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Related Questions
- How do contextualized language models handle word embeddings for out-of-vocabulary words?
- What are the benefits of using contextualized language models for NLP tasks involving rare or unseen words?
- Can contextualized language models learn to represent out-of-vocabulary words using context-dependent word embeddings?
- How do contextualized language models compare to traditional word embedding methods for handling out-of-vocabulary words?
- What is the impact of contextualized language models on the performance of NLP tasks when dealing with out-of-vocabulary words?
- Can contextualized language models be fine-tuned for specific NLP tasks to improve their handling of out-of-vocabulary words?
- How do contextualized language models use context to disambiguate out-of-vocabulary words and improve overall NLP performance?
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