What are the differences between contextual embeddings and traditional embeddings?

What are the differences between contextual embeddings and traditional embeddings?

Traditional embeddings, like Word2Vec, represent words as fixed vectors, while contextual embeddings, like BERT, generate dynamic vectors based on context.

Can you give an example of how contextual embeddings work?

Sure! In the sentence 'The bank can refuse to lend money,' the word 'bank' has a different meaning than in 'I went to the bank to fish.' Contextual embeddings capture these nuances.

Why are contextual embeddings considered better?

Contextual embeddings are better because they understand the meaning of words based on their surrounding words, improving accuracy in tasks like sentiment analysis.

Are there any downsides to using contextual embeddings?

Yes, they can be computationally intensive and require more resources compared to traditional embeddings, which can be a drawback in some applications.