  Linguistic features have shown promising applications for detecting various
cognitive impairments. To improve detection accuracies, increasing the amount
of data or the number of linguistic features have been two applicable
approaches. However, acquiring additional clinical data can be expensive, and
hand-crafting features is burdensome. In this paper, we take a third approach,
proposing Consensus Networks (CNs), a framework to classify after reaching
agreements between modalities. We divide linguistic features into
non-overlapping subsets according to their modalities, and let neural networks
learn low-dimensional representations that agree with each other. These
representations are passed into a classifier network. All neural networks are
optimized iteratively.
  In this paper, we also present two methods that improve the performance of
CNs. We then present ablation studies to illustrate the effectiveness of
modality division. To understand further what happens in CNs, we visualize the
representations during training. Overall, using all of the 413 linguistic
features, our models significantly outperform traditional classifiers, which
are used by the state-of-the-art papers.
