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Related Questions
- Can subword tokenization methods, such as WordPiece or BPE, be used to mitigate the impact of out-of-vocabulary words on BERT and RoBERTa models?
- How can embedding-based methods, such as adding a random embedding vector, be used to deal with out-of-vocabulary words in BERT and RoBERTa models?
- What are the trade-offs between using knowledge distillation and fine-tuning pre-trained models when dealing with out-of-vocabulary words?
- Can pre-training on larger datasets or using multi-task learning help to improve the robustness of BERT and RoBERTa models to out-of-vocabulary words?
- What are some strategies for calibrating the confidence scores of BERT and RoBERTa models when they encounter out-of-vocabulary words?
- How can the use of external knowledge sources, such as knowledge graphs or dictionaries, be used to augment the training data and improve the handling of out-of-vocabulary words?
- Can the use of attention mechanisms and contextualized embeddings in BERT and RoBERTa models help to focus on relevant information and reduce the impact of out-of-vocabulary words?
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