Modeling documents with Generative Adversarial Networks

Goal

GAN performence with nlp tasks, especially discovering methods for distributed representation documents using GAN.

Model

facts:

1. Energy based model perform better than probabilstic model
2. Denoising AutoEncoder within the discriminator to learn the document representation

Experiments

1. comparision against DocNade

2. t-sne doc representation