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Related Questions
- Can you explain the core differences between uncertainty-based and diversity-based sampling strategies for active learning in noisy dialogue data?
- How do uncertainty-based sampling strategies determine which data points to label next in active learning, and what are the assumptions behind these methods?
- What are some of the key benefits and challenges of using diversity-based sampling strategies for active learning in noisy dialogue data?
- How do the goals of uncertainty-based and diversity-based sampling strategies impact the choice of data points for labeling in active learning?
- Can you compare and contrast the theoretical guarantees and empirical performance of uncertainty-based and diversity-based sampling strategies for active learning?
- How do noisy dialogue data and imperfect human annotations affect the design and evaluation of uncertainty-based and diversity-based sampling strategies for active learning?
- What are some common challenges and limitations of active learning in noisy dialogue data, and how can they be addressed using uncertainty-based or diversity-based sampling strategies?
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