DGST: a Dual-Generator Network for Text Style Transfer
Xiao Li, Guanyi Chen, Chenghua Lin, Ruizhe Li
Abstract
We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training. Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several strong baselines with more complicated architecture designs.
Topics & Concepts
Computer scienceGenerator (circuit theory)Dual (grammatical number)ArchitectureStyle (visual arts)Transfer (computing)Simple (philosophy)Artificial intelligenceDual purposeNetwork architectureNatural language processingComputer networkPower (physics)Parallel computingEngineeringLinguisticsMechanical engineeringQuantum mechanicsArchaeologyArtPhilosophyEpistemologyPhysicsVisual artsHistoryTopic ModelingNatural Language Processing TechniquesSpeech Recognition and Synthesis