The number of databases as well as their size and complexity is increasing. This creates a barrier to use especially for non-experts, who have to come to grips with the nature of the data, the way it has been represented in the database, and the specific query languages or user interfaces by which data are accessed. These difficulties worsen in research settings, where it is common to work with many different databases. One approach to improving this situation is to allow users to pose their queries in natural language. In this work we describe a machine learning framework, Polyglotter, that in a general way supports the mapping of natural language searches to database queries. Importantly, it does not require the creation of manually annotated data for training and therefore can be applied easily to multiple domains. The framework is polyglot in the sense that it supports multiple different database engines that are accessed with a variety of query languages, including SQL and Cypher. Furthermore Polyglotter also supports multi-class queries. Our results indicate that our framework performs well on both synthetic and real databases, and may provide opportunities for database maintainers to improve accessibility to their resources.

中文翻译：

---

将合成自然语言转换为数据库查询：多语言深度学习框架

数据库的数量及其规模和复杂性都在增加。这给非专家特别是非专家使用设置了障碍，他们必须掌握数据的性质，数据在数据库中的表示方式以及访问数据的特定查询语言或用户界面。这些困难在研究环境中变得更加严重，因为在这种环境中使用许多不同的数据库是很常见的。改善这种情况的一种方法是允许用户以自然语言提出他们的查询。在本文中，我们描述了一种机器学习框架Polyglotter，该框架以一般方式支持自然语言搜索到数据库查询的映射。重要的是，它不需要创建用于培训的手动注释数据，因此可以轻松地应用于多个域。从某种意义上说，该框架是多语言的，它支持使用多种查询语言（包括SQL和Cypher）访问的多个不同的数据库引擎。此外，Polyglotter还支持多类查询。我们的结果表明，我们的框架在综合数据库和真实数据库上均表现良好，并且可能为数据库维护人员提供机会以改善对其资源的可访问性。