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Related Questions
- What are the implications of BERT's self-attention mechanism on its ability to model long-range dependencies in text data?
- How does the fixed-length contextualized embedding representation in BERT impact its capacity to capture long-distance semantic dependencies?
- Can you explain how BERT's limitations on long-range dependencies affect its performance on tasks that require modeling complex, multi-sentence relationships?
- What are some alternative architectures that can better capture long-distance semantic dependencies in text data?
- How do BERT's limitations on long-range dependencies compare to those of other popular transformer-based models?
- Can you discuss the trade-offs between BERT's computational efficiency and its ability to capture long-distance semantic dependencies?
- What are some potential solutions to address BERT's limitations on long-range dependencies, such as using hierarchical or recursive architectures?
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