Abstract
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 specifi-
cally 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