  Cloze-style queries are representative problems in reading comprehension.
Over the past few months, we have seen much progress that utilizing neural
network approach to solve Cloze-style questions. In this paper, we present a
novel model called attention-over-attention reader for the Cloze-style reading
comprehension task. Our model aims to place another attention mechanism over
the document-level attention, and induces "attended attention" for final
predictions. Unlike the previous works, our neural network model requires less
pre-defined hyper-parameters and uses an elegant architecture for modeling.
Experimental results show that the proposed attention-over-attention model
significantly outperforms various state-of-the-art systems by a large margin in
public datasets, such as CNN and Children's Book Test datasets.
