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Related Questions
- How do data augmentation and subword tokenization reduce low-frequency word bias in BERT and RoBERTa models?
- What are the main differences between data augmentation and subword tokenization in reducing low-frequency word bias in transformer-based models?
- Can you explain the concept of low-frequency word bias and how data augmentation and subword tokenization mitigate it in BERT and RoBERTa models?
- How does subword tokenization compare to data augmentation in improving the performance of BERT and RoBERTa models on low-frequency words?
- What are some techniques for data augmentation and subword tokenization, and how do they contribute to reducing low-frequency word bias?
- How does the combination of data augmentation and subword tokenization improve the performance of BERT and RoBERTa models on low-resource tasks?
- What are the challenges and limitations of reducing low-frequency word bias using data augmentation and subword tokenization in transformer-based models?
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