In this paper, we introduce EHR-SeqSQL, a novel sequential text-to-SQL
dataset for Electronic Health Record (EHR) databases. EHR-SeqSQL is designed to
address critical yet underexplored aspects in text-to-SQL parsing:
interactivity, compositionality, and efficiency. To the best of our knowledge,
EHR-SeqSQL is not only the largest but also the first medical text-to-SQL
dataset benchmark to include sequential and contextual questions. We provide a
data split and the new test set designed to assess compositional generalization
ability. Our experiments demonstrate the superiority of a multi-turn approach
over a single-turn approach in learning compositionality. Additionally, our
dataset integrates specially crafted tokens into SQL queries to improve
execution efficiency. With EHR-SeqSQL, we aim to bridge the gap between
practical needs and academic research in the text-to-SQL domain.

本研究介绍了 EHR-SeqSQL，这是一个面向电子健康记录（EHR）数据库的新颖的顺序文本到 SQL 的数据集。EHR-SeqSQL 旨在解决文本到 SQL 解析中关键且尚未充分探索的方面：互动性、组合性和效率。通过我们的实验证明了多轮方法在学习组合性方面优于单轮方法。此外，我们的数据集将特别设计的记号整合进 SQL 查询以提高执行效率。通过 EHR-SeqSQL，我们旨在弥合文本到 SQL 领域的实际需求和学术研究之间的差距。