  One daunting problem for semantic parsing is the scarcity of annotation.
Aiming to reduce nontrivial human labor, we propose a two-stage semantic
parsing framework, where the first stage utilizes an unsupervised paraphrase
model to convert an unlabeled natural language utterance into the canonical
utterance. The downstream naive semantic parser accepts the intermediate output
and returns the target logical form. Furthermore, the entire training process
is split into two phases: pre-training and cycle learning. Three tailored
self-supervised tasks are introduced throughout training to activate the
unsupervised paraphrase model. Experimental results on benchmarks Overnight and
GeoGranno demonstrate that our framework is effective and compatible with
supervised training.
