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Related Questions
- How does the self-attention mechanism in BERT enable it to model complex relationships between words in a sentence?
- Can you explain the role of self-attention in BERT's ability to capture long-range dependencies in text, such as between words that are far apart in a sentence?
- How does BERT's use of self-attention differ from traditional recurrent neural networks in terms of its ability to model long-range dependencies?
- What are some challenges that BERT faces when trying to capture long-range dependencies in text, and how does self-attention help address these challenges?
- Can you provide examples of how BERT's self-attention mechanism is used to capture long-range dependencies in text, such as in sentiment analysis or question answering tasks?
- How does BERT's ability to capture long-range dependencies impact its performance on tasks that require understanding of complex relationships between words, such as text classification or machine translation?
- What are some potential limitations of BERT's self-attention mechanism when it comes to capturing long-range dependencies in text, and how might these limitations be addressed in future research?
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