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Related Questions
- Can contextualized word embeddings improve the accuracy of natural language processing tasks such as sentiment analysis and named entity recognition?
- How do contextualized word embeddings affect the performance of language models in tasks that require understanding of nuances and subtleties in language?
- Do contextualized word embeddings have a significant impact on the generation of coherent and contextually relevant text?
- Can contextualized word embeddings be used to improve the robustness of language models to out-of-vocabulary words and domain adaptation?
- How do contextualized word embeddings compare to traditional word embeddings in terms of their ability to capture semantic relationships between words?
- Can contextualized word embeddings be fine-tuned for specific tasks and domains to improve their performance?
- What are the limitations and challenges of using contextualized word embeddings in large-scale language models and how can they be addressed?
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