  Recent work on unsupervised question answering has shown that models can be
trained with procedurally generated question-answer pairs and can achieve
performance competitive with supervised methods. In this work, we consider the
task of unsupervised reading comprehension and present a method that performs
"test-time learning" (TTL) on a given context (text passage), without requiring
training on large-scale human-authored datasets containing
\textit{context-question-answer} triplets. This method operates directly on a
single test context, uses self-supervision to train models on synthetically
generated question-answer pairs, and then infers answers to unseen
human-authored questions for this context. Our method achieves accuracies
competitive with fully supervised methods and significantly outperforms current
unsupervised methods. TTL methods with a smaller model are also competitive
with the current state-of-the-art in unsupervised reading comprehension.
