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Related Questions
- Can you explain how contextualized language models leverage surrounding words and phrases to determine the intended meaning of a word?
- How do contextualized language models handle out-of-vocabulary (OOV) words, and what techniques do they employ to disambiguate their meanings?
- What role does the use of word embeddings play in enabling contextualized language models to capture nuanced word meanings, including those of rare or novel words?
- Can you discuss the impact of pre-training on the ability of contextualized language models to disambiguate word meanings in downstream tasks?
- How do contextualized language models handle polysemy, and what strategies do they employ to distinguish between different senses of a word?
- What is the relationship between contextualized language models and the concept of 'word sense induction,' and how do they contribute to it?
- Can you describe the trade-offs between the level of contextualization and the computational resources required to train and deploy contextualized language models?
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