  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.
