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Related Questions
- What are the key architectural features of transformer models that enable them to handle long-range dependencies in NLP tasks?
- Can you explain how the self-attention mechanism in transformer models allows for parallelization of long-range dependencies?
- How do transformer models compare to recurrent neural networks (RNNs) in handling long-range dependencies in NLP tasks?
- What are some common challenges associated with training transformer models on long-range dependency tasks?
- How do transformer models handle out-of-vocabulary (OOV) words when dealing with long-range dependencies?
- Can you discuss the impact of attention mechanisms on the ability of transformer models to handle long-range dependencies?
- What are some techniques for regularizing transformer models to prevent overfitting on long-range dependency tasks?
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