How do you prevent output duplication when training a GAN for text generation

Question

Can you tell me How do you prevent output duplication when training a GAN for text generation?

Answer 1

To prevent output duplication when training a GAN for text generation, you can follow the following key points:

• Diverse Training Data: Ensure a diverse and balanced dataset to encourage varied outputs.
• Penalty for Repetition: Add loss terms (e.g., self-BLEU or n-gram penalties) to discourage repetitive outputs.
• Temperature Sampling: Lower the sampling temperature to increase randomness in token selection.
• Regularization: Apply regularization techniques like dropout to promote diversity in learned patterns.

Here is the code snippet you can follow:

In the above code, we are using the following key points:

• Self-BLEU: Penalizes repetitive outputs by measuring similarity to itself.
• Diverse Sampling: Use techniques like top-k or nucleus sampling during generation.
• Regularization: Encourages the generator to explore diverse outputs during training.

Hence, these steps help reduce duplication and promote more varied and meaningful text generation.