Long Text Generation via Adversarial Training with Leaked Information

Understand

Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.

• Recently, by combining with policy gradient, Generative Adversarial Nets (GAN) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation.
• However, the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process.
• As such, it limits its success when the length of the generated text samples is long (more than 20 words).