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％。

使用细粒度查询理解提升文本转 SQL 语义解析

Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding

Natural Language Search (NLS) extends the capabilities of search engines that
perform keyword search allowing users to issue queries in a more "natural"
language. The engine tries to understand the meaning of the queries and to map
the query words to the symbols it supports like Persons, Organizations, Time
Expressions etc.. It, then, retrieves the information that satisfies the user's
need in different forms like an answer, a record or a list of records. We
present an NLS system we implemented as part of the Search service of a major
CRM platform. The system is currently in production serving thousands of
customers. Our user studies showed that creating dynamic reports with NLS saved
more than 50% of our user's time compared to achieving the same result with
navigational search. We describe the architecture of the system, the
particularities of the CRM domain as well as how they have influenced our
design decisions. Among several submodules of the system we detail the role of
a Deep Learning Named Entity Recognizer. The paper concludes with discussion
over the lessons learned while developing this product.

该研究介绍了一种基于自然语言的搜索引擎，它应用了深度学习的命名实体识别技术，实现了高效的客户关系管理平台，用户通过这个系统可以极大地节省时间，快速生成动态报告。

自然语言企业搜索的查询理解

Query Understanding for Natural Language Enterprise Search

In this paper we investigate the role of the dependency tree in a named
entity recognizer upon using a set of GCN. We perform a comparison among
different NER architectures and show that the grammar of a sentence positively
influences the results. Experiments on the ontonotes dataset demonstrate
consistent performance improvements, without requiring heavy feature
engineering nor additional language-specific knowledge.

本文研究了依赖树在使用一组 GCN 的命名实体识别器中的作用。我们对不同的 NER 架构进行比较，并表明一句话的语法对结果产生积极影响。在 OntoNotes 数据集上的实验表明，在不需要大量特征工程或额外的语言特定知识的情况下，可以实现一致的性能改进。