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Related Questions
- How does the type of data augmentation (e.g., word dropout, synonym replacement) affect the performance of BERT and RoBERTa on tasks with low-frequency words?
- Can you explain the impact of data augmentation on the representation of low-frequency words in BERT and RoBERTa models?
- How does the frequency of data augmentation (e.g., how often to apply augmentation) influence the performance of BERT and RoBERTa on tasks involving low-frequency words?
- What is the effect of using different data augmentation techniques (e.g., back-translation, paraphrasing) on the performance of BERT and RoBERTa on tasks with low-frequency words?
- Can you discuss the relationship between the choice of data augmentation technique and the quality of the training data for BERT and RoBERTa on tasks involving low-frequency words?
- How does the choice of data augmentation technique impact the robustness of BERT and RoBERTa to out-of-vocabulary (OOV) words, particularly those with low frequency?
- What are the implications of using different data augmentation techniques on the interpretability of BERT and RoBERTa's performance on tasks involving low-frequency words?
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