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Related Questions
- What specific limitations of BERT-based models lead to difficulties with long-range dependencies in text?
- Are there any techniques to compensate for BERT's ability to capture long-range semantic relationships?
- How do the self-attention mechanisms in BERT, such as multi-head attention, impact its performance with long-range dependencies?
- Can pre-training BERT on tasks with long-range dependencies, like machine translation, improve its ability to handle such instances?
- Is there a trade-off between the model's ability to capture long-range dependencies and its computational efficiency?
- How do transformer-based architectures, like those used in BERT, compare to recurrent neural network (RNN) alternatives in handling long-range text dependencies?
- Are there any strategies, such as using position-based embeddings or incorporating external information, to help BERT better capture long-range information in text?
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