1. Decrease the requirement of in-depth linguistic insight.
2. How to achieve cross-tasks data augmentation?

University of Michigan - Ann Arbor

New instances can be obtained based on existing ones by modifying the features with transformations that do not change the label. To go beyond the token level and add more diversity to the augmented sentences, data augmentation can also be performed on sentence parts.

Data Augmentation for Low-Resource NLP

https://aclanthology.org/2021.naacl-main.201.pdf

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios


Low-resource languages are an important and challenging application for DA, typically for neural machine translation (NMT). Using WordNet, backtranslation and etc is proved to be successful on Low-Resource NMT.

DA in Low-Resource NMT

Open Issues of Applying Data Augmentation to Low-resource NLP

1. Distant & weak supervision.
2. Cross-Lingual Annotation Projections
3. Learning with Noisy Labels
4. Non-Expert Support

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