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- In a dialogue system where the user's preferences and interests are highly diverse, active learning can help to efficiently update the model's knowledge with minimal human annotation effort by selecting the most informative user inputs for labeling.
- When the dialogue system is deployed in a new domain or with a new user group, active learning can be used to adapt the model to the new context with a small set of high-quality labeled data.
- In a multi-turn dialogue system where the user's goals and preferences change over time, active learning can help the model to learn from user feedback and adapt to the changing context.
- When the dialogue system needs to handle out-of-vocabulary words or domain-specific terminology, active learning can be used to incorporate the new terms into the model's knowledge base with minimal human annotation effort.
- In a dialogue system where the user's mental state or emotions need to be understood, active learning can help the model to learn from user feedback and adapt to the user's emotional state.
- When the dialogue system is used in a setting where the user's data privacy is a concern, active learning can be used to minimize the amount of sensitive user data that needs to be labeled.
- In a dialogue system where the user's input is noisy or contains errors, active learning can be used to select the most accurate and informative user inputs for labeling and improve the overall model accuracy.
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