What are the advantages of contextual embeddings in NLP?

What are the advantages of contextual embeddings in NLP?

Contextual embeddings capture the meaning of words based on their context, improving understanding in NLP tasks.

Can you give me an example of how they work?

Sure! In the sentence 'The bank can refuse to lend money,' the word 'bank' is understood differently than in 'I went to the bank to fish.' Contextual embeddings help differentiate these meanings.

How do they compare to traditional embeddings?

Traditional embeddings assign a fixed vector to each word, while contextual embeddings generate dynamic vectors based on surrounding words, leading to better performance in tasks like sentiment analysis.

What are some popular models that use contextual embeddings?

Popular models include BERT, ELMo, and GPT, all of which leverage contextual embeddings to enhance their language understanding capabilities.

Are there any limitations to using contextual embeddings?

Yes, they can be computationally intensive and may require large datasets for training, which can be a challenge in some applications.