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Related Questions
- Can you explain how data augmentation methods that alter sentence structure, such as switching or rotating sentences, impact the performance of language models?
- How do methods that modify words or characters, like substitution, insertion, or deletion, affect the performance of models on tasks like text classification or sentiment analysis?
- In what ways can non-semantic preserving data augmentation methods, such as word jumbling or swapping words, impact the quality of model predictions in natural language processing tasks?
- Can you discuss the impact of non-semantic preserving data augmentation methods on model robustness, especially in scenarios where the training and test data distributions are similar?
- How do methods that generate paraphrases or paraphrastic phrases impact the performance of models on tasks like language translation or text summarization?
- What are some potential limitations of using non-semantic preserving data augmentation methods in NLP tasks, and how can these limitations be addressed?
- Can you explain how the choice of non-semantic preserving data augmentation method can affect the generalizability of models on out-of-distribution test data?
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