What is the future of word embeddings in AI?

What is the future of word embeddings in AI?

The future of word embeddings is likely to involve more context-aware models, such as transformers, which can capture nuances better.

How do transformers improve upon traditional word embeddings?

Transformers use self-attention mechanisms to weigh the importance of different words in context, leading to richer representations.

Will word embeddings still be relevant with advancements in AI?

Yes, while techniques may evolve, the core idea of representing words in a vector space will remain relevant for many applications.

What are some challenges in developing future word embeddings?

Challenges include handling polysemy, ensuring cultural sensitivity, and reducing biases in the training data.