Text-To-Text Generative Adversarial Networks

Generative Adversarial Networks (GAN), which are capable of generating realistic synthetic real-valued data, have achieved great progress in machine learning field. However, generator in GAN framework requires being differentiable, which means that the generator cannot produce discrete data, and it poses great challenge for GAN applied in Natural Language Processing (NLP) research. To unlock the potential of GAN in NLP, we develop a novel Text-to-Text Generative Adversarial Networks (TT-GAN), through which we can get generated text based on semantic information translated from source text. We demonstrate that our model can generate not only realistic texts, but also the source text's paraphrase or its semantic summarization. As our best knowledge, it is the first framework capable of generating natural language on semantic level in real sense, and gives a new perspective to apply GAN on NLP research.