Relational recurrent neural networks

TL;DR

This paper introduces the Relational Memory Core, a new memory module for neural networks that enhances relational reasoning capabilities, leading to significant improvements in various sequential tasks and language modeling benchmarks.

Contribution

The paper proposes the Relational Memory Core, a novel memory module using multi-head attention to improve relational reasoning in neural networks.

Findings

Large gains in reinforcement learning domains like Mini PacMan

State-of-the-art results on WikiText-103, Project Gutenberg, and GigaWord datasets

Improved relational reasoning in sequential tasks

Abstract

Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to perform complex relational reasoning with the information they remember. Here, we first confirm our intuitions that standard memory architectures may struggle at tasks that heavily involve an understanding of the ways in which entities are connected -- i.e., tasks involving relational reasoning. We then improve upon these deficits by using a new memory module -- a \textit{Relational Memory Core} (RMC) -- which employs multi-head dot product attention to allow memories to interact. Finally, we test the RMC on a suite of tasks that may profit from more capable relational reasoning across sequential information, and show large gains in RL domains (e.g. Mini PacMan), program evaluation, and language modeling, achieving state-of-the-art results on the WikiText-103, Project Gutenberg, and GigaWord datasets.