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Related Questions
- How do word embeddings and contextualized embeddings differ in handling out-of-vocabulary words?
- Can you provide examples of tasks where word embeddings perform better than contextualized embeddings and vice versa?
- How do contextualized embeddings, such as BERT, handle polysemy and homographs compared to word embeddings?
- What is the impact of pre-training and fine-tuning on the performance of contextualized embeddings?
- Can you explain the concept of 'in-context' learning and how it relates to contextualized embeddings?
- How do contextualized embeddings, such as RoBERTa, handle long-range dependencies in language compared to word embeddings?
- What are the limitations of using word embeddings in tasks that require nuanced understanding of language, such as sentiment analysis and question answering?
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