Implementing data augmentation during the training of generative models can help increase the dataset and improve model robustness. Here are some good techniques along with code examples to get you started.
Data Augmentation Techniques
Text Augmentation:
- Synonym Replacement: Replacing words with their synonyms
- Random Insertion: Introducing random words in text
- Back Translation: Translate a text into another language and translate it back to introduce different variations
Noise Injection: Introduce random noise by introducing typographical errors or changing punctuation.
- Sentence Shuffling: Shuffle sentences in a paragraph to generate new variations.
Code Examples
Here are some simple implementations of these techniques using Python:
1. Synonym Replacement
2. Back Translation
3. Sentence Shuffling