What are the benefits of contextual embeddings in AI?

What are the benefits of contextual embeddings in AI?

Contextual embeddings improve understanding of word meanings based on context, enhancing NLP tasks.

Can you give me an example of how they work?

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

How do they compare to traditional embeddings?

Traditional embeddings assign a fixed vector to each word, while contextual embeddings generate vectors based on surrounding words, capturing nuances.

What impact do they have on machine translation?

They significantly enhance machine translation by providing context-aware translations, leading to more accurate and fluent results.

Are there any limitations to using contextual embeddings?

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