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

Various bias can be learned by LM when it's trained on the data containing human discrimination. Researchers have tried using fixed rules or specific models to eliminate bias and used it for mitigating bias contained in the LM.

DA in Mitigating Bias

Fixing class imbalance typically involves a combination of undersampling and oversampling.

DA in Fixing Class Imbalance

DA methods can ease few-shot learning by adding more examples for novel classes introduced in the few-shot phase. 

DA in Few-shot Learning

Adversarial examples can be generated using innocuous label-preserving transformations (e.g. paraphrasing) that fool state-of-the-art NLP models

DA in AVEs

It's widely used in Low-Resource NMT, Mitigating Bias, Fixing Class Imbalance, Few-shot Learning, and Adversarial Examples (AVEs).

University of Michigan - Ann Arbor

Data augmentation (DA) refers to strategies for increasing the diversity of training examples without explicitly collecting new data. Related methods are first explored in computer vision (CV), and DA’s adaptation for natural language processing (NLP) seems secondary and comparatively underexplored.

Data Augmentation in NLP

https://arxiv-download.xixiaoyao.cn/pdf/2105.03075.pdf

A Survey of Data Augmentation Approaches for NLP

The trade-off is often based on development cost and data variation.

Trade-off of DA Tricks in NLP

The typical explanation of DA is that it serves as regularization. Some researcher believes that it's insufficient. Existing work on this topic is mainly surface-level, and rarely investigates the theoretical underpinnings and principles.

Interpretation of DA in NLP

Rule-based data augmentation is to apply basic changes to the dataset which enlarges the divergence that the neural network sees.

Rule-based Data Augmentation

This class of techniques is also sometimes referred to as Mixed Sample Data Augmentation (MSDA). In the nlp area, related work often interpolates inner components, more general mixing schemes, and add adversaries.

Example Interpolation in NLP

Seq2seq and language models have also been used for DA. Techniques include applying selective gates, generating different samples, and augmenting word representations with a context-sensitive attention-based mixture. 

Model-Based DA Techniques in NLP

1. Summarization: Common Crawl, WikiRef, QMDSCNN, QMDSIR
2. QA: XLDA, QANet
3. Sequence Tagging Tasks: DAGA, dependency tree morphing, SEQMIX
4. Parsing Tasks: Data Recombination, GRAPPA, compositionality
5. Grammatical Error Correction: Use annotated data or confusion sets
6. Neural Machine Translation: BackTranslation, SwitchOut, Soft Contextual, Data Diversification
7. Data-to-Text NLG: perturbing language game scores or inputs, or adding noise.
8. Open-Ended & Conditional Generation: GEAUNG
9. Dialogue: LIGHTWEIGHT AUGMENTATION, diversity rank, MADA, spoken language understanding
10. Multimodal Tasks: Augment parallel data seperately and reinforce the alignment.

Downstream NLP Tasks applying DA Techniques 

1. Dissonance between empirical novelties and theoretical narrative
2. Minimal benefit for pretrained models on indomain data
3. Multimodal challenges
4. Span-based tasks
5. Working in specialized domains
6. Working with low-resource languages
7. More vision-inspired techniques
8. Self-supervised learning
9. Offline versus online data augmentation
10. Lack of unification
11. Good data augmentation practices

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