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Related Questions
- How do character-level embeddings enable a language model to capture the nuances of out-of-vocabulary words?
- Can you explain the concept of contextualized embeddings and how it improves a model's ability to handle out-of-vocabulary words?
- How do language models using character-level embeddings and contextualized embeddings learn to represent out-of-vocabulary words in a more accurate manner?
- What are some potential limitations of relying solely on character-level embeddings for out-of-vocabulary words, and how can contextualized embeddings address these limitations?
- How do models using contextualized embeddings learn to disambiguate out-of-vocabulary words in context, and what role do the surrounding words play in this process?
- Can you discuss the trade-offs between using character-level embeddings and contextualized embeddings for handling out-of-vocabulary words, and how do these trade-offs impact model performance?
- How do techniques like subwording and masking help language models handle out-of-vocabulary words when using character-level embeddings and contextualized embeddings?
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