Natural Language Processing consist in the study of how to automatically process information conveyed in Natural Language, that is, human language.This thesis is interested in a subset of this domain called Text Generation which consist in the elaboration of computational system capable of generating text comprehensible by humans.More specifically, it is interest in explore a specific model named Variational Autoencoders, which is one of the most popular models in the academia in the time of elaboration of this work. In the context of the VAE, this thesis aims to accomplish two sets of goals.The first is: to improve the overall performance of the VAE in the text generation task, the understanding of the most new techniques and reflect upon how to evaluate Text Generation Models.