The Natural Language to SQL (NL2SQL) technique is used to convert natural
language queries into executable SQL statements. Typically, slot-filling is
employed as a classification method for multi-task cases to achieve this goal.
However, slot-filling can result in inaccurate SQL statement generation due to
negative migration issues arising from different classification tasks. To
overcome this limitation, this study introduces a new approach called
Multi-Layer Expert Generate SQL (MLEG-SQL), which utilizes a dedicated
multi-task hierarchical network. The lower layer of the network extracts
semantic features of natural language statements, while the upper layer builds
a specialized expert system for handling specific classification tasks. This
hierarchical approach mitigates performance degradation resulting from
different task conflicts. The proposed method was evaluated on the WiKSQL
dataset and was found to be effective in generating accurate SQL statements.

本研究提出了一种名为 Multi-Layer Expert Generate SQL (MLEG-SQL) 的新方法，它利用一个专门的多任务分层网络来准确生成 SQL 语句，以解决由于不同分类任务引起的性能下降问题。

基于多层专家网络的改进 NL2SQL

Improved NL2SQL based on Multi-layer Expert Network

Natural language to SQL (NL2SQL) aims to parse a natural language with a
given database into a SQL query, which widely appears in practical Internet
applications. Jointly encode database schema and question utterance is a
difficult but important task in NL2SQL. One solution is to treat the input as a
heterogeneous graph. However, it failed to learn good word representation in
question utterance. Learning better word representation is important for
constructing a well-designed NL2SQL system. To solve the challenging task, we
present a Relation aware Semi-autogressive Semantic Parsing (\MODN) ~framework,
which is more adaptable for NL2SQL. It first learns relation embedding over the
schema entities and question words with predefined schema relations with
ELECTRA and relation aware transformer layer as backbone. Then we decode the
query SQL with a semi-autoregressive parser and predefined SQL syntax. From
empirical results and case study, our model shows its effectiveness in learning
better word representation in NL2SQL.

该论文提出了一种基于关系嵌入的半自回归语义分析 NL2SQL 方法，通过 ELECTRA 和关系感知 transformer 层实现与模式实体及问题单词之间的关系嵌入，再使用半自回归解析器和预定义的 SQL 语法解码查询 SQL，从实验结果和案例研究来看，该方法在 NL2SQL 中具有更好的词汇表现力。

NL2SQL 的关系感知半自回归语义解析

Relation Aware Semi-autoregressive Semantic Parsing for NL2SQL

Parsing natural language to corresponding SQL (NL2SQL) with data driven
approaches like deep neural networks attracts much attention in recent years.
Existing NL2SQL datasets assume that condition values should appear exactly in
natural language questions and the queries are answerable given the table.
However, these assumptions may fail in practical scenarios, because user may
use different expressions for the same content in the table, and query
information outside the table without the full picture of contents in table.
Therefore we present TableQA, a large-scale cross-domain Natural Language to
SQL dataset in Chinese language consisting 64,891 questions and 20,311 unique
SQL queries on over 6,000 tables. Different from exisiting NL2SQL datasets,
TableQA requires to generalize well not only to SQL skeletons of different
questions and table schemas, but also to the various expressions for condition
values. Experiment results show that the state-of-the-art model with 95.1%
condition value accuracy on WikiSQL only gets 46.8% condition value accuracy
and 43.0% logic form accuracy on TableQA, indicating the proposed dataset is
challenging and necessary to handle. Two table-aware approaches are proposed to
alleviate the problem, the end-to-end approaches obtains 51.3% and 47.4%
accuracy on the condition value and logic form tasks, with improvement of 4.7%
and 3.4% respectively.

本文介绍了 TableQA dataset 及其挑战性和必要性，探讨了解决不同条件值表述和表格外查询的问题的两种基于表格知识的方法，并给出实验结果。

TableQA：一个用于表感知 SQL 生成的大规模中文文本到 SQL 数据集

TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL  Generation

We present a sequence-to-action parsing approach for the natural language to
SQL task that incrementally fills the slots of a SQL query with feasible
actions from a pre-defined inventory. To account for the fact that typically
there are multiple correct SQL queries with the same or very similar semantics,
we draw inspiration from syntactic parsing techniques and propose to train our
sequence-to-action models with non-deterministic oracles. We evaluate our
models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the
test set, a 2.1% absolute improvement over the models trained with traditional
static oracles assuming a single correct target SQL query. When further
combined with the execution-guided decoding strategy, our model sets a new
state-of-the-art performance at an execution accuracy of 87.1%.

该研究介绍了一种面向自然语言到 SQL 任务的序列到操作分析方法，使用从预定义库存中得到的操作填充 SQL 查询的槽。该方法考虑到通常有多个具有相同或非常相似语义的正确 SQL 查询，从句法分析技术中汲取灵感，并提出用非确定性的神谕来训练序列到操作模型，该模型可处理上述不确定性，取得了更好的执行精度。与执行引导的解码策略相结合的模型，在执行精度方面达到了 87.1% 的新的最高性能。评估结果来自 WikiSQL 数据集。