What is the difference between static and contextual embeddings?

Can you explain the difference between static and contextual embeddings?

Sure! Static embeddings, like Word2Vec, assign a fixed vector to each word regardless of context. Contextual embeddings, like BERT, generate vectors based on the surrounding words, capturing meaning in context.

Why are contextual embeddings considered better?

Contextual embeddings are better because they understand word meanings based on context, improving tasks like sentiment analysis and translation.

Can you give an example of how they differ in a sentence?

Absolutely! In the sentence 'The bank can refuse to lend money,' 'bank' refers to a financial institution. In 'I sat by the bank of the river,' it refers to the side of a river. Contextual embeddings capture these meanings, while static ones do not.

What are some popular models that use contextual embeddings?

Popular models include BERT, ELMo, and GPT, all of which leverage contextual embeddings to enhance understanding in various NLP tasks.