What is data augmentation in NLP?
An example of an AI chatbot conversation about data augmentation techniques for NLP tasks.
What is data augmentation in NLP?
Data augmentation in NLP refers to techniques used to increase the diversity of training data without collecting new data. This can help improve model performance.
Can you give me some examples of data augmentation techniques?
Sure! Common techniques include synonym replacement, back-translation, and random insertion of words.
How does back-translation work?
Back-translation involves translating a sentence to another language and then translating it back to the original language, which can create variations.
What are the benefits of using data augmentation?
Benefits include improved model robustness, reduced overfitting, and better generalization to unseen data.
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