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Related Questions
- How do language models like RoBERTa handle unknown or unseen words during training and inference?
- What are the common techniques used to handle out-of-vocabulary (OOV) words in transformer-based models like RoBERTa?
- Can you explain the concept of subwording and its role in handling OOV words in language models?
- How do embedding-based approaches, such as subword embeddings, help mitigate the OOV problem in RoBERTa and other language models?
- What are some alternatives to subwording, such as character-level or wordpiece embeddings, and how do they compare to subwording?
- Can you discuss the trade-offs between different OOV handling techniques in terms of model performance, computational cost, and interpretability?
- How do OOV handling techniques impact the overall performance of RoBERTa on downstream NLP tasks, such as sentiment analysis or question answering?
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