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Related Questions
- What is the concept of subwording and how can it be used to handle out-of-vocabulary words in language models?
- How can data augmentation techniques such as back-translation and paraphrasing be used to increase the exposure of OOV words to the model?
- What is the role of pre-training on large datasets and fine-tuning on smaller datasets in augmenting the training data to include more OOV words?
- How can the use of language models with dynamic vocabulary size, such as those based on neural language models, help to improve handling of OOV words?
- What are some techniques for incorporating external knowledge sources, such as knowledge graphs and ontologies, to augment the training data and improve handling of OOV words?
- How can the use of multi-task learning, where the model is trained on multiple tasks simultaneously, help to improve handling of OOV words?
- What are some techniques for using transfer learning, where a pre-trained model is fine-tuned on a smaller dataset, to augment the training data and improve handling of OOV words?
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