What are the applications of contextual embeddings in NLP?

What are the applications of contextual embeddings in NLP?

Contextual embeddings are used in various NLP tasks like sentiment analysis, machine translation, and question answering.

Can you explain how they improve sentiment analysis?

Sure! They capture the meaning of words based on context, allowing for better understanding of nuances in sentiment.

What models are commonly used for generating contextual embeddings?

Popular models include BERT, ELMo, and GPT, which all generate embeddings that consider surrounding words.

Are there any limitations to using contextual embeddings?

Yes, they can be computationally expensive and may require large datasets for training to achieve optimal performance.