What are the main challenges in data augmentation for NLP?
An example of an AI chatbot conversation about challenges in data augmentation for NLP.
What are the main challenges in data augmentation for NLP?
Some key challenges include maintaining semantic meaning, ensuring diversity, and avoiding overfitting.
Can you explain how semantic meaning is affected?
When augmenting data, it's crucial that the new examples still convey the same meaning as the original data.
What techniques can help overcome these challenges?
Techniques like back-translation, synonym replacement, and contextual embeddings can help address these issues.
Are there any specific tools you recommend for data augmentation?
Yes, tools like TextAttack, nlpaug, and Hugging Face's Transformers library are great for NLP data augmentation.
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