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Related Questions
- What are the key considerations when selecting a small set of high-quality labeled data for active learning in a new domain or user group?
- How does active learning help a dialogue system adapt to new contexts and user groups without requiring a large amount of labeled data?
- What are the potential challenges and limitations of using active learning in a deployed dialogue system?
- Can you explain the concept of 'high-quality labeled data' in the context of active learning and how it differs from traditional labeled data?
- How does the model's performance change over time as it is updated with new labeled data through active learning?
- What are some strategies for selecting the most informative and relevant data points for labeling during active learning?
- How does active learning compare to other approaches to adapting a dialogue system to a new domain or user group, such as fine-tuning or transfer learning?
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