  We present a novel deep learning architecture to address the cloze-style
question answering task. Existing approaches employ reading mechanisms that do
not fully exploit the interdependency between the document and the query. In
this paper, we propose a novel \emph{dependent gated reading} bidirectional GRU
network (DGR) to efficiently model the relationship between the document and
the query during encoding and decision making. Our evaluation shows that DGR
obtains highly competitive performance on well-known machine comprehension
benchmarks such as the Children's Book Test (CBT-NE and CBT-CN) and Who DiD
What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate
our model by ablation and attention studies.
