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Detecting emotion from dialogue is a challenge that has not yet beenextensively surveyed. One could consider the emotion of each dialogue turn tobe independent, but in this paper, we introduce a hierarchical approach toclassify emotion, hypothesizing that the current emotional state depends onprevious latent emotions. We benchmark several feature-based classifiers usingpre-trained word and emotion embeddings, state-of-the-art end-to-end neuralnetwork models, and Gaussian processes for automatic hyper-parameter search. Inour experiments, hierarchical architectures consistently give significantimprovements, and our best model achieves a 76.77% F1-score on the test set.

CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification