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Related Questions
- What are the limitations of RNNs in handling long-term dependencies in dialogue generation?
- How do transformer-based models improve the efficiency and parallelization of dialogue generation compared to RNNs?
- Can you explain the concept of self-attention in transformer-based models and how it applies to dialogue generation?
- What are some common applications of transformer-based models in dialogue generation, such as conversational AI or chatbots?
- How do RNNs typically handle sequence data, and what are some common architectures used in RNN-based dialogue generation?
- What are the challenges of training RNNs for dialogue generation, such as vanishing gradients and exploding gradients?
- Can you compare the performance of transformer-based models and RNNs in a dialogue generation task, such as generating coherent and engaging responses?
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