Fetching the paper…
Reading the bibliography…
Recent works have empirically shown the effectiveness of data augmentation (DA) in NLP tasks, especially for those suffering from data scarcity.
Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 1901
Earlier work this paper cites.
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim. 2019 · 1905
Earlier work this paper cites.
Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2019 · 1906
Earlier work this paper cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019 · 1906
Earlier work this paper cites.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1911
Earlier work this paper cites.
Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
Earlier work this paper cites.
Jiaao Chen, Zichao Yang, and Diyi Yang. 2020 · 2004
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Model-portability experiments for textual temporal analysis
Oleksandr Kolomiyets, Steven Bethard, and Marie Francine Moens. 2011 · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Return of the devil in the details: Delving deep into convolutional nets
Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015 · 2015
Earlier work this paper cites.
That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets
William Yang Wang and Diyi Yang. 2015 · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Earlier work this paper cites.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Semeval-2017 task 4: Sentiment analysis in twitter
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017 · 2017
Earlier work this paper cites.
Data noising as smoothing in neural network language models
Ziang Xie, Sida I Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, and Andrew Y Ng. 2017 · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2017 · 2017
Cited alongside, same era.
Text data augmentation made simple by leveraging nlp cloud apis
Claude Coulombe. 2018 · 2018
Cited alongside, same era.
Large scale crowdsourcing and characterization of twitter abusive behavior
Antigoni Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, and Nicolas Kourtellis. 2018 · 2018
Cited alongside, same era.
Contextual augmentation: Data augmentation by words with paradigmatic relations
Do not have enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, and Naama Zwerdling. 2020 · 2020
Later among the works it cites.
Data boost: Text data augmentation through reinforcement learning guided conditional generation
Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, and Soroush Vosoughi. 2020 · 2020
Later among the works it cites.
Learn to augment: Joint data augmentation and network optimization for text recognition
Canjie Luo, Yuanzhi Zhu, Lianwen Jin, and Yongpan Wang. 2020 · 2020
Later among the works it cites.
Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp
John X Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020 · 2020
Later among the works it cites.
Ssmba: Self-supervised manifold based data augmentation for improving out-of-domain robustness
Nathan Ng, Kyunghyun Cho, and Marzyeh Ghassemi. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sosuke Kobayashi. 2018 · 2018
Cited alongside, same era.
Fixing weight decay regularization in adam
Ilya Loshchilov and Frank Hutter. 2018 · 2018
Cited alongside, same era.
Adversarial over-sensitivity and over-stability strategies for dialogue models
Tong Niu and Mohit Bansal. 2018 · 2018
Cited alongside, same era.
Data augmentation via dependency tree morphing for low-resource languages
Gözde Gül Şahin and Mark Steedman. 2018 · 2018
Cited alongside, same era.
Semeval-2018 task 3: Irony detection in english tweets
Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018 · 2018
Cited alongside, same era.
Qanet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le. 2018 · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le. 2019 · 2019
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.
Named entity recognition for social media texts with semantic augmentation
Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, and Bo Dai. 2020 · 2020
Later among the works it cites.
Textual data augmentation for efficient active learning on tiny datasets
Husam Quteineh, Spyridon Samothrakis, and Richard Sutcliffe. 2020 · 2020
Later among the works it cites.
G-daug: Generative data augmentation for commonsense reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, and Doug Downey. 2020 · 2020
Later among the works it cites.
Data augmentation with adversarial training for cross-lingual nli
Xin Luna Dong, Yaxin Zhu, Zuohui Fu, Dongkuan Xu, and Gerard de Melo. 2021 · 2021
Later among the works it cites.
A survey of data augmentation approaches for nlp
Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021 · 2021
Later among the works it cites.
Aeda: An easier data augmentation technique for text classification
Akbar Karimi, Leonardo Rossi, and Andrea Prati. 2021 · 2021
Later among the works it cites.
Substructure substitution: Structured data augmentation for nlp
Haoyue Shi, Karen Livescu, and Kevin Gimpel. 2021 · 2021
Later among the works it cites.
Better robustness by more coverage: Adversarial and mixup data augmentation for robust finetuning
Chenglei Si, Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, and Maosong Sun. 2021 · 2021
Later among the works it cites.
Seqattack: On adversarial attacks for named entity recognition
Walter Simoncini and Gerasimos Spanakis. 2021 · 2021
Later among the works it cites.
Text augmentation in a multi-task view
Jason Wei, Chengyu Huang, Shiqi Xu, and Soroush Vosoughi. 2021 · 2021
Later among the works it cites.
Weakly-supervised text classification based on keyword graph
Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, and Shuigeng Zhou. 2021 · 2021
Later among the works it cites.
Virtual data augmentation: A robust and general framework for fine-tuning pre-trained models
Kun Zhou, Wayne Xin Zhao, Sirui Wang, Fuzheng Zhang, Wei Wu, and Ji-Rong Wen. 2021 · 2021
Later among the works it cites.
Learnda: Learnable knowledge-guided data augmentation for event causality identification
Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, and Yuguang Chen. 2021 · 2021
Later among the works it cites.