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Related Questions
- How do the fixed-length input limitations of BERT and RoBERTa impact the ability to model long-range dependencies in natural language processing tasks?
- What are some potential solutions to overcome the fixed-length input limitations of BERT and RoBERTa in tasks that require modeling long-range dependencies?
- Can you explain the concept of contextualized embeddings and how they relate to the fixed-length input limitations of BERT and RoBERTa?
- How do the fixed-length input limitations of BERT and RoBERTa affect the performance of question answering models that require capturing long-range dependencies?
- What are some alternative models that can handle longer input sequences and model long-range dependencies more effectively?
- Can you discuss the trade-offs between using BERT and RoBERTa for tasks that require modeling long-range dependencies versus using alternative models with longer input lengths?
- How have researchers addressed the fixed-length input limitations of BERT and RoBERTa in their work on text classification tasks that require modeling long-range dependencies?
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