  Wasserstein autoencoders are effective for text generation. They do not
however provide any control over the style and topic of the generated sentences
if the dataset has multiple classes and includes different topics. In this
work, we present a semi-supervised approach for generating stylized sentences.
Our model is trained on a multi-class dataset and learns the latent
representation of the sentences using a mixture of Gaussian prior without any
adversarial losses. This allows us to generate sentences in the style of a
specified class or multiple classes by sampling from their corresponding prior
distributions. Moreover, we can train our model on relatively small datasets
and learn the latent representation of a specified class by adding external
data with other styles/classes to our dataset. While a simple WAE or VAE cannot
generate diverse sentences in this case, generated sentences with our approach
are diverse, fluent, and preserve the style and the content of the desired
classes.
