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 twostage 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-ofthe-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 . Teaching machines to read and comprehend human languages is a long-standing objective in natural language processing . In order to evaluate this ability , reading comprehension ( RC ) is designed to answer questions through reading relevant passages . In recent years , RC has attracted intense interest . Various advanced neural models have been proposed along with newly released datasets ( Hermann et al . , 2015 ; Rajpurkar et al . , 2016 ; Dunn et al . , 2017 ; Dhingra et al . , 2017b ; He et al . , 2017 ) . In this paper , we formulate the problem of RC as a two-stage process , which first generates candidates with an extraction model , then selects the final answer by combining the information from all the candidates . Furthermore , we treat candidate extraction as a latent variable and jointly train these two stages with RL . Experiments on public open-domain RC datasets Quasar-T and SearchQA show the necessity of introducing the selection model and the effectiveness of fusing candidates information when modeling . Moreover , our joint training strategy leads to significant improvements in performance .

Challenge: Existing models for reading comprehension do not consider multiple answer candidates which can be problematic when they need to fuse information from multiple passages.
Approach: They propose to approach reading comprehension with an extract-then-select procedure, where a model learns two tasks jointly using latent variables and reinforcement learning.
Outcome: The proposed model can fuse answer candidates from multiple candidates and significantly outperform existing models on two open-domain reading comprehension tasks.