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

A Hybrid Semantic Parsing Approach for Tabular Data Analysis