  Most recent research on Text-to-SQL semantic parsing relies on either parser
itself or simple heuristic based approach to understand natural language query
(NLQ). When synthesizing a SQL query, there is no explicit semantic information
of NLQ available to the parser which leads to undesirable generalization
performance. In addition, without lexical-level fine-grained query
understanding, linking between query and database can only rely on fuzzy string
match which leads to suboptimal performance in real applications. In view of
this, in this paper we present a general-purpose, modular neural semantic
parsing framework that is based on token-level fine-grained query
understanding. Our framework consists of three modules: named entity recognizer
(NER), neural entity linker (NEL) and neural semantic parser (NSP). By jointly
modeling query and database, NER model analyzes user intents and identifies
entities in the query. NEL model links typed entities to schema and cell values
in database. Parser model leverages available semantic information and linking
results and synthesizes tree-structured SQL queries based on dynamically
generated grammar. Experiments on SQUALL, a newly released semantic parsing
dataset, show that we can achieve 56.8% execution accuracy on
WikiTableQuestions (WTQ) test set, which outperforms the state-of-the-art model
by 2.7%.
