  Variational autoencoders have been widely applied for natural language
generation, however, there are two long-standing problems: information
under-representation and posterior collapse. The former arises from the fact
that only the last hidden state from the encoder is transformed to the latent
space, which is insufficient to summarize data. The latter comes as a result of
the imbalanced scale between the reconstruction loss and the KL divergence in
the objective function. To tackle these issues, in this paper we propose the
discrete variational attention model with categorical distribution over the
attention mechanism owing to the discrete nature in languages. Our approach is
combined with an auto-regressive prior to capture the sequential dependency
from observations, which can enhance the latent space for language generation.
Moreover, thanks to the property of discreteness, the training of our proposed
approach does not suffer from posterior collapse. Furthermore, we carefully
analyze the superiority of discrete latent space over the continuous space with
the common Gaussian distribution. Extensive experiments on language generation
demonstrate superior advantages of our proposed approach in comparison with the
state-of-the-art counterparts.
