What are the future trends in NLP data augmentation?

What are the future trends in NLP data augmentation?

Future trends in NLP data augmentation include the use of generative models, transfer learning, and synthetic data generation.

How will generative models change data augmentation?

Generative models can create diverse and realistic data samples, enhancing the training process and improving model performance.

What role does transfer learning play in this context?

Transfer learning allows models to leverage knowledge from pre-trained models, reducing the need for extensive data augmentation.

Can you give examples of synthetic data generation?

Examples include using GANs to create text data or employing language models to generate paraphrased sentences.

What challenges do we face with these trends?

Challenges include ensuring data quality, managing biases in generated data, and the computational cost of training complex models.