What are the applications of data augmentation in NLP?

What are the applications of data augmentation in NLP?

Data augmentation in NLP enhances training data by creating variations, improving model robustness and performance.

Can you give me some specific examples of data augmentation techniques?

Sure! Techniques include synonym replacement, back-translation, and random insertion of words.

How does data augmentation help in reducing overfitting?

By providing more diverse training examples, data augmentation helps models generalize better, reducing overfitting.

Are there any limitations to using data augmentation?

Yes, excessive augmentation can lead to noise in the data, potentially harming model performance.