  Reading comprehension (RC) is a challenging task that requires synthesis of
information across sentences and multiple turns of reasoning. Using a
state-of-the-art RC model, we empirically investigate the performance of
single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The
RC model is an end-to-end neural network with iterative attention, and uses
reinforcement learning to dynamically control the number of turns. We find that
multiple-turn reasoning outperforms single-turn reasoning for all question and
answer types; further, we observe that enabling a flexible number of turns
generally improves upon a fixed multiple-turn strategy. %across all question
types, and is particularly beneficial to questions with lengthy, descriptive
answers. We achieve results competitive to the state-of-the-art on these two
datasets.
