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Related Questions
- What are some limitations of BERT in capturing long-range dependencies?
- Can transformers like BERT handle non-local dependencies in text effectively?
- What are some alternative models to BERT that are specifically designed for capturing long-range dependencies?
- How does the self-attention mechanism in BERT affect its ability to capture long-range dependencies?
- Can BERT be fine-tuned for specific tasks that require long-range dependency modeling?
- What are some common pitfalls to avoid when using BERT for applications that require long-range dependency modeling?
- Are there any techniques or modifications to BERT that can enhance its ability to capture long-range dependencies in text?
- Can BERT be combined with other models or techniques to improve its long-range dependency modeling capabilities?
- How does BERT's ability to capture long-range dependencies compare to other language models like ELMo or RoBERTa?
- What are some real-world applications or tasks where capturing long-range dependencies is critical, and how can BERT be used for them?
- Are there any pre-trained versions of BERT that have been specifically fine-tuned for long-range dependency modeling?
- Can BERT be used for downstream tasks that require modeling of long-range dependencies, such as machine translation or text summarization?
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