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The state of art natural language processing systems relies on sizable training datasets to achieve high performance.
Unsupervised data augmentation
Qizhe Xie, Zihang Dai, Eduard H. Hovy, Minh-Thang Luong, and Quoc V. Le. 2019 · 1904
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Transformation invariance in pattern recognition-tangent distance and tangent propagation
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Long short-term memory
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Attention is all you need
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The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures
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Facebook fair’s WMT19 news translation task submission
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Data augmentation for spoken language understanding via joint variational generation
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Data manipulation: Towards effective instance learning for neural dialogue generation via learning to augment and reweight
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Adversarial example generation with syntactically controlled paraphrase networks
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An analysis of simple data augmentation for named entity recognition
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Don’t stop pretraining: Adapt language models to domains and tasks
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Seqmix: Augmenting active sequence labeling via sequence mixup
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A survey of data augmentation approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard H. Hovy. 2021 · 2021
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Data augmentation in a hybrid approach for aspect-based sentiment analysis
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