Relational recurrent neural networks
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 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.
This paper  proposes relational recurrent neural networks to incorporate relational reasoning into memory-based neural networks. More specifically, the authors design a new memory module, called relational memory core (RMC), to make memories to interact. Multi-head dot product attention is used for this purpose.  Experiments are constructed on a few supervised learning and reinforcement learning tasks, which may profit from more capable relational reasoning across sequential information, to show the effectiveness of the proposed method.  The main strengths of this paper are as follows: (1) It is a novel and well motivated idea to design a new deep learning architecture which can perform relational reasoning. (2) The proposed network structure, RMC, is a reasonable memory module to allow the memories to interact.   (3) The experiments are extensive. The results show explicit performance improvements.  The main weaknesses of this paper are as follows: (1) Some claimed points are not very understandable to me. First, it is unclear how the intuition (that memory architectures involving relational reasoning benefit some learning tasks) can be confirmed. Second, how can you claim that the proposed method has the capacity of relation reasoning from the evidence of "improved function". These points are the footstones of this paper, which should be clearly analyzed, instead of simple explanation.  (2) The analysis of (hyper)parameters should be analyzed in the experiments. It will be nice to show the influence of of the attention model with different settings. (3) The presentation can be improved. For example, Figure 3 are not clear enough for understanding. (4) Another paper titled "Relation Deep Reinforcement Learning" is closely related to this one, thus should be cited and discussed.