  This paper presents our semantic parsing system for the evaluation task of
open domain semantic parsing in NLPCC 2019. Many previous works formulate
semantic parsing as a sequence-to-sequence(seq2seq) problem. Instead, we treat
the task as a sketch-based problem in a coarse-to-fine(coarse2fine) fashion.
The sketch is a high-level structure of the logical form exclusive of low-level
details such as entities and predicates. In this way, we are able to optimize
each part individually. Specifically, we decompose the process into three
stages: the sketch classification determines the high-level structure while the
entity labeling and the matching network fill in missing details. Moreover, we
adopt the seq2seq method to evaluate logical form candidates from an overall
perspective. The co-occurrence relationship between predicates and entities
contribute to the reranking as well. Our submitted system achieves the exactly
matching accuracy of 82.53% on full test set and 47.83% on hard test subset,
which is the 3rd place in NLPCC 2019 Shared Task 2. After optimizations for
parameters, network structure and sampling, the accuracy reaches 84.47% on full
test set and 63.08% on hard test subset(Our code and data are available at
https://github.com/zechagl/NLPCC2019-Semantic-Parsing).
