Text Feature Adversarial Learning for Text Generation With Knowledge Transfer From GPT2.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Generative Adversarial Networks (GANs) have shown success in image generation.
• Existing text GANs face challenges due to text's discrete nature, leading to high-variance gradients and mode collapse.
• Reinforcement learning (RL) or continuous relaxations are often used, introducing bias.

Purpose of the Study:

• To propose a novel text GAN model, Text Feature GAN (TFGAN), to address limitations in current text generation methods.
• To improve text generation by performing adversarial learning in a continuous text feature space.
• To alleviate mode collapse and enhance generative diversity in text GANs.

Main Methods:

• Developed TFGAN, a new text GAN architecture.
• Utilized GPT2 to provide "true" text features for adversarial training.
• Combined maximum likelihood estimation in text space with adversarial learning in text feature space.
• Trained TFGAN using a unified objective function.

Main Results:

• TFGAN demonstrates appealing performance in text generation tasks.
• The model effectively alleviates mode collapse, leading to improved generative diversity.
• Adversarial learning in a continuous feature space reduces gradient estimation issues.

Conclusions:

• TFGAN offers an effective solution for text generation, overcoming common GAN limitations.
• The proposed method achieves strong performance and enhanced generative diversity.
• TFGAN serves as a flexible framework for learning robust text representations.