  While sophisticated neural-based techniques have been developed in reading
comprehension, most approaches model the answer in an independent manner,
ignoring its relations with other answer candidates. This problem can be even
worse in open-domain scenarios, where candidates from multiple passages should
be combined to answer a single question. In this paper, we formulate reading
comprehension as an extract-then-select two-stage procedure. We first extract
answer candidates from passages, then select the final answer by combining
information from all the candidates. Furthermore, we regard candidate
extraction as a latent variable and train the two-stage process jointly with
reinforcement learning. As a result, our approach has improved the
state-of-the-art performance significantly on two challenging open-domain
reading comprehension datasets. Further analysis demonstrates the effectiveness
of our model components, especially the information fusion of all the
candidates and the joint training of the extract-then-select procedure.
