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Related Questions
- What are some common techniques used to fine-tune pre-trained language models for out-of-vocabulary (OOV) words?
- How can we leverage the context of the sentence to help the model understand OOV words?
- What role does the size of the training dataset play in improving the model's ability to handle OOV words?
- Can you explain the concept of 'zero-shot learning' in the context of fine-tuning pre-trained language models for OOV words?
- What are some common evaluation metrics used to measure the performance of a fine-tuned model on OOV words?
- How can we use active learning to select the most informative examples for fine-tuning the model on OOV words?
- What are some strategies for incorporating external knowledge graph or dictionary data to help the model understand OOV words?
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