  Question Answering (QA) systems require a large amount of annotated data
which is costly and time-consuming to gather. Converting datasets of existing
QA benchmarks are challenging due to different formats and complexities. To
address these issues, we propose an algorithm to automatically generate shorter
questions resembling day-to-day human communication in the Natural Questions
(NQ) dataset from longer trivia questions in Quizbowl (QB) dataset by
leveraging conversion in style among the datasets. This provides an automated
way to generate more data for our QA systems. To ensure quality as well as
quantity of data, we detect and remove ill-formed questions using a neural
classifier. We demonstrate that in a low resource setting, using the generated
data improves the QA performance over the baseline system on both NQ and QB
data. Our algorithm improves the scalability of training data while maintaining
quality of data for QA systems.
