What are contextual embeddings and how do they compare?

What are contextual embeddings and how do they compare to traditional embeddings?

Contextual embeddings are representations of words that take into account the context in which they appear, unlike traditional embeddings that assign a fixed vector to each word. This allows for better understanding of word meanings in different contexts.

Can you give me examples of popular models that use contextual embeddings?

Sure! Popular models include BERT, ELMo, and GPT. These models generate embeddings based on the surrounding words, making them context-aware.

How do these models improve performance in NLP tasks?

They improve performance by capturing nuances in meaning and relationships between words, leading to better results in tasks like sentiment analysis and question answering.

Are there any limitations to using contextual embeddings?

Yes, some limitations include increased computational costs and the need for large datasets for training, which can be resource-intensive.