Single-table text-to-SQL aims to transform a natural language question into a
SQL query according to one single table. Recent work has made promising
progress on this task by pre-trained language models and a multi-submodule
framework. However, zero-shot table, that is, the invisible table in the
training set, is currently the most critical bottleneck restricting the
application of existing approaches to real-world scenarios. Although some work
has utilized auxiliary tasks to help handle zero-shot tables, expensive extra
manual annotation limits their practicality. In this paper, we propose a new
approach for the zero-shot text-to-SQL task which does not rely on any
additional manual annotations. Our approach consists of two parts. First, we
propose a new model that leverages the abundant information of table content to
help establish the mapping between questions and zero-shot tables. Further, we
propose a simple but efficient meta-learning strategy to train our model. The
strategy utilizes the two-step gradient update to force the model to learn a
generalization ability towards zero-shot tables. We conduct extensive
experiments on a public open-domain text-to-SQL dataset WikiSQL and a
domain-specific dataset ESQL. Compared to existing approaches using the same
pre-trained model, our approach achieves significant improvements on both
datasets. Compared to the larger pre-trained model and the tabular-specific
pre-trained model, our approach is still competitive. More importantly, on the
zero-shot subsets of both the datasets, our approach further increases the
improvements.

本研究提出了一种新的零样本文本到 SQL 任务中的方法，该方法不依赖于任何额外的手动注释，并在 WikiSQL 数据集和 ESQL 数据集上展开了广泛实验，相较于使用相同预训练模型的现有方法，我们的方法在两个数据集上都取得了显著的改进，特别是在零样本子集上，我们的方法进一步提高了改进。

利用表格内容进行元学习的零样本文本到 SQL

Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning

We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to
achieve human performance in WikiSQL dataset. We revisit and discuss diverse
popular methods in NL2SQL literature, take a full advantage of BERT {Devlin et
al., 2018) through an effective table contextualization method, and coherently
combine them, outperforming the previous state of the art by 8.2% and 2.5% in
logical form and execution accuracy, respectively. We particularly note that
BERT with a seq2seq decoder leads to a poor performance in the task, indicating
the importance of a careful design when using such large pretrained models. We
also provide a comprehensive analysis on the dataset and our model, which can
be helpful for designing future NL2SQL datsets and models. We especially show
that our model's performance is near the upper bound in WikiSQL, where we
observe that a large portion of the evaluation errors are due to wrong
annotations, and our model is already exceeding human performance by 1.3% in
execution accuracy.

SQLova 是第一个在 WikiSQL 数据集中实现人类性能的自然语言到 SQL 模型，利用了 BERT 通过有效的表格语境方法，结合了多种流行的 NL2SQL 方法，在逻辑形式和执行准确度方面分别优于先前的最新技术水平 8.2％和 2.5％。我们特别注意到，在使用这样的大型预训练模型时，BERT 与序列到序列解码器会导致性能不佳，这表明设计的重要性。我们还对数据集和我们的模型进行了全面的分析，这可能有助于设计未来的 NL2SQL 数据集和模型。我们特别展示了我们模型的表现已经接近 WikiSQL 的上限，在其中我们观察到大部分评估错误是由于错误的注释，而我们的模型在执行准确度方面已经超过人类表现 1.3％。

具备表格感知的单词语境综合 WikiSQL 探索

A Comprehensive Exploration on WikiSQL with Table-Aware Word  Contextualization

Interacting with relational databases through natural language helps users of
any background easily query and analyze a vast amount of data. This requires a
system that understands users' questions and converts them to SQL queries
automatically. In this paper we present a novel approach, TypeSQL, which views
this problem as a slot filling task. Additionally, TypeSQL utilizes type
information to better understand rare entities and numbers in natural language
questions. We test this idea on the WikiSQL dataset and outperform the prior
state-of-the-art by 5.5% in much less time. We also show that accessing the
content of databases can significantly improve the performance when users'
queries are not well-formed. TypeSQL gets 82.6% accuracy, a 17.5% absolute
improvement compared to the previous content-sensitive model.

本文提出了一种新方法 TypeSQL，通过将问题转换为插槽填充任务，并利用类型信息来更好地理解自然语言问题中的稀有实体和数字，从而实现了通过自然语言与关系型数据库进行交互。在 WikiSQL 数据集上测试该方法，比现有技术提高了 5.5％的性能。同时，利用数据库内容进行访问可以显著提高用户查询的性能，TypeSQL 的准确度为 82.6％，相对于之前的内容敏感模型提高了 17.5％。