  During the last years, there has been a lot of interest in achieving some
kind of complex reasoning using deep neural networks. To do that, models like
Memory Networks (MemNNs) have combined external memory storages and attention
mechanisms. These architectures, however, lack of more complex reasoning
mechanisms that could allow, for instance, relational reasoning. Relation
Networks (RNs), on the other hand, have shown outstanding results in relational
reasoning tasks. Unfortunately, their computational cost grows quadratically
with the number of memories, something prohibitive for larger problems. To
solve these issues, we introduce the Working Memory Network, a MemNN
architecture with a novel working memory storage and reasoning module. Our
model retains the relational reasoning abilities of the RN while reducing its
computational complexity from quadratic to linear. We tested our model on the
text QA dataset bAbI and the visual QA dataset NLVR. In the jointly trained
bAbI-10k, we set a new state-of-the-art, achieving a mean error of less than
0.5%. Moreover, a simple ensemble of two of our models solves all 20 tasks in
the joint version of the benchmark.
