What are the challenges of subword tokenization in NLP?

What are the challenges of subword tokenization in NLP?

Subword tokenization helps in handling out-of-vocabulary words, but it has challenges like increased complexity and ambiguity.

Can you explain how it increases complexity?

Sure! It complicates the model's training as it needs to learn representations for many subword units instead of whole words.

What about ambiguity? How does that affect understanding?

Ambiguity arises when the same subword can represent different meanings, making it harder for models to grasp context.

Are there any solutions to these challenges?

Yes, techniques like byte pair encoding and using context-aware embeddings can help mitigate these issues.