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Related Questions
- Can data augmentation techniques such as back-translation and paraphrasing help improve the robustness of language models to out-of-vocabulary words?
- How does the use of word embeddings in data augmentation impact the language model's ability to handle out-of-vocabulary words?
- What are some common data augmentation techniques used to improve language model robustness, and how do they address out-of-vocabulary words?
- Can data augmentation be used to reduce the reliance of language models on specific vocabulary and improve their generalizability to unseen words?
- How does the size and quality of the augmented dataset affect the language model's robustness to out-of-vocabulary words?
- What are some challenges associated with using data augmentation to improve language model robustness, particularly in the context of out-of-vocabulary words?
- Can data augmentation be used in conjunction with other techniques, such as transfer learning and fine-tuning, to further improve language model robustness to out-of-vocabulary words?
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