Generative Adversarial Networks for text using word2vec intermediaries
This work addresses the challenge of text generation for natural language processing applications, but it is incremental as it adapts existing GAN techniques to text using word embeddings.
The paper tackles the problem of generating text using Generative Adversarial Networks (GANs) by proposing a novel approach that handles the discrete nature of text through word embeddings, achieving competitive results compared to existing methods with discrete gradient estimators.
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.