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Generic generation and manipulation of text is challenging and has limitedsuccess compared to recent deep generative modeling in visual domain. Thispaper aims at generating plausible natural language sentences, whose attributesare dynamically controlled by learning disentangled latent representations withdesignated semantics. We propose a new neural generative model which combinesvariational auto-encoders and holistic attribute discriminators for effectiveimposition of semantic structures. With differentiable approximation todiscrete text samples, explicit constraints on independent attribute controls,and efficient collaborative learning of generator and discriminators, our modellearns highly interpretable representations from even only word annotations,and produces realistic sentences with desired attributes. Quantitativeevaluation validates the accuracy of sentence and attribute generation.

Toward Controlled Generation of Text