  Learning to control the structure of sentences is a challenging problem in
text generation. Existing work either relies on simple deterministic approaches
or RL-based hard structures. We explore the use of structured variational
autoencoders to infer latent templates for sentence generation using a soft,
continuous relaxation in order to utilize reparameterization for training.
Specifically, we propose a Gumbel-CRF, a continuous relaxation of the CRF
sampling algorithm using a relaxed Forward-Filtering Backward-Sampling (FFBS)
approach. As a reparameterized gradient estimator, the Gumbel-CRF gives more
stable gradients than score-function based estimators. As a structured
inference network, we show that it learns interpretable templates during
training, which allows us to control the decoder during testing. We demonstrate
the effectiveness of our methods with experiments on data-to-text generation
and unsupervised paraphrase generation.
