Memory Network based models have shown a remarkable progress on the task of relational reasoning.
 Recently, a simpler yet powerful neural network module called Relation Network (RN) has been introduced. 
 Despite its architectural simplicity, the time complexity of relation network grows quadratically with data, hence limiting its application to tasks with a large-scaled memory.
 We introduce Related Memory Network, an end-to-end neural network architecture exploiting both memory network and relation network structures. 
 We follow memory network's four components while each component operates similar to the relation network without taking a pair of objects. 
 As a result, our model is as simple as RN but the computational complexity is reduced to linear time.
 It achieves the state-of-the-art results in jointly trained bAbI-10k story-based question answering and  bAbI dialog dataset.
A simple reasoning architecture based on the memory network (MemNN) and relation network (RN), reducing the time complexity compared to the RN and achieving state-of-the-are result on bAbI story based QA and bAbI dialog. Introduces Related Memory Network (RMN), an improvement over Relationship Networks (RN).