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Related Questions
- What are the key factors that contribute to diversity in query selection for active learning in NLP?
- How does diversity in query selection impact the accuracy of active learning in NLP, particularly in terms of reducing annotation bias?
- Can you explain the relationship between diversity in query selection and the efficiency of active learning in NLP, including the impact on model convergence and annotation time?
- What are some effective strategies for promoting diversity in query selection in active learning for NLP, such as using sampling techniques or incorporating multiple annotators?
- How does diversity in query selection affect the performance of NLP models in terms of capturing nuances of language and handling out-of-distribution data?
- Can you discuss the trade-offs between diversity and informativeness in query selection for active learning in NLP, and how to balance these competing objectives?
- What are some common pitfalls or challenges associated with promoting diversity in query selection in active learning for NLP, and how to address them?
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