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We propose a contextual emotion classifier based on a transferable languagemodel and dynamic max pooling, which predicts the emotion of each utterance ina dialogue. A representative emotion analysis task, EmotionX, requires toconsider contextual information from colloquial dialogues and to deal with aclass imbalance problem. To alleviate these problems, our model leverages theself-attention based transferable language model and the weighted cross entropyloss. Furthermore, we apply post-training and fine-tuning mechanisms to enhancethe domain adaptability of our model and utilize several machine learningtechniques to improve its performance. We conduct experiments on twoemotion-labeled datasets named Friends and EmotionPush. As a result, our modeloutperforms the previous state-of-the-art model and also shows competitiveperformance in the EmotionX 2019 challenge. The code will be available in theGithub page.

EmotionX-KU: BERT-Max based Contextual Emotion Classifier