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Interpolation-based Data Augmentation (DA) methods (Mixup) linearly interpolate the inputs and labels of two or more training examples.
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Local additivity based data augmentation for semi-supervised NER
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SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
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Seqmix: Augmenting active sequence labeling via sequence mixup
Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020 · 2010
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WALS Online
Matthew S. Dryer and Martin Haspelmath, editors. 2013 · 2013
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Question-answer driven semantic role labeling: Using natural language to annotate natural language
Luheng He, Mike Lewis, and Luke Zettlemoyer. 2015 · 2015
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Xuezhe Ma and Eduard H. Hovy. 2016 · 2016
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Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018
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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 · 2019
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EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 2019
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Sequence-level mixed sample data augmentation
Demi Guo, Yoon Kim, and Alexander Rush. 2020 · 2020
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Soft gazetteers for low-resource named entity recognition
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Data augmentation via dependency tree morphing for low-resource languages
Gözde Gül Şahin and Mark Steedman. 2018 · 2018
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Xinyi Wang, Hieu Pham, Zihang Dai, and Graham Neubig. 2018 · 2018
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Jiaao Chen, Zichao Yang, and Diyi Yang. 2020b
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Shruti Rijhwani, Shuyan Zhou, Graham Neubig, and Jaime Carbonell. 2020 · 2020
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Extending multilingual BERT to low-resource languages
Zihan Wang, Karthikeyan K, Stephen Mayhew, and Dan Roth. 2020 · 2020
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MasakhaNER: Named Entity Recognition for African Languages
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