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Related Questions
- What are the common techniques used in natural language processing to handle out-of-vocabulary words?
- How do subword models address the issue of out-of-vocabulary words in language modeling?
- What is the impact of out-of-vocabulary words on model performance, and how can it be mitigated?
- Can you explain the concept of subword tokenization and its role in reducing the impact of out-of-vocabulary words?
- How do language models handle words that are not seen during training, and what are the implications for model performance?
- What are some strategies for dealing with out-of-vocabulary words in language generation tasks, such as text summarization or machine translation?
- How can the use of word embeddings, such as Word2Vec or GloVe, help mitigate the impact of out-of-vocabulary words on model performance?
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