  Deep neural networks are a family of computational models that are naturally
suited to the analysis of hierarchical data such as, for instance, sequential
data with the use of recurrent neural networks. In the other hand, ordinal
regression is a well-known predictive modelling problem used in fields as
diverse as psychometry to deep neural network based voice modelling. Their
specificity lies in the properties of their outcome variable, typically
considered as a categorical variable with natural ordering properties,
typically allowing comparisons between different states ("a little" is less
than "somewhat" which is itself less than "a lot", with transitivity allowed).
This article investigates the application of sequence-to-sequence learning
methods provided by the deep learning framework in ordinal regression, by
formulating the ordinal regression problem as a sequential binary search. A
method for visualizing the model's explanatory variables according to the
ordinal target variable is proposed, that bears some similarities to linear
discriminant analysis. The method is compared to traditional ordinal regression
methods on a number of benchmark dataset, and is shown to have comparable or
significantly better predictive power.
