  Prior work on controllable text generation usually assumes that the
controlled attribute can take on one of a small set of values known a priori.
In this work, we propose a novel task, where the syntax of a generated sentence
is controlled rather by a sentential exemplar. To evaluate quantitatively with
standard metrics, we create a novel dataset with human annotations. We also
develop a variational model with a neural module specifically designed for
capturing syntactic knowledge and several multitask training objectives to
promote disentangled representation learning. Empirically, the proposed model
is observed to achieve improvements over baselines and learn to capture
desirable characteristics.
