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%.

本文提出了一种基于标记级精细化查询理解的通用、模块化的神经语义分析框架，包括命名实体识别器（NER）、神经实体链接器（NEL）和神经语义解析器（NSP），该框架联合建模查询和数据库，并基于动态生成的语法合成基于树结构的 SQL 查询。实验证明，该模型在 SQUALL 数据集上的执行准确率达到了 56.8％，超过了现有技术水平 2.7％。