Generative Adversarial Networks for text using word2vec intermediaries

WS 2019 · Akshay Budhkar, Krishnapriya Vishnubhotla, Safwan Hossain, Frank Rudzicz ·

Generative adversarial networks (GANs) have shown considerable success, especially in the realistic generation of images. In this work, we apply similar techniques for the generation of text. We propose a novel approach to handle the discrete nature of text, during training, using word embeddings. Our method is agnostic to vocabulary size and achieves competitive results relative to methods with various discrete gradient estimators.