MetaEx-GAN: Meta Exploration to Improve Natural Language Generation via Generative Adversarial Networks

Generative Adversarial Networks (GANs) have been popularly researched in natural language generation, so-called Language GANs. Existing works adopt reinforcement learning (RL) based methods such as policy gradients for training Language GANs. The previous research of Language GANs usually focuses on stabilizing policy gradients or applying robust architectures (such as the large-scale pre-trained GPT-2) to achieve better performance. However, the quality and diversity of sampling are not guaranteed simultaneously. In this article, we propose a novel meta-learning-based generative adversarial network, Meta Exploration GAN (MetaEx-GAN), for ensuring the quality and diversity of sampling (sampling efficiency). In the proposed MetaEx-GAN, we develop an explorer trained by Meta Exploration to sample from the generated data to achieve better sampling efficiency. MetaEx-GAN employs MetaEx first applied to Language GANs to achieve better performance. We also propose a critical training method for MetaEx-GAN on the NLG task. According to our experimental results, MetaEx-GAN achieves state-of-the-art performance compared with existing Language GANs methods. Our experiments also demonstrate the generality of MetaEx-GAN with different architectures (involving GPT-2) and how MetaEx-GAN operates to improve Language GANs.