Fetching the paper…
Reading the bibliography…
While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited.
Semi-supervised learning for neural machine translation
Yong Cheng, Wei Xu, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 1974
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.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
On using monolingual corpora in neural machine translation
Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, Loic Barrault, Huei-Chi Lin, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. 2015 · 2015
Earlier work this paper cites.
Dual learning for machine translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma. 2016 · 2016
Cited alongside, same era.
Unsupervised neural machine translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho. 2017 · 2017
Cited alongside, same era.
Data augmentation for low-resource neural machine translation
Marzieh Fadaee, Arianna Bisazza, and Christof Monz. 2017 · 2017
Cited alongside, same era.
Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017 · 2017
Cited alongside, same era.
Unsupervised machine translation using monolingual corpora only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2017 · 2017
Cited alongside, same era.
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
Later among the works it cites.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le. 2018 · 2018
Later among the works it cites.
Iterative back-translation for neural machine translation
Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, and Trevor Cohn. 2018 · 2018
Later among the works it cites.
Contextual augmentation: Data augmentation by words with paradigmatic relations
Sosuke Kobayashi. 2018 · 2018
Later among the works it cites.
Switchout: an efficient data augmentation algorithm for neural machine translation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015a
Cited in the paper.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015b
Cited in the paper.
Xinyi Wang, Hieu Pham, Zihang Dai, and Graham Neubig. 2018 · 2018
Later among the works it cites.
Conditional bert contextual augmentation
Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, and Songlin Hu. 2018 · 2018
Later among the works it cites.