Boosting Variational Generative Model Via Condition Enhancing and Lexical-Editing

Conditional Variational Autoencoder (CVAE) has shown promising performance in text generation. However, CVAE is inadequate to generate sentences that are highly coherent to its condition due to error accumulation in decoding and KL-vanishing problem. In this paper, we propose an Edit-CVAE (ECVAE) in which we attempt to exploit information-related data to address the problem by (1) explicitly editing the generated sentence. (2) enriching the latent representation. While maintaining the diversity and information consistency. Experiment results on dialogue and Chinese poetry generation show that our method substantially increases generative coherence while maintaining the diversity and information consistency.