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Related Questions
- How do pre-trained language models like BERT and RoBERTa leverage contextual information to disambiguate polysemous words?
- Can you explain how contextual information is used in BERT and RoBERTa to improve polysemy resolution in natural language processing tasks?
- What role does the attention mechanism play in enabling BERT and RoBERTa to incorporate contextual information for polysemy resolution?
- How do the self-attention and masked language modeling components of BERT and RoBERTa contribute to polysemy resolution through contextual information?
- In what ways do the pre-trained weights and contextual information interact to facilitate polysemy resolution in BERT and RoBERTa?
- Can you discuss the implications of using contextual information for polysemy resolution in BERT and RoBERTa on downstream NLP tasks?
- What are some potential limitations of relying on contextual information for polysemy resolution in BERT and RoBERTa, and how can they be addressed?
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