  Question Answering (QA) is in increasing demand as the amount of information
available online and the desire for quick access to this content grows. A
common approach to QA has been to fine-tune a pretrained language model on a
task-specific labeled dataset. This paradigm, however, relies on scarce, and
costly to obtain, large-scale human-labeled data. We propose an unsupervised
approach to training QA models with generated pseudo-training data. We show
that generating questions for QA training by applying a simple template on a
related, retrieved sentence rather than the original context sentence improves
downstream QA performance by allowing the model to learn more complex
context-question relationships. Training a QA model on this data gives a
relative improvement over a previous unsupervised model in F1 score on the
SQuAD dataset by about 14%, and 20% when the answer is a named entity,
achieving state-of-the-art performance on SQuAD for unsupervised QA.
