A new method for Text-to-SQL parsing, Grammar Pre-training (GP), is proposed
to decode deep relations between question and database. Firstly, to better
utilize the information of databases, a random value is added behind a question
word which is recognized as a column, and the new sentence serves as the model
input. Secondly, initialization of vectors for decoder part is optimized, with
reference to the former encoding so that question information can be concerned.
Finally, a new approach called flooding level is adopted to get the non-zero
training loss which can generalize better results. By encoding the sentence
with GRAPPA and RAT-SQL model, we achieve better performance on spider, a
cross-DB Text-to-SQL dataset (72.8 dev, 69.8 test). Experiments show that our
method is easier to converge during training and has excellent robustness.

提出了一种名为语法预训练的 Text-to-SQL 解析新方法，通过增加问题词后的随机值以及优化解码器部分的向量初始化，采用泛化效果更佳的非零训练损失等技术提高了模型性能，成功地应用于了跨数据库的 Text-to-SQL 数据集（spider），在开发集上获得了 72.8% 的准确性，在测试集上获得了 69.8% 的准确性。

文本转 SQL 解析器的无上下文语法预训练

GP: Context-free Grammar Pre-training for Text-to-SQL Parsers

Recent neural text-to-SQL models can effectively translate natural language
questions to corresponding SQL queries on unseen databases. Working mostly on
the Spider dataset, researchers have proposed increasingly sophisticated
solutions to the problem. Contrary to this trend, in this paper we focus on
simplifications. We begin by building DuoRAT, a re-implementation of the
state-of-the-art RAT-SQL model that unlike RAT-SQL is using only relation-aware
or vanilla transformers as the building blocks. We perform several ablation
experiments using DuoRAT as the baseline model. Our experiments confirm the
usefulness of some techniques and point out the redundancy of others, including
structural SQL features and features that link the question with the schema.

本研究旨在建立 DuoRAT 作为对最新的 RAT-SQL 模型的重新实现，验证其在使用关系感知或普通变换器作为构建块的情况下的有效性，并通过消融实验来确认一些技术的有用性以及结构化 SQL 功能和在问题与架构之间建立联系的冗余特征。