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Related Questions
- What are the key differences in pre-training objectives between BERT and RoBERTa, and how do these differences impact their performance on low-resource languages?
- How does the use of masked language modeling in pre-training enable BERT and RoBERTa to generalize to low-resource languages with limited training data?
- Can you explain the concept of 'language understanding' in the context of BERT and RoBERTa, and how it relates to their performance on low-resource languages?
- What role does the 'masked language modeling' objective play in enabling BERT and RoBERTa to capture contextual relationships in low-resource languages?
- How do the pre-training objectives of BERT and RoBERTa impact their ability to adapt to new languages with limited resources?
- What are some potential challenges in applying BERT and RoBERTa to low-resource languages, and how can these challenges be addressed?
- Can you discuss the implications of using BERT and RoBERTa on low-resource languages for downstream NLP tasks such as machine translation and text classification?
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