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Related Questions
- How do contextual embeddings improve the performance of sentiment analysis models?
- Can you explain the role of contextual embeddings in question answering tasks, such as open-domain QA?
- How do contextual embeddings capture nuances in language, such as sarcasm and idioms, that are relevant to sentiment analysis?
- In what ways do contextual embeddings enhance the ability of models to understand the context of a question in QA tasks?
- How do contextual embeddings compare to traditional word embeddings in terms of their impact on downstream tasks?
- Can you provide examples of how contextual embeddings have been used to improve the performance of sentiment analysis and question answering models?
- What are some challenges associated with using contextual embeddings in downstream tasks, and how can they be addressed?
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