Title: Hierarchically-Structured Variational Autoencoders for Long Text Generation. Abstract: Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation. Existing methods primarily focus on synthesizing relatively short sentences (with less than twenty words). In this paper, we propose a novel framework, hierarchically-structured variational autoencoder (hier-VAE), for generating long and coherent units of text. To enhance the model’s plan-ahead ability, intermediate sentence representations are introduced into the generative networks to guide the word-level predictions. To alleviate the typical optimization challenges associated with textual VAEs, we further employ a hierarchy of stochastic layers between the encoder and decoder networks. Extensive experiments are conducted to evaluate the proposed method, where hier-VAE is shown to make effective use of the latent codes and achieve lower perplexity relative to language models. Moreover, the generated samples from hier-VAE also exhibit superior quality according to both automatic and human evaluations. 