What is the difference between contextual and non-contextual embeddings?

Can you explain the difference between contextual and non-contextual embeddings?

Sure! Non-contextual 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 more dynamically.

Why are contextual embeddings considered better?

Contextual embeddings are better because they understand the nuances of language, capturing different meanings of a word based on its context, which leads to improved performance in NLP tasks.

Can you give an example of each type of embedding?

Absolutely! For non-contextual, 'king' might be represented as a single vector. In contrast, with contextual embeddings, 'bank' in 'river bank' and 'financial bank' would have different vectors reflecting their meanings.

How do these embeddings impact machine learning models?

They significantly enhance model performance. Contextual embeddings allow models to better understand language, leading to more accurate predictions and interpretations in tasks like sentiment analysis or translation.