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Related Questions
- What are the key differences between active learning and traditional machine learning approaches in identifying suboptimal tokenization?
- How can human annotators be used to select the most informative samples for correcting suboptimal tokenization?
- What are some common metrics used to evaluate the effectiveness of active learning in correcting suboptimal tokenization?
- Can you explain the concept of 'pool-based' and 'query-based' active learning approaches in the context of tokenization correction?
- How can active learning be used to identify and correct suboptimal tokenization in large datasets with high dimensionality?
- What are some strategies for reducing the labeling cost associated with active learning in tokenization correction?
- Can you discuss the role of transfer learning in active learning for tokenization correction, and how it can be applied to new datasets?
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