  The aim of this work is to create a framework for synthetically generating
question/query pairs with as little human input as possible. These datasets can
be used to train machine translation systems to convert natural language
questions into queries, a useful tool that could allow for more natural access
to database information. Existing methods of dataset generation require human
input that scales linearly with the size of the dataset, resulting in small
datasets. Aside from a short initial configuration task, no human input is
required during the query generation process of our system. We leverage
WikiData, a knowledge base of RDF triples, as a source for generating the main
content of questions and queries. Using multiple layers of question templating
we are able to sidestep some of the most challenging parts of query generation
that have been handled by humans in previous methods; humans never have to
modify, aggregate, inspect, annotate, or generate any questions or queries at
any step in the process. Our system is easily configurable to multiple domains
and can be modified to generate queries in natural languages other than
English. We also present an example dataset of 110,000 question/query pairs
across four WikiData domains. We then present a baseline model that we train
using the dataset which shows promise in a commercial QA setting.
