  In this paper, we introduce Query-based Attention CNN(QACNN) for Text
Similarity Map, an end-to-end neural network for question answering. This
network is composed of compare mechanism, two-staged CNN architecture with
attention mechanism, and a prediction layer. First, the compare mechanism
compares between the given passage, query, and multiple answer choices to build
similarity maps. Then, the two-staged CNN architecture extracts features
through word-level and sentence-level. At the same time, attention mechanism
helps CNN focus more on the important part of the passage based on the query
information. Finally, the prediction layer find out the most possible answer
choice. We conduct this model on the MovieQA dataset using Plot Synopses only,
and achieve 79.99% accuracy which is the state of the art on the dataset.
