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Related Questions
- How do contextualized language models like BERT and RoBERTa capture nuances of word embeddings in text classification tasks?
- Can you explain how contextualized language models handle polysemy and its impact on text classification accuracy?
- In what ways do contextualized language models like ELMo and XLNet represent figurative language in text classification tasks?
- How do contextualized language models account for out-of-vocabulary words and their effect on text classification performance?
- Can you discuss the role of contextualized language models in handling idiomatic expressions and their impact on text classification?
- How do contextualized language models like ULMFiT and AWD-LSTM handle linguistic phenomena like negation and its effect on text classification?
- What are the key differences in how contextualized language models like BERT and RoBERTa handle linguistic phenomena like anaphora and its impact on text classification accuracy?
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