Exploring the generalization of a text-to-SQL parser is essential for a
system to automatically adapt the real-world databases. Previous works provided
investigations focusing on lexical diversity, including the influence of the
synonym and perturbations in both natural language questions and databases.
However, research on the structure variety of database schema~(DS) is
deficient. Specifically, confronted with the same input question, the target
SQL is probably represented in different ways when the DS comes to a different
structure. In this work, we provide in-deep discussions about the structural
generalization of text-to-SQL tasks. We observe that current datasets are too
templated to study structural generalization. To collect eligible test data, we
propose a framework to generate novel text-to-SQL data via automatic and
synchronous (DS, SQL) pair altering. In the experiments, significant
performance reduction when evaluating well-trained text-to-SQL models on the
synthetic samples demonstrates the limitation of current research regarding
structural generalization. According to comprehensive analysis, we suggest the
practical reason is the overfitting of (NL, SQL) patterns.

本文讨论了文本到 SQL 任务的结构概括问题。通过研究现有数据集和实验结果，我们观察到了（自然语言，SQL）模式过分拟合的实践限制。

文本转 SQL 中结构概括的研究

On the Structural Generalization in Text-to-SQL

When translating natural language questions into SQL queries to answer
questions from a database, contemporary semantic parsing models struggle to
generalize to unseen database schemas. The generalization challenge lies in (a)
encoding the database relations in an accessible way for the semantic parser,
and (b) modeling alignment between database columns and their mentions in a
given query. We present a unified framework, based on the relation-aware
self-attention mechanism, to address schema encoding, schema linking, and
feature representation within a text-to-SQL encoder. On the challenging Spider
dataset this framework boosts the exact match accuracy to 57.2%, surpassing its
best counterparts by 8.7% absolute improvement. Further augmented with BERT, it
achieves the new state-of-the-art performance of 65.6% on the Spider
leaderboard. In addition, we observe qualitative improvements in the model's
understanding of schema linking and alignment. Our implementation will be
open-sourced at this https URL

该研究使用基于关系感知自注意机制的统一框架，解决将自然语言问题转换为 SQL 查询中出现的通用性问题，包括编码数据库关系，建立数据库列和查询的链接，以及特征表示。在 Spider 数据集上，该框架将的精确匹配准确度提高到 57.2％，优于其最好的对手 8.7％的绝对改进，并在 BERT 的支持下，实现了 65.6％的性能，成为了新的最佳表现，同时也在模型对数据库链接和对齐的理解方面得到了定性改善。

RAT-SQL：基于关系感知的文本 - SQL 解析器的模式编码和链接

RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL  Parsers

Research on parsing language to SQL has largely ignored the structure of the
database (DB) schema, either because the DB was very simple, or because it was
observed at both training and test time. In Spider, a recently-released
text-to-SQL dataset, new and complex DBs are given at test time, and so the
structure of the DB schema can inform the predicted SQL query. In this paper,
we present an encoder-decoder semantic parser, where the structure of the DB
schema is encoded with a graph neural network, and this representation is later
used at both encoding and decoding time. Evaluation shows that encoding the
schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically
above the current state of the art, which is at 19.7%.

该研究关注语言解析到 SQL 的过程，并提出了一种使用图神经网络对数据库模式进行编码的方法，在 Spider 数据集上得到了较高的解析准确率。