Converting text into the structured query language (Text2SQL) is a research
hotspot in the field of natural language processing (NLP), which has broad
application prospects. In the era of big data, the use of databases has
penetrated all walks of life, in which the collected data is large in scale,
diverse in variety, and wide in scope, making the data query cumbersome and
inefficient, and putting forward higher requirements for the Text2SQL model. In
practical applications, the current mainstream end-to-end Text2SQL model is not
only difficult to build due to its complex structure and high requirements for
training data, but also difficult to adjust due to massive parameters. In
addition, the accuracy of the model is hard to achieve the desired result.
Based on this, this paper proposes a pipelined Text2SQL method: SPSQL. This
method disassembles the Text2SQL task into four subtasks--table selection,
column selection, SQL generation, and value filling, which can be converted
into a text classification problem, a sequence labeling problem, and two text
generation problems, respectively. Then, we construct data formats of different
subtasks based on existing data and improve the accuracy of the overall model
by improving the accuracy of each submodel. We also use the named entity
recognition module and data augmentation to optimize the overall model. We
construct the dataset based on the marketing business data of the State Grid
Corporation of China. Experiments demonstrate our proposed method achieves the
best performance compared with the end-to-end method and other pipeline
methods.

本文提出了一种基于管道的 Text2SQL 方法 SPSQL，将任务分解为表选择、列选择、SQL 生成和值填充四个子任务，采用不同的数据格式以提高模型精度，并使用命名实体识别模块和数据增强进行优化，通过实验得出在市场业务数据上的 SPSQL 方法比端到端方法和其他管道方法表现更好。

SPSQL: 基于逐步解析的文本到 SQL 生成框架

SPSQL: Step-by-step Parsing Based Framework for Text-to-SQL Generation

With the future striving toward data-centric decision-making, seamless access
to databases is of utmost importance. There is extensive research on creating
an efficient text-to-sql (TEXT2SQL) model to access data from the database.
Using a Natural language is one of the best interfaces that can bridge the gap
between the data and results by accessing the database efficiently, especially
for non-technical users. It will open the doors and create tremendous interest
among users who are well versed in technical skills or not very skilled in
query languages. Even if numerous deep learning-based algorithms are proposed
or studied, there still is very challenging to have a generic model to solve
the data query issues using natural language in a real-work scenario. The
reason is the use of different datasets in different studies, which comes with
its limitations and assumptions. At the same time, we do lack a thorough
understanding of these proposed models and their limitations with the specific
dataset it is trained on. In this paper, we try to present a holistic overview
of 24 recent neural network models studied in the last couple of years,
including their architectures involving convolutional neural networks,
recurrent neural networks, pointer networks, reinforcement learning, generative
models, etc. We also give an overview of the 11 datasets that are widely used
to train the models for TEXT2SQL technologies. We also discuss the future
application possibilities of TEXT2SQL technologies for seamless data queries.

本文研究了使用自然语言作为桥梁，通过有效访问数据库创建一个高效的文本到 SQL 模型，重点探讨了 24 个不同神经网络模型以及 11 个常用数据集的特点和局限性，最终讨论了 Text2SQL 技术在实现无缝数据查询方面的可能性。

基于深度学习的自然语言文本到 SQL 查询转换：一项调查

Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey

The current state-of-the-art generative models for open-domain question
answering (ODQA) have focused on generating direct answers from unstructured
textual information. However, a large amount of world's knowledge is stored in
structured databases, and need to be accessed using query languages such as
SQL. Furthermore, query languages can answer questions that require complex
reasoning, as well as offering full explainability. In this paper, we propose a
hybrid framework that takes both textual and tabular evidence as input and
generates either direct answers or SQL queries depending on which form could
better answer the question. The generated SQL queries can then be executed on
the associated databases to obtain the final answers. To the best of our
knowledge, this is the first paper that applies Text2SQL to ODQA tasks.
Empirically, we demonstrate that on several ODQA datasets, the hybrid methods
consistently outperforms the baseline models that only take homogeneous input
by a large margin. Specifically we achieve state-of-the-art performance on
OpenSQuAD dataset using a T5-base model. In a detailed analysis, we demonstrate
that the being able to generate structural SQL queries can always bring gains,
especially for those questions that requires complex reasoning.

本文提出一种混合型框架，将文本和表格式证据作为输入，根据问题生成直接答案或 SQL 查询，并在几个开放领域问题回答（ODQA）数据集上表现出了明显优于基线模型的表现。在详细的分析中，证明生成结构化 SQL 查询将始终带来收益，尤其是对于那些需要复杂推理的问题。这是第一个将 Text2SQL 应用于 ODQA 任务的论文。