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Related Questions
- How do contextualized embeddings capture nuanced meaning and context in language?
- What are some common applications of contextualized embeddings in intent-based tasks, such as sentiment analysis and question answering?
- Can you explain the difference between contextualized and non-contextualized embeddings in language understanding?
- How do contextualized embeddings handle out-of-vocabulary words and words with multiple meanings?
- What are some challenges in using contextualized embeddings for intent-based tasks, and how can they be addressed?
- Can you provide examples of how contextualized embeddings can improve performance in tasks such as named entity recognition and text classification?
- How do contextualized embeddings relate to other approaches in language understanding, such as attention mechanisms and graph-based methods?
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