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 .
Existing models for reading comprehension do not consider multiple answer candidates which can be problematic when they need to fuse information from multiple passages.
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.
The proposed model can fuse answer candidates from multiple candidates and significantly outperform existing models on two open-domain reading comprehension tasks.