  Detecting emotion from dialogue is a challenge that has not yet been
extensively surveyed. One could consider the emotion of each dialogue turn to
be independent, but in this paper, we introduce a hierarchical approach to
classify emotion, hypothesizing that the current emotional state depends on
previous latent emotions. We benchmark several feature-based classifiers using
pre-trained word and emotion embeddings, state-of-the-art end-to-end neural
network models, and Gaussian processes for automatic hyper-parameter search. In
our experiments, hierarchical architectures consistently give significant
improvements, and our best model achieves a 76.77% F1-score on the test set.
