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Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training.
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SwitchOut: an efficient data augmentation algorithm for neural machine translation
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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
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Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, and Andrew Y. Ng. 2017 · 2017
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Edinburgh neural machine translation systems for WMT 16
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016b
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EDA: Easy data augmentation techniques for boosting performance on text classification tasks
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