What are the main challenges in NLP when using neural networks?

What are the main challenges in NLP when using neural networks?

Some key challenges include data sparsity, ambiguity in language, and the need for large labeled datasets.

How does data sparsity affect neural network performance in NLP?

Data sparsity can lead to overfitting, as the model may not generalize well to unseen data due to limited examples.

What strategies can be used to overcome these challenges?

Techniques like transfer learning, data augmentation, and using pre-trained models can help mitigate these issues.

Are there specific neural network architectures that perform better in NLP?

Yes, architectures like Transformers and RNNs have shown significant improvements in various NLP tasks.