Applying Ordinal logit model to Self-Attentive-LSTM

Abstract

In this research, we propose a method incorporating structure of ordered logit model into output layer of Self-Attentive-LSTM. This helps to predict ordinal variables. Model with using this method learns relationships among classes since the order of classes are assumed in advance. We show this model is useful when the objective variable is ordinal by applying to "The economic watcher", data surveyed by the Japanese Cabinet Office. When predicting the Economic assessment index, this proposed method improved F-measures of both sides of the ordinal indices compared to Self-Attentive-LSTM.