Text-to-SQL is a technology that converts natural language queries into the
structured query language SQL. A novel research approach that has recently
gained attention focuses on methods based on the complexity of SQL queries,
achieving notable performance improvements. However, existing methods entail
significant storage and training costs, which hampers their practical
application. To address this issue, this paper introduces a method for
Text-to-SQL based on Refined Schema and Hardness Prompt. By filtering out
low-relevance schema information with a refined schema and identifying query
hardness through a Language Model (LM) to form prompts, this method reduces
storage and training costs while maintaining performance. It's worth mentioning
that this method is applicable to any sequence-to-sequence (seq2seq) LM. Our
experiments on the Spider dataset, specifically with large-scale LMs, achieved
an exceptional Execution accuracy (EX) of 82.6%, demonstrating the
effectiveness and greater suitability of our method for real-world
applications.

该研究论文介绍了一种基于精炼模式和难度提示的文本到 SQL 的方法，通过过滤低相关性的模式信息和使用语言模型通过难度提示来减少存储和训练成本，同时保持性能，实现了在大规模语言模型上 82.6% 的出色执行准确度，证明了我们方法在实际应用中的有效性和更大的适用性。

RH-SQL：精确模式和难度提示的文本转 SQL

RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL

Translating natural language queries (NLQ) into structured query language
(SQL) in interfaces to relational databases is a challenging task that has been
widely studied by researchers from both the database and natural language
processing communities. Numerous works have been proposed to attack the natural
language interfaces to databases (NLIDB) problem either as a conventional
pipeline-based or an end-to-end deep-learning-based solution. Nevertheless,
regardless of the approach preferred, such solutions exhibit black-box nature,
which makes it difficult for potential users targeted by these systems to
comprehend the decisions made to produce the translated SQL. To this end, we
propose xDBTagger, an explainable hybrid translation pipeline that explains the
decisions made along the way to the user both textually and visually. We also
evaluate xDBTagger quantitatively in three real-world relational databases. The
evaluation results indicate that in addition to being fully interpretable,
xDBTagger is effective in terms of accuracy and translates the queries more
efficiently compared to other state-of-the-art pipeline-based systems up to
10000 times.

xdbtagger 是一种可解释的混合翻译管道，适用于自然语言查询翻译为结构化查询语言的问题，通过文本和视觉方式解释决策，有效地提高了翻译的准确性和效率，适用于多个实际关系数据库和数据集。

xDBTagger: 使用关键词映射和模式图解释数据库的自然语言接口

xDBTagger: Explainable Natural Language Interface to Databases Using Keyword Mappings and Schema Graph

Text-to-SQL parsing is an essential and challenging task. The goal of
text-to-SQL parsing is to convert a natural language (NL) question to its
corresponding structured query language (SQL) based on the evidences provided
by relational databases. Early text-to-SQL parsing systems from the database
community achieved a noticeable progress with the cost of heavy human
engineering and user interactions with the systems. In recent years, deep
neural networks have significantly advanced this task by neural generation
models, which automatically learn a mapping function from an input NL question
to an output SQL query. Subsequently, the large pre-trained language models
have taken the state-of-the-art of the text-to-SQL parsing task to a new level.
In this survey, we present a comprehensive review on deep learning approaches
for text-to-SQL parsing. First, we introduce the text-to-SQL parsing corpora
which can be categorized as single-turn and multi-turn. Second, we provide a
systematical overview of pre-trained language models and existing methods for
text-to-SQL parsing. Third, we present readers with the challenges faced by
text-to-SQL parsing and explore some potential future directions in this field.

该研究综述了文本到结构化查询语言解析的深度学习方法，介绍了单轮和多轮对话的文本到 SQL 解析语料库，阐明了预训练语言模型和现有方法，探讨了面临的挑战和未来发展方向。