  We present several techniques to tackle the mismatch in class distributions
between training and test data in the Contextual Emotion Detection task of
SemEval 2019, by extending the existing methods for class imbalance problem.
Reducing the distance between the distribution of prediction and ground truth,
they consistently show positive effects on the performance. Also we propose a
novel neural architecture which utilizes representation of overall context as
well as of each utterance. The combination of the methods and the models
achieved micro F1 score of about 0.766 on the final evaluation.
