  This paper presents a novel approach to translating natural language
questions to SQL queries for given tables, which meets three requirements as a
real-world data analysis application: cross-domain, multilingualism and
enabling quick-start. Our proposed approach consists of: (1) a novel data
abstraction step before the parser to make parsing table-agnosticism; (2) a set
of semantic rules for parsing abstracted data-analysis questions to
intermediate logic forms as tree derivations to reduce the search space; (3) a
neural-based model as a local scoring function on a span-based semantic parser
for structured optimization and efficient inference. Experiments show that our
approach outperforms state-of-the-art algorithms on a large open benchmark
dataset WikiSQL. We also achieve promising results on a small dataset for more
complex queries in both English and Chinese, which demonstrates our language
expansion and quick-start ability.
