  To solve the text-based question and answering task that requires relational
reasoning, it is necessary to memorize a large amount of information and find
out the question relevant information from the memory. Most approaches were
based on external memory and four components proposed by Memory Network. The
distinctive component among them was the way of finding the necessary
information and it contributes to the performance. Recently, a simple but
powerful neural network module for reasoning called Relation Network (RN) has
been introduced. We analyzed RN from the view of Memory Network, and realized
that its MLP component is able to reveal the complicate relation between
question and object pair. Motivated from it, we introduce which uses MLP to
find out relevant information on Memory Network architecture. It shows new
state-of-the-art results in jointly trained bAbI-10k story-based question
answering tasks and bAbI dialog-based question answering tasks.
