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To solve the text-based question and answering task that requires relationalreasoning, it is necessary to memorize a large amount of information and findout the question relevant information from the memory. Most approaches werebased on external memory and four components proposed by Memory Network. Thedistinctive component among them was the way of finding the necessaryinformation and it contributes to the performance. Recently, a simple butpowerful neural network module for reasoning called Relation Network (RN) hasbeen introduced. We analyzed RN from the view of Memory Network, and realizedthat its MLP component is able to reveal the complicate relation betweenquestion and object pair. Motivated from it, we introduce which uses MLP tofind out relevant information on Memory Network architecture. It shows newstate-of-the-art results in jointly trained bAbI-10k story-based questionanswering tasks and bAbI dialog-based question answering tasks.

Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning