  We propose a simple yet robust stochastic answer network (SAN) that simulates
multi-step reasoning in machine reading comprehension. Compared to previous
work such as ReasoNet which used reinforcement learning to determine the number
of steps, the unique feature is the use of a kind of stochastic prediction
dropout on the answer module (final layer) of the neural network during the
training. We show that this simple trick improves robustness and achieves
results competitive to the state-of-the-art on the Stanford Question Answering
Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading
COmprehension Dataset (MS MARCO).
