  The rate at which medical questions are asked online far exceeds the capacity
of qualified people to answer them, and many of these questions are not unique.
Identifying same-question pairs could enable questions to be answered more
effectively. While many research efforts have focused on the problem of general
question similarity for non-medical applications, these approaches do not
generalize well to the medical domain, where medical expertise is often
required to determine semantic similarity. In this paper, we show how a
semi-supervised approach of pre-training a neural network on medical
question-answer pairs is a particularly useful intermediate task for the
ultimate goal of determining medical question similarity. While other
pre-training tasks yield an accuracy below 78.7% on this task, our model
achieves an accuracy of 82.6% with the same number of training examples, and an
accuracy of 80.0% with a much smaller training set.
