Effective DA methods for multiple modalities have been challenging. Many works focus on augmenting a single modality or multiple ones separately.

Multimodal Challenges of DA in NLP

It offers unique DA challenges as there are typically many correlated classification decisions.

Why Span-based Tasks are Important for DA in NLP 

DA techniques successful in the high resource scenario may not be effective for low resource languages that are of a different language family or very distinctive in linguistic and typological terms. 

Why low-resource languages can be challenging for using DA in NLP

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

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

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.

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