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Related Questions
- How do contextualized word embeddings like BERT and RoBERTa differ from traditional word embeddings in terms of their ability to capture language nuances?
- Can you explain the concept of 'contextualized' in the context of word embeddings and how it relates to the nuances of language?
- What are some examples of language nuances that contextualized word embeddings are particularly good at capturing?
- How do contextualized word embeddings address the limitations of traditional word embeddings in capturing subtle aspects of language?
- What is the role of pre-training in enabling contextualized word embeddings to capture nuances of language?
- Can you provide some examples of tasks where contextualized word embeddings have shown significant improvements over traditional word embeddings?
- How do contextualized word embeddings handle out-of-vocabulary words and their nuances in language?
- What is the relationship between contextualized word embeddings and the concept of polysemy in language, and how do they capture multiple meanings of words?
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