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Related Questions
- How do contextualized embeddings, like BERT or RoBERTa, capture nuances in language that static word embeddings may miss?
- Can you explain how contextualized embeddings enable models to understand the relationships between words in a sentence or passage?
- What are some potential applications of contextualized embeddings in natural language processing tasks, such as sentiment analysis or text classification?
- How do contextualized embeddings improve the performance of models on tasks that require understanding of idioms, figurative language, or sarcasm?
- Can you discuss the potential impact of contextualized embeddings on areas like information retrieval, question answering, or machine translation?
- What are some limitations or challenges associated with using contextualized embeddings, and how are researchers addressing these issues?
- How do contextualized embeddings compare to other types of embeddings, such as word2vec or GloVe, in terms of their ability to capture linguistic context?
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