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Related Questions
- What are the key differences in word embeddings between contextualized and non-contextualized language models?
- How do contextualized models, such as BERT, capture word sense disambiguation compared to non-contextualized models?
- Can you explain how contextualized models handle out-of-vocabulary words versus non-contextualized models?
- What is the impact of contextualization on word embeddings in terms of semantic meaning and nuance?
- How do contextualized models, such as RoBERTa, improve over non-contextualized models in terms of capturing contextualized word representations?
- What are the implications of contextualized word embeddings on downstream NLP tasks, such as sentiment analysis and question answering?
- Can you compare and contrast the performance of contextualized and non-contextualized language models on tasks that require nuanced understanding of language, such as idiomatic expressions and figurative language?
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