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Related Questions
- How does pre-training a language model impact its ability to handle low-resource languages?
- What are the key strategies employed during pre-training to minimize the effect of out-of-vocabulary words on NLP model performance?
- Can you elaborate on how pre-training helps alleviate the issue of rare words or unseen entities in downstream tasks?
- How does the size and type of pre-training corpus affect a model's ability to handle out-of-vocabulary words?
- In what ways can pre-trained models be fine-tuned to adapt to specific out-of-vocabulary words in downstream tasks?
- Are there any specific techniques or architectures that are better suited for handling out-of-vocabulary words in pre-training?
- Can pre-training be used as a means to augment and supplement the performance of other methods for handling out-of-vocabulary words?
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