Feature space DA approaches generate augmented examples in the model’s feature space rather than input data.

Feature Space DA

It's a set of token-level random perturbation operations including random insertion, deletion, and swap. It shows improved performance on many text classification tasks. 

Easy Data Augmentation (EDA)

UDA show how supervised DA methods can be exploited for unsupervised data through consistency training on $(x, DA(x))$ pairs.

Unsupervised Data Augmentation (UDA)

For dependency-annotated sentences, children of the same parent are swapped or some deleted.

Dependency Tree Morphing

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

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

Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. 

Data Augmentation

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

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

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

Applications of DA 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

Challenges & Future Directions of DA in NLP

Interpolation is the process of estimating unknown values that fall between known values.

Interpolation

It mixes data and labels of real samples in the exact same way to create more available data.

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