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Training deep neural networks (DNNs) under weak supervision has attracted increasing research attention as it can significantly reduce the annotation cost.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng. 2019 · 1928
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Learning from noisy examples
Dana Angluin and Philip Laird. 1988 · 1988
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky. 1995 · 1995
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Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Sang and Fien De Meulder. 2003 · 2003
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
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Adaptive self-training for few-shot neural sequence labeling
Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, and Ahmed Hassan Awadallah. 2020 · 2010
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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A ctd-pfizer collaboration: manual curation of 88 000 scientific articles text mined for drug-disease and drug-phenotype interactions
Allan Peter Davis, Thomas C. Wiegers, Phoebe M. Roberts, Benjamin L. King, Jean M. Lay, Kelley Lennon-Hopkins, Daniela Sciaky, Robin J. Johnson, Heather Keating, Nigel Greene, Robert Hernandez, Kevin J. McConnell, Ahmed Enayetallah, and Carolyn J. Mattingly. 2013 · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al. 2013 · 2013
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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
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Training deep neural-networks based on unreliable labels
Alan Joseph Bekker and Jacob Goldberger. 2016 · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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Learning from binary labels with instance-dependent corruption
Aditya Krishna Menon, Brendan van Rooyen, and Nagarajan Natarajan. 2016 · 2016
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Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven. 2017 · 2017
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Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. 2017 · 2017
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Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, and Christopher Ré. 2017 · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017 · 2017
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Bond: Bert-assisted open-domain named entity recognition with distant supervision
Chen Liang, Yue Yu, Haoming Jiang, Siawpeng Er, Ruijia Wang, Tuo Zhao, and Chao Zhang. 2020 · 2020
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Named entity recognition without labelled data: A weak supervision approach
Pierre Lison, Jeremy Barnes, Aliaksandr Hubin, and Samia Touileb. 2020 · 2020
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Uncertainty-aware self-training for few-shot text classification
Subhabrata Mukherjee and Ahmed Hassan Awadallah. 2020 · 2020
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Denoising multi-source weak supervision for neural text classification
Wendi Ren, Yinghao Li, Hanting Su, David Kartchner, Cassie Mitchell, and Chao Zhang. 2020 · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard H. Hovy, and Quoc V. Le. 2020 · 2020
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama. 2018 · 2018
Cited alongside, same era.
Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel. 2018 · 2018
Cited alongside, same era.
Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. 2018 · 2018
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Learning from rules generalizing labeled exemplars
Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, and Sunita Sarawagi. 2020 · 2020
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Fast and three-rious: Speeding up weak supervision with triplet methods
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, and Christopher Ré. 2020 · 2020
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Wenxuan Zhou, Hongtao Lin, Bill Yuchen Lin, Ziqi Wang, Junyi Du, Leonardo Neves, and Xiang Ren. 2020 · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le. 2020 · 2020
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An instance-dependent simulation framework for learning with label noise
Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, and Dong Yin. 2021 · 2021
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Self-training with weak supervision
Giannis Karamanolakis, Subhabrata (Subho) Mukherjee, Guoqing Zheng, and Ahmed H. Awadallah. 2021 · 2021
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Meta pseudo labels
Hieu Pham, Zihang Dai, Qizhe Xie, and Quoc V. Le. 2021 · 2021
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Fine-tuning pre-trained language model with weak supervision: A contrastive-regularized self-training approach
Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, and Chao Zhang. 2021 · 2021
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WRENCH: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner. 2021 · 2021
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Bert learns to teach: Knowledge distillation with meta learning
Wangchunshu Zhou, Canwen Xu, and Julian McAuley. 2022 · 2022
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Is BERT robust to label noise? A study on learning with noisy labels in text classification
Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai, David Ifeoluwa Adelani, and Dietrich Klakow. 2022 · 2022
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