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Related Questions
- How does subword tokenization improve the handling of out-of-vocabulary words in language models?
- What are the benefits and limitations of using subword tokenization for OOV word handling?
- Can you explain how subword tokenization mitigates the issue of OOV words in neural machine translation?
- How does subword tokenization compare to other techniques for handling OOV words in NLP tasks?
- What are the computational and memory requirements of subword tokenization, and how do they impact model performance?
- How does the choice of subword vocabulary size affect the trade-off between OOV word handling and model complexity?
- Can you provide examples of how subword tokenization is used in real-world NLP applications to handle OOV words?
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