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Related Questions
- What are the key differences in interpretability between data-driven and rule-based dialogue models?
- How do data-driven models learn from large datasets, and what impact does this have on interpretability?
- Can you provide examples of how rule-based models are more interpretable, and why?
- What are some challenges in making data-driven dialogue models more interpretable, and how can they be addressed?
- Do data-driven models have an advantage over rule-based models in terms of handling nuances and context in conversations?
- How do the interpretability of data-driven and rule-based models affect the trust and reliance on AI-powered dialogue systems?
- Can you discuss the role of explainability techniques, such as saliency maps and feature importance, in improving the interpretability of data-driven dialogue models?
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