How do contextual embeddings improve NLP?

What are contextual embeddings and how do they work?

Contextual embeddings are representations of words that capture their meanings based on surrounding words. Unlike traditional embeddings, they adapt to context.

Can you give me an example of how they improve NLP tasks?

Sure! In sentiment analysis, contextual embeddings help distinguish between 'bank' as a financial institution and 'bank' as the side of a river, improving accuracy.

What are some popular models that use contextual embeddings?

Popular models include BERT, ELMo, and GPT, which leverage contextual embeddings to understand language nuances better.

How do these embeddings compare to traditional word embeddings?

Traditional embeddings like Word2Vec assign a fixed vector to each word, while contextual embeddings generate different vectors based on context, enhancing understanding.

Are there any limitations to using contextual embeddings?

Yes, they can be computationally intensive and may require large datasets for training, which can be a challenge in some applications.