What are contextual embeddings in AI?

What are contextual embeddings in AI?

Contextual embeddings are representations of words that capture their meanings based on the context in which they appear.

How do they differ from traditional word embeddings?

Unlike traditional embeddings, which assign a fixed vector to each word, contextual embeddings generate different vectors for the same word depending on its context.

Can you give me an example of how they work?

Sure! In the sentence 'The bank can refuse to lend money,' the word 'bank' refers to a financial institution, while in 'I sat by the bank of the river,' it refers to the side of a river.

What are some applications of contextual embeddings?

They are widely used in tasks like sentiment analysis, machine translation, and question answering, improving understanding of nuanced language.