Abstract: Machine reading comprehension is a key part
of natural language understanding. Due to
wide range of applications, machine reading
comprehension has attracted considerable in-
terest from both commercial and academic en-
tities. Recently, deep neural network based
models have been able to achieve near human
performance on certain types of reading com-
prehension datasets (Rajpurkar et al., 2016;
Devlin et al., 2018; Hermann et al., 2015; Seo
et al., 2016). Newer and much harder datasets
have been proposed that require more sophisti-
cated reasoning capabilities. One such dataset
was proposed by Khashabi et al.(2018) called
MultiRC. In this work, we propose a multi-
stage approach which significantly improves
the previous state of the art results on MultiRC
(Khashabi et al., 2018).
Keywords: question answering
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