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Related Questions
- Can data augmentation techniques such as back-translation and paraphrasing help increase the representation of out-of-vocabulary words in pre-trained language models like BERT and RoBERTa?
- How does the use of data augmentation impact the out-of-vocabulary word coverage in large language models?
- Can data augmentation techniques such as word insertion and replacement improve the accuracy of BERT and RoBERTa on out-of-vocabulary words?
- Does the application of data augmentation techniques to training data help improve the performance of RoBERTa on tasks where out-of-vocabulary words are common?
- Can data augmentation techniques help reduce the vocabulary gap in BERT and RoBERTa by learning to generate new words from context?
- What are some effective data augmentation techniques that can be applied to pre-trained language models like BERT and RoBERTa to improve their out-of-vocabulary word representation?
- Can data augmentation techniques be used to increase the robustness of BERT and RoBERTa to out-of-vocabulary words in downstream NLP tasks?
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