Fetching the paper…
Reading the bibliography…
This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix.
Cross-lingual language model pretraining
Guillaume Lample and Alexis Conneau. 2019 · 1901
Earlier work this paper cites.
EDA: easy data augmentation techniques for boosting performance on text classification tasks
Jason W. Wei and Kai Zou. 2019 · 1901
Earlier work this paper cites.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le. 2019 · 1904
Earlier work this paper cites.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel. 2019 · 1905
Earlier work this paper cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. 2019 · 1905
Earlier work this paper cites.
Variational pretraining for semi-supervised text classification
Suchin Gururangan, Tam Dang, Dallas Card, and Noah A. Smith. 2019 · 1906
Earlier work this paper cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019b · 1906
Earlier work this paper cites.
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
Earlier work this paper cites.
Graphmix: Regularized training of graph neural networks for semi-supervised learning
Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, and Jian Tang. 2019c · 1909
Earlier work this paper cites.
Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2019 · 1993
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio. 2004 · 2004
Earlier work this paper cites.
Importance of semantic representation: Dataless classification
Ming-Wei Chang, Lev Ratinov, Dan Roth, and Vivek Srikumar. 2008 · 2008
Earlier work this paper cites.
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
Earlier work this paper cites.
Dbpedia for nlp: A multilingual cross-domain knowledge base
Pablo N. Mendes, Max Jakob, and Christian Bizer. 2012 · 2012
Earlier work this paper cites.
Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee. 2013 · 2013
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Cited alongside, same era.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015 · 2015
Cited alongside, same era.
Weakly supervised role identification in teamwork interactions
Diyi Yang, Miaomiao Wen, and Carolyn Rose. 2015 · 2015
Cited alongside, same era.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
Cited alongside, same era.
Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio. 2016 · 2016
Cited alongside, same era.
Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 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.
Weakly-supervised neural text classification
Yu Meng, Jiaming Shen, Chao Zhang, and Jiawei Han. 2018 · 2018
Later among the works it cites.
An exploration of three lightly-supervised representation learning approaches for named entity classification
Ajay Nagesh and Mihai Surdeanu. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Language models are unsupervised multitask learners
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Samuli Laine and Timo Aila. 2016 · 2016
Cited alongside, same era.
Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
Cited alongside, same era.
Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow. 2017 · 2017
Cited alongside, same era.
The effectiveness of data augmentation in image classification using deep learning
Luis Perez and Jason Wang. 2017 · 2017
Cited alongside, same era.
Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola. 2017 · 2017
Cited alongside, same era.
Improved variational autoencoders for text modeling using dilated convolutions
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick. 2017 · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz. 2017 · 2017
Cited alongside, same era.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
Pooled contextualized embeddings for named entity recognition
Alan Akbik, Tanja Bergmann, and Roland Vollgraf. 2019 · 2019
Later among the works it cites.
Incorporating structured commonsense knowledge in story completion
Jiaao Chen, Jianshu Chen, and Zhou Yu. 2019 · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
What does BERT learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
Later among the works it cites.
Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation
Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, and Partha Talukdar. 2019 · 2019
Later among the works it cites.
Exploration of noise strategies in semi-supervised named entity classification
Pooja Lakshmi Narayan, Ajay Nagesh, and Mihai Surdeanu. 2019 · 2019
Later among the works it cites.
Let’s make your request more persuasive: Modeling persuasive strategies via semi-supervised neural nets on crowdfunding platforms
Diyi Yang, Jiaao Chen, Zichao Yang, Dan Jurafsky, and Eduard Hovy. 2019a · 2019
Later among the works it cites.
Semi-supervised Models via Data Augmentation for Classifying Interactive Affective Responses
Jiaao Chen, Yuwei Wu, and Diyi Yang. 2020 · 2020
Closest in time.
Learning from labeled and unlabeled data: An empirical study across techniques and domains
Nitesh V. Chawla and Grigoris I. Karakoulas. 2011 · 2047
Closest in time.