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Related Questions
- How does the sequential nature of dialogue impact the selection of informative samples for active learning?
- What are some strategies for handling the context dependence of dialogue, where the relevance of a sample to the current dialogue turn may depend on previous turns?
- How can active learning be adapted to handle the uncertainty associated with dialogue, where the meaning of a turn may be ambiguous or open to multiple interpretations?
- What are some techniques for selecting samples that are representative of the dialogue space, taking into account the dependencies between turns?
- How can active learning be used to improve the robustness of dialogue systems to out-of-vocabulary words, typos, and other forms of noisy input?
- What are some methods for evaluating the effectiveness of active learning in multi-turn dialogue scenarios, where the evaluation metric may depend on the specific application and task?
- How can active learning be used to adapt dialogue systems to new domains or tasks, where the available labeled data may be limited or biased?
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