How do contextual embeddings work?

Can you explain how contextual embeddings work in natural language processing?

Sure! Contextual embeddings are representations of words that take into account the context in which they appear, allowing for more nuanced understanding.

How do they differ from traditional word embeddings?

Traditional word embeddings assign a fixed vector to each word, while contextual embeddings generate different vectors based on surrounding words.

Can you give an example of how this works in a sentence?

Absolutely! 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 capture these differences.

What are some popular models that use contextual embeddings?

Popular models include BERT, ELMo, and GPT, which all utilize contextual embeddings to improve language understanding.