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Related Questions
- How do word embeddings and contextualized embeddings differ in terms of their representation of word meanings?
- Can you explain the limitations of word embeddings and how contextualized embeddings address them?
- What is the key characteristic that distinguishes contextualized embeddings from word embeddings?
- How do contextualized embeddings capture nuances in word meanings that word embeddings often miss?
- Can you provide examples of applications where contextualized embeddings are preferred over word embeddings?
- What is the impact of contextualized embeddings on downstream natural language processing tasks such as language translation and text classification?
- How do contextualized embeddings handle out-of-vocabulary words, and what are the implications for language modeling?
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