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Related Questions
- How do different fusion methods, such as early fusion, late fusion, and hybrid fusion, impact the performance of multimodal conversational AI systems?
- What are the current evaluation metrics used to assess the effectiveness of multimodal fusion in conversational AI, and are there any limitations or biases associated with these metrics?
- What are the key challenges in evaluating the interpretability and explainability of multimodal fusion in conversational AI systems, and how can these challenges be addressed?
- How can the evaluation of multimodal fusion in conversational AI systems be scaled up to accommodate large and diverse datasets, and what are the potential implications of this scaling?
- What are the current challenges in evaluating the impact of multimodal fusion on the emotional and social intelligence of conversational AI systems, and how can these challenges be addressed?
- How can the evaluation of multimodal fusion in conversational AI systems be made more robust to handle uncertainty, ambiguity, and contextual factors, and what are the potential benefits of doing so?
- What are the current challenges in evaluating the transferability and generalizability of multimodal fusion in conversational AI systems across different domains, languages, and cultures, and how can these challenges be addressed?
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