How do contextual embeddings work?

Can you explain how contextual embeddings work in natural language processing?

Sure! Contextual embeddings are representations of words that take into account the context in which they appear. Unlike traditional embeddings, which assign a single vector to each word, contextual embeddings generate different vectors for the same word based on its surrounding words.

What are some popular models that use contextual embeddings?

Some popular models include BERT, ELMo, and GPT. These models use deep learning techniques to create embeddings that capture the nuances of language.

How do these embeddings improve understanding in NLP tasks?

They improve understanding by providing richer representations of words, allowing models to grasp meanings based on context, which enhances performance in tasks like sentiment analysis and machine translation.

Can you give an example of how context changes word meaning?

Absolutely! Take the word 'bank'. In 'river bank', it refers to the land alongside a river, while in 'savings bank', it refers to a financial institution. Contextual embeddings help models distinguish between these meanings.