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Related Questions
- What are the key benefits of using subword tokenization in natural language processing?
- How does subword tokenization improve the performance of language models on tasks involving figurative language?
- What are some common challenges in handling out-of-vocabulary words in language models, and how does subword tokenization address them?
- Can you explain the difference between subword tokenization and word-level tokenization in the context of language models?
- How does subword tokenization enable language models to capture nuances of language and improve their overall performance?
- What are some real-world applications of subword tokenization in NLP tasks, such as machine translation and text summarization?
- How does subword tokenization compare to other techniques for handling out-of-vocabulary words, such as back-translation or word embeddings?
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