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Related Questions
- What are the key differences between contextualized embeddings and traditional word embeddings in terms of dealing with out-of-vocabulary words?
- How do contextualized embeddings handle words that have multiple meanings depending on the context?
- Can contextualized embeddings improve the performance of NLP tasks when working with rare or unseen words?
- How do contextualized embeddings address the issue of word polysemy?
- What are some common applications of contextualized embeddings in natural language processing?
- Can contextualized embeddings be used to improve the performance of language models on tasks such as sentiment analysis and named entity recognition?
- How do contextualized embeddings compare to traditional word embeddings in terms of computational efficiency and training time?
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