  Recently, semantic parsing has attracted much attention in the community.
Although many neural modeling efforts have greatly improved the performance, it
still suffers from the data scarcity issue. In this paper, we propose a novel
semantic parser for domain adaptation, where we have much fewer annotated data
in the target domain compared to the source domain. Our semantic parser
benefits from a two-stage coarse-to-fine framework, thus can provide different
and accurate treatments for the two stages, i.e., focusing on domain invariant
and domain specific information, respectively. In the coarse stage, our novel
domain discrimination component and domain relevance attention encourage the
model to learn transferable domain general structures. In the fine stage, the
model is guided to concentrate on domain related details. Experiments on a
benchmark dataset show that our method consistently outperforms several popular
domain adaptation strategies. Additionally, we show that our model can well
exploit limited target data to capture the difference between the source and
target domain, even when the target domain has far fewer training instances.
