DISTRIBUTED ASSOCIATIVE MEMORY NETWORK WITH ASSOCIATION REINFORCING LOSS

Abstract

Despite recent progress in memory augmented neural network research, associative memory networks with a single external memory still show limited performance on complex relational reasoning tasks. The main reason for this problem comes from the lossy representation of relational information stored in a contentbased addressing memory and its insufficient associating performance for long temporal sequence data. To address these problems, here we introduce a novel Distributed Associative Memory architecture (DAM) with Association Reinforcing Loss (ARL) function which enhances the relation reasoning performance of memory augmented neural network. In this framework, instead of relying on a single large external memory, we form a set of multiple smaller associative memory blocks and update these sub-memory blocks simultaneously and independently with the content-based addressing mechanism. Based on DAM architecture, we can effectively retrieve complex relational information by integrating diverse representations distributed across multiple sub-memory blocks with an attention mechanism. Moreover, to further enhance the relation modeling performance of memory network, we propose ARL which assists a task's target objective while learning relational information exist in data. ARL enables the memory augmented neural network to reinforce an association between input data and task objective by reproducing stochastically sampled input data from stored memory contents. With this content reproducing task, it enriches the representations with relational information. In experiments, we apply our two main approaches to Differential Neural Computer (DNC), which is one of the representative contentbased addressing memory model and achieves state-of-the-art performance on both memorization and relational reasoning tasks.

1. INTRODUCTION

The essential part of human intelligence for understanding the story and predicting unobserved facts largely depends on the ability of memorizing the past and reasoning for relational information based on the pieces of memory. In this context, research on artificial intelligence has focused on designing human like associative memory network which can easily store and recall both events and relational information from a part of information. In neural network research, many approaches generally model sequential data with memory systems, such as Long Short Term Memory (LSTM) (Hochreiter & Schmidhuber, 1997) and memoryaugmented neural networks (MANN). Especially, recent approach in MANN constructs an associative memory with a content-based addressing mechanism and stores both input data and its relational information to a single external memory. MANN has already proven to be an essential component on many tasks which need long term context understanding (Weston et al., 2014; Sukhbaatar et al., 2015; Graves et al., 2014; 2016; Gulcehre et al., 2018) . Also, compared to recurrent neural networks, it can store more information from sequential input data and correctly recall desired information from memory with a given cue. However, even with its promising performance on a wide range of tasks, MANN still has difficulties in solving complex relational reasoning problems (Weston et al., 2015) . Since content-based addressing model implicitly encodes data item and its relational information into one vector representation, they often result in a lossy representation of relational information which is not rich enough for solving relational reasoning tasks. To address such weakness, some researches find relational information by leveraging interaction between memory entities 1

