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Related Questions
- How do self-attention mechanisms in transformer models improve handling of long-range dependencies compared to recurrent neural networks?
- Can you explain the key differences in how transformer models and recurrent neural networks process sequential data like machine translation tasks?
- What are some common challenges in handling long-range dependencies in machine translation tasks, and how do transformer models address them?
- How does the parallelization of self-attention mechanisms in transformer models impact their ability to handle long-range dependencies compared to recurrent neural networks?
- What are some notable examples of transformer models being used for machine translation tasks and their performance compared to recurrent neural networks?
- Can you discuss the role of positional encoding in transformer models and how it helps with handling long-range dependencies in sequential data?
- How do the computational requirements of transformer models compare to recurrent neural networks for tasks like machine translation, particularly when dealing with long-range dependencies?
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