledge-constrained self training
Hal Daumé III. 2008 · 2008
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When is self-training effective for parsing?
David McClosky, Eugene Charniak, and Mark Johnson. 2008 · 2008
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Improving a simple bigram hmm part-of-speech tagger by latent annotation and self-training
Zhongqiang Huang, Vladimir Eidelman, and Mary Harper. 2009 · 2009
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Self-training pcfg grammars with latent annotations across languages
Zhongqiang Huang and Mary Harper. 2009 · 2009
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Semi-supervised sequence labeling with self-learned features
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko Sadamasa, Koray Kavukcuoglu, and Jason Weston. 2009 · 2009
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Self-training without reranking for parser domain adaptation and its impact on semantic role labeling
Kenji Sagae. 2010 · 2010
Cited alongside, same era.
Co-training for domain adaptation
Minmin Chen, Kilian Q Weinberger, and John Blitzer. 2011 · 2011
Cited alongside, same era.
Guided self training for sentiment classification
Brett Drury, Luis Torgo, and Jose Joao Almeida. 2011 · 2011
Cited alongside, same era.
Self-training from labeled features for sentiment analysis
Yulan He and Deyu Zhou. 2011 · 2011
Cited alongside, same era.
Training a parser for machine translation reordering
Jason Katz-Brown, Slav Petrov, Ryan McDonald, Franz Och, David Talbot, Hiroshi Ichikawa, Masakazu Seno, and Hideto Kazawa. 2011 · 2011
Cited alongside, same era.
Multisource domain adaptation and its application to early detection of fatigue
Rita Chattopadhyay, Qian Sun, Wei Fan, Ian Davidson, Sethuraman Panchanathan, and Jieping Ye. 2012 · 2012
Deep reinforcement learning for mention-ranking coreference models
Kevin Clark and Christopher D Manning. 2016 · 2016
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Learning to translate in real-time with neural machine translation
Jiatao Gu, Graham Neubig, Kyunghyun Cho, and Victor OK Li. 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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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. 2016 · 2016
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Cited alongside, same era.
Self-training with selection-by-rejection
Yan Zhou, Murat Kantarcioglu, and Bhavani Thuraisingham. 2012 · 2012
Cited alongside, same era.
Don’t until the final verb wait: Reinforcement learning for simultaneous machine translation
Alvin Grissom II, He He, Jordan Boyd-Graber, John Morgan, and Hal Daumé III. 2014 · 2014
Cited alongside, same era.
Generating text with deep reinforcement learning
Hongyu Guo. 2015 · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. 2015 · 2015
Cited alongside, same era.
Domain adaptation for learning from label proportions using self-training
Ehsan Mohammady Ardehaly and Aron Culotta. 2016 · 2016
Cited alongside, same era.
An actor-critic algorithm for sequence prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2016 · 2016
Cited alongside, same era.
Pei-Hao Su, Milica Gasic, Nikola Mrksic, Lina Rojas-Barahona, Stefan Ultes, David Vandyke, Tsung-Hsien Wen, and Steve Young. 2016 · 2016
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Predicting the effectiveness of self-training: Application to sentiment classification
Vincent Van Asch and Walter Daelemans. 2016 · 2016
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Dual learning for machine translation
Yingce Xia, Di He, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma. 2016 · 2016
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Learning to compose words into sentences with reinforcement learning
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Wang Ling. 2016 · 2016
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Learning how to active learn: A deep reinforcement learning approach
Meng Fang, Yuan Li, and Trevor Cohn. 2017 · 2017
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Self-training for multi-target regression with tree ensembles
Jurica Levati, Michelangelo Ceci, Dragi Kocev, and Sao Deroski. 2017 · 2017
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Split and rephrase
Shashi Narayan, Claire Gardent, Shay B. Cohen, and Anastasia Shimorina. 2017 · 2017
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Sentence simplification with deep reinforcement learning
Xingxing Zhang and Mirella Lapata. 2017 · 2017
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