Several large-scale datasets (e.g., WikiSQL, Spider) for developing natural
language interfaces to databases have recently been proposed. These datasets
cover a wide breadth of domains but fall short on some essential domains, such
as finance and accounting. Given that accounting databases are used worldwide,
particularly by non-technical people, there is an imminent need to develop
models that could help extract information from accounting databases via
natural language queries. In this resource paper, we aim to fill this gap by
proposing a new large-scale Text-to-SQL dataset for the accounting and
financial domain: BookSQL. The dataset consists of 100k natural language
queries-SQL pairs, and accounting databases of 1 million records. We experiment
with and analyze existing state-of-the-art models (including GPT-4) for the
Text-to-SQL task on BookSQL. We find significant performance gaps, thus
pointing towards developing more focused models for this domain.

给定一份新的大规模文本转 SQL 数据集（BookSQL），研究作者通过实验与分析现有的最先进模型，发现在会计和金融领域存在显著的性能差距，进而指出有需要开发更专注于该领域的模型。

BookSQL：面向会计领域的大规模文本到 SQL 数据集

BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain

We present Archer, a challenging bilingual text-to-SQL dataset specific to
complex reasoning, including arithmetic, commonsense and hypothetical
reasoning. It contains 1,042 English questions and 1,042 Chinese questions,
along with 521 unique SQL queries, covering 20 English databases across 20
domains. Notably, this dataset demonstrates a significantly higher level of
complexity compared to existing publicly available datasets. Our evaluation
shows that Archer challenges the capabilities of current state-of-the-art
models, with a high-ranked model on the Spider leaderboard achieving only 6.73%
execution accuracy on Archer test set. Thus, Archer presents a significant
challenge for future research in this field.

Archer 是一个具有挑战性的双语文本转 SQL 数据集，专注于复杂推理，包括算术、常识和假设推理。它包含 1042 个英文问题和 1042 个中文问题，以及 521 个不同的 SQL 查询，涵盖了 20 个英文数据库在 20 个领域中。该数据集相比现有公开数据集具有明显更高的复杂性。通过我们的评估，Archer 挑战了当前最先进模型的能力，目前在 Archer 测试集上排名最高的模型仅达到 6.73％的执行准确率。因此，Archer 对未来在该领域的研究提出了重要挑战。

Archer：一个包含算术、常识和假设推理的人工标注文本到 SQL 的数据集

Archer: A Human-Labeled Text-to-SQL Dataset with Arithmetic, Commonsense  and Hypothetical Reasoning

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。