  Text generation aims to produce human-like natural language output for
down-stream tasks. It covers a wide range of applications like machine
translation, document summarization, dialogue generation and so on. Recently
deep neural network-based end-to-end architectures have been widely adopted.
The end-to-end approach conflates all sub-modules, which used to be designed by
complex handcrafted rules, into a holistic encode-decode architecture. Given
enough training data, it is able to achieve state-of-the-art performance yet
avoiding the need of language/domain-dependent knowledge. Nonetheless, deep
learning models are known to be extremely data-hungry, and text generated from
them usually suffer from low diversity, interpretability and controllability.
As a result, it is difficult to trust the output from them in real-life
applications. Deep latent-variable models, by specifying the probabilistic
distribution over an intermediate latent process, provide a potential way of
addressing these problems while maintaining the expressive power of deep neural
networks. This dissertation presents how deep latent-variable models can
improve over the standard encoder-decoder model for text generation.
