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Related Questions
- How does domain adaptation affect the performance of pre-trained language models like BERT and RoBERTa on out-of-vocabulary words?
- Can fine-tuning on a specific domain dataset help improve the ability of BERT and RoBERTa to generalize to unseen words?
- In what ways do BERT and RoBERTa's architecture and training objectives influence their performance on low-frequency words?
- What are the key factors that contribute to the improved performance of fine-tuned BERT and RoBERTa on domain-specific datasets?
- Can we expect similar improvements in performance on low-frequency words when fine-tuning other pre-trained language models, such as XLNet or ALBERT?
- How does the size and quality of the domain-specific dataset impact the effectiveness of fine-tuning BERT and RoBERTa on low-frequency words?
- What are some potential limitations or challenges associated with fine-tuning pre-trained language models on domain-specific datasets for improved performance on low-frequency words?
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