Title: Toward Controlled Generation of Text,
Abstract: Generic generation and manipulation of text is challenging and has limited
success compared to recent deep generative modeling in visual domain. This
paper aims at generating plausible natural language sentences, whose attributes
are dynamically controlled by learning disentangled latent representations with
designated semantics. We propose a new neural generative model which combines
variational auto-encoders and holistic attribute discriminators for effective
imposition of semantic structures. With differentiable approximation to
discrete text samples, explicit constraints on independent attribute controls,
and efficient collaborative learning of generator and discriminators, our model
learns highly interpretable representations from even only word annotations,
and produces realistic sentences with desired attributes. Quantitative
evaluation validates the accuracy of sentence and attribute generation.